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These books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
\\n\\nThis collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
\\n\\nTo celebrate the 50 books published, we have gathered them at one location - just one click away, so that you can easily browse the subjects of your interest, download the content directly, share it or read online.
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IntechOpen and Knowledge Unlatched formed a partnership to support researchers working in engineering sciences by enabling an easier approach to publishing Open Access content. Using the Knowledge Unlatched crowdfunding model to raise the publishing costs through libraries around the world, Open Access Publishing Fee (OAPF) was not required from the authors.
\n\nInitially, the partnership supported engineering research, but it soon grew to include physical and life sciences, attracting more researchers to the advantages of Open Access publishing.
\n\n\n\nThese books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
\n\nThis collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
\n\nTo celebrate the 50 books published, we have gathered them at one location - just one click away, so that you can easily browse the subjects of your interest, download the content directly, share it or read online.
\n\n\n\n\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"3398",leadTitle:null,fullTitle:"Current Issues in Sports and Exercise Medicine",title:"Current Issues in Sports and Exercise Medicine",subtitle:null,reviewType:"peer-reviewed",abstract:"This unique resource presents current issues in sports and exercise medicine which outlines new areas of knowledge and provides updates on current knowledge in the broad field of sports and exercise medicine.\n\nWritten by experts in their own sub-disciplines, Current Issues in Sports and Exercise Medicine discusses the physiology behind sports injuries and presents new and exciting approaches to manage such injuries. 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Such uncertain parameters can be product demand and price, raw material supply chain cost, production cost. Disregarding uncertainty could, unfortunately, render the solution of a deterministic optimization problem suboptimal or even infeasible. In the era of big data and deep learning (DL), intelligent use of data and knowledge extraction from them have great benefits for organizations. Besides, in today’s complex world, uncertainty on the lack of enough data has been replaced by too much data, which creates numerous opportunities for academicians and practitioners [4]. A large amount of interactive data is routinely created, collected, and archived in different industries; these data are becoming an important asset in process operation, control, and design. Explosive growth in volume and different sorts of data in organizations has created the need to develop technologies that can intelligently and rapidly analyze large volumes of data [4]. The traditional optimization methods cannot face big data satisfactorily. Nowadays, a wide array of emerging machine learning (ML) techniques can be leveraged to analyze data and extract relevant, accurate, and useful information and knowledge for smart decision-making. More recently, the dramatic progress of ML, especially DL over the past decade, coupled with recent advances in mathematical programming, sparks a flurry of interest in data-driven optimization [5, 6]. The uncertainty model is formulated based on a data-driven optimization paradigm, allowing uncertainty data to speak for themselves in the optimization algorithm. In this way, rich knowledge underlying uncertainty data set can be extracted and harnessed automatically for smart and data-driven decision making. In such situations, the effectiveness and efficiency of traditional operational research methods are questionable. In recent years, the inefficiency of traditional methods in facing the uncertainty caused by big data has led researchers to integrate
The objective of this study is to provide an overview of the use of data-driven optimization in academia and practice from the following perspectives:
How can integrate artificial intelligence techniques with mathematical programming models to develop the intelligencete and data-driven
We demonstrate the use of data-driven optimization across three case studies from operations research.
In this regard, this chapter reviews recent advances in data-driven optimization that highlight the integration of mathematical programming and ML for decision-making under uncertainty and identifies potential research opportunities. We compare data-driven optimization performance to conventional models from optimization methodology. We summarize the existing research papers on data-driven optimization under uncertainty and classify them into three categories: Data-driven stochastic program, Data-driven robust optimization, and Data-driven chance-constrained, according to their unique approach to uncertainty modeling distinct optimization structures. Based on the literature survey, we identify five promising future research directions on optimization under uncertainty in the era of big data and DL, (i) Employment of DL in the field of data-driven optimization under uncertainty, (ii) Deep data-driven models, (iii) Online learning-based data-driven optimization, (iv) Leveraging RL techniques for optimization, and (v) Deep RL for solving NP-hard problems and highlight respective research challenges and potential methodologies. We conducted an extensive literature review on recent papers published across the premier journals between 2002 and 2020 in our field, namely, the European Journal of Operational Research, Operations Research, Journal of Cleaner Production, Production and Operations Management, Journal of Operations Management, Computers in Industry, and Decision Sciences. We specifically searched for papers containing “big data”, “data-driven optimization”, “artificial intelligence”, “machine learning”, “deep learning”, and “Reinforcement learning”. However, our research into the existing literature reveals a scarcity of research works utilizing DL and RL in these disciplines.
The remainder of this paper is organized as follows: Section 2 provides an introduction to the mathematical optimization method. In Section 3, a brief review of AI methods such as ML, DL, and RL is provided. In sections 4–6, applying different ML, DL, and RL techniques in data-driven optimization under uncertainty are presented. Finally, the book chapter ends with the conclusion, some managerial implications, and future research recommendations.
In recent years, mathematical programming techniques for decision-making under uncertainty have been applied in many science and engineering areas, including process design, production scheduling and planning, design, control, and supply chain optimization.
Optimization under uncertainty has been motivated because parameters involved in optimization models for design, planning, scheduling, and supply chains are often uncertain parameters such as product demands, prices of raw material, product, and yields.
A major modeling decision in optimization under uncertainty is whether the decision-maker should rely on robust optimization to use stochastic programming [7]. The robust optimization basis idea is to guarantee feasibility over a specified uncertainty set. In contrast, in the stochastic programming approach, a subset of decisions is set by anticipating that recourse actions can be taken once the uncertainties are revealed over a pre-specified scenario with discrete probabilities of uncertainties. The robust optimization basis idea is to guarantee feasibility over a specified uncertainty set. In contrast, in the stochastic programming approach, a subset of decisions is set by anticipating that recourse actions can be taken once the uncertainties are revealed over a pre-specified scenario with discrete probabilities of the uncertainties.
In general, the optimization approach tends to be more appropriate for short-term scheduling problems in which feasibility over a specified set of uncertain parameters is a major concern and when there is not much scope for recourse decisions. On the other hand, the stochastic programming approach tends to be more appropriate for long-term production planning and strategic design decisions.
In this section, the authors briefly explain three leading modeling paradigms for optimization under uncertainty, namely stochastic programming, robust optimization, and chance-constrained programming.
Under uncertainty, a common decision-making approach is stochastic programming, aiming to optimize the expected objective value across all the uncertainty realizations [8]. The stochastic programming key idea is to model the randomness in uncertain parameters with probability distributions. In this approach, the first stage, all the decisions must be made without knowing precisely the uncertainty realizations. The decision-maker then waits for resolving the uncertainty and knowing the actual value of the uncertain parameters. In the second stage, the decision-maker takes corrective actions after uncertainty is revealed. The stochastic programming approach has demonstrated various applications, such as inventory routing problems [9], supply chain network modeling [10], distributed energy systems design [11], optimal tactical planning [12], and energy management [13].
Robust optimization is a promising alternative paradigm to optimization under uncertainty that does not require accurate knowledge on probability distributions of uncertain parameters. The key idea of robust optimization is to construct a convex uncertainty set of possible realizations of the uncertain parameters and then optimize against worse case realization within this set [14]. A robust optimization framework aims to hedge against the worst-case within the uncertainty set. The robust optimization approach has demonstrated various applications, such as supply chain planning [15], supply chain management [16], inventory management [17].
Chance constrained programming is another common paradigm for optimization under uncertainty with soft probabilistic constraints on the decision variable in place of the hard ones present in robust optimization. Specifically, chance-constrained programming aims to compute a solution that satisfies the constraint with high probability in an uncertain environment. In the chance-constrained optimization paradigm, the probability distribution of uncertain parameters should be known to capture the randomness of uncertain parameters. Chance constrained programs are increasingly used in many applications, such as robotics [18], stochastic model predictive control [19], energy systems [20], and autonomous driving [21].
All mathematical optimization methods are inefficient and effective in facing uncertainty caused by the large volume of data. In the following section, three AI areas as tools for compensating the weaknesses of mathematical optimizing methods are introduced. The term “AI” is often used to describe machines (or computers) that mimic “cognitive” functions that humans associate with the human mind, such as “learning” and problem-solving” [22]. A brief description of the three main areas of AI, including ML, DL, and RL, is provided in the following.
ML is a sub-area of AI that can automatically extract artificial information and knowledge from diverse data types with high speed. The advancement in computational power and the emergence of big data have led to ML optimization and simulation methods. Analysis of big data by ML offers considerable advantages for integrating and evaluating large amounts of complex data [23]. ML solutions have scalability and flexibility compared with traditional statistical methods, making them deployable for many tasks, such as clustering, classification, and prediction. ML models have demonstrated outstanding ability for learning intricate patterns that enable them to make predictions about unobserved data. In addition to using models for prediction, it can accurately interpret what a model has learned.
ML techniques use large sets of data inputs and outputs to recognize patterns and effectively “learn” to make autonomous recommendations or decisions [24]. These algorithms attempt to minimize their errors and maximize the likelihood of their predictions being true [25]. The predictive abilities of ML models are increasingly applied in various fields such as healthcare, genetic, finance, education, and production.
In real applications, uncertainty data exhibit highly complex and nonlinear characteristics. DL is an ML technique and includes algorithms and computational models that imitate the architecture of the biological neural networks in the brain [artificial neural networks (ANNs)] [25]. The DL technology consists of numerous layers responsible for extracting important abstract features from the data [26]. It can process a large volume of data through a complex architecture [27]. DL algorithms can uncover useful uncertainty data patterns for mathematical programming [28]. Recently, the DL technique has been used in optimization under uncertainty.
In particular, RL has gained tremendous attraction recently in different research areas. In RL, an agent gains experience from directly interacting with the environment and selecting an optimal action. RL is concerned with how a software agent should choose an action to maximize a cumulative reward. Combining DL with the RL technique creates the concept of deep RL, which enables RL to tackle the previously intractable decision-making problems. Inspired by the recent advances of deep RL in video games, robotics, and cyber-security, it has been used in optimization problems.
After introducing mathematical optimization methods and three main AI areas, it is time to pay to apply ML, DL, and RL methods in data-driven optimization. They are discussed in turn in the following sections.
In the big data and ML era, a large amount of interactive data are routinely generated and collected in different industries. Intelligence and data-driven analysis and decision-making have a critical role in process operations, design, and control. The success of the DSS depends primarily on the ability to process and analyze large amounts of data and extract relevant and useful knowledge and information from them. In this context, the data-driven approach has gained prominence due to its provision of insights for decision-making and easy implementation. The data-driven optimization framework is a hybrid system that integrates AI and optimization methods for devising a data-driven and intelligent DSS. The data-driven system applied ML techniques for uncertainty modeling. The data-driven approach can discover various database patterns without relying on prior knowledge while also can handle multiple scenarios and flexible objectives. It can also extract information and knowledge from data without speed [29, 30].
The framework of data-driven optimization under uncertainty could be considered a hybrid system that integrates the data-driven system based on ML to extract useful and relevant information from data. The model-based system is based on mathematical programming to derive the optimal decisions from the information [28]. The inability of traditional optimization methods to analyze big data, as well as recent advances in ML techniques, made data-driven optimization a promising way to hedge against uncertainty in the era of big data and ML. Therefore, these promises create the need for organic integration and effective interaction between ML and mathematical programming. In existing data-driven optimization frameworks, data serve as input to a data-driven system. After that, useful, accurate, and relevant uncertainty information is extracted through the data-driven system and further passed along to the model-based system based on mathematical programming for rigorous and systematic optimization under uncertainty, using paradigms such as robust optimization and stochastic programming.
The various ML techniques and their potentials applications in data-driven optimization under uncertainty are presented in the following.
The stochastic programs are used where the distribution of the uncertain parameters is only observable through a finite training dataset [31]. As the primary assumption in the stochastic programming approach, the probability distribution of uncertain parameters should be clear. However, such complete knowledge of parameters probability distribution is rarely available in practice. In practice, instead of knowing the actual distribution of an uncertainty parameter, what the decision-maker has is a set of historical/ or real-time uncertainty data and possibly some prior structure knowledge of the probability. Also, the assumed possibility distribution of uncertain parameters may deviate from their actual distribution. Moreover, relying on a single probability distribution could lead to sub-optimal solutions or even lead to the deterioration in out-of-sample performance [32]. Motivated by these stochastic programming weaknesses, DRO emerges as a new data-driven optimization paradigm that hedges against the worst-case distribution in an ambiguity set [28]. DRO paradigm integrates data-driven systems and model-based systems. A data-driven approach is applied in the DRO model to construct an uncertainty set of probability distributions from uncertainty data through statistical inference and big data analytics [28]. In data-driven stochastic modeling, the uncertainty is modeled via a family of probability distributions that well capture uncertainty data on hand [28]. This set of probability distributions is referred to as an ambiguity set. With this ambiguity set, a model is then proposed for problem design. Finally, a solution strategy is applied for solving the optimization problem. For example in the literature, the Wasserstein metric has been used, to construct a ball in the space of (multivariate and non-discrete) probability distributions centered at the uniform distribution on the training samples, to seek decisions that perform best in view of the worst-case distribution within this Wasserstein ball [31]. Different practical approaches, such as the moment-based, and the adopted distance metric, were employed for uncertainty constructing [33, 34], and [31]. DRO is an effective method to address the inexactness of probability distributions of uncertain parameters in decision-making under uncertainty that can be applied for optimizing supply chain activities, for planning and scheduling under uncertainty. This way reduces the modeling difficulty for uncertain parameters. Wang & Chen [35] proposed a two-stage DRO model considering scarce data of disasters. A moment-based fuzzy set describes uncertain distributions of blood demand to optimize blood inventory prepositioning and relief activities together. Chiou [36], to regulate the risk associated with hazardous material transportation and minimize total travel cost on the interested area under stochasticity, presented a multi-objective data-driven stochastic optimization model to determine generalized travel cost for hazmat carriers. Gao et al. [37] proposed a two-stage DRO model for better decision making in optimal design and shale gas supply chains under uncertainty. They applied a data-driven approach to construct the ambiguity set based on principal component analysis and first-order deviation functions. In the other study, Ning & You [28] proposed a novel data-driven Wasserstein DRO model for biomass with agricultural waste-to-energy network design under uncertainty. They proposed a data-driven approach to construct the Wasserstein ambiguity set for the feedstock price uncertainty, which is utilized to quantify their distances from the data-based empirical distribution.
A robust optimization is a popular approach for optimization under uncertainty. It defines an uncertainty set of possible realizations of the uncertain parameters and then optimizes against worst-case realizations within this set [5, 6]. In real-world applications, the underlying distribution of uncertainties may be intrinsically complicated and vary under different circumstances [38]. Choosing the accurate underlying distribution of uncertainties and the uncertainty sets by prior knowledge is somewhat challenging in practice. In robust optimization, the uncertainty is formed as an uncertainty set in which any point is a possible scenario [39]. Since the uncertainty set includes the worst case, robust optimization may be over-conservative. It is essential to apply the appropriate approach to construct the uncertainty set and adjust the conservatism level simultaneously [39]. As an essential ingredient in robust optimization, uncertainty sets endogenously determine robust optimal solutions and, therefore, should be devised with special care [28]. However, uncertainty sets in the conventional robust optimization methodology are typically set a priori using a fixed shape and model without providing sufficient flexibility to capture the structure and complexity of uncertainty data [28]. For instance, the geometric shapes of uncertainty set in the conventional robust optimization methodology do not change with the intrinsic structure and complexity of uncertainty data. Furthermore, these uncertainty sets are specified by a finite number of parameters, thereby limiting modeling flexibility. Motivated by this knowledge gap, data-driven robust optimization emerges as a powerful paradigm for addressing uncertainty in decision making.
Choosing a good uncertainty set enables robust optimization models to provide better solutions than other approaches solutions [5, 6]. Poor choice of the uncertainty set makes robust optimization model overly conservative or computationally intractable. In the era of big data, many data are routinely generated and collected containing abundant information about the distribution of uncertainties; thereby, ML tools can construct the uncertainty sets based upon these data. Data-driven robust optimization is a new paradigm for hedging against uncertainty in the era of big data. The ML tools can be applied to estimate data densities with sufficient accuracy and construct an appropriate uncertainty set based upon intelligent analysis and the use of uncertainty data for modeling robust optimization problems. A desirable uncertainty set shall have enough flexibility to adapt to the intrinsic structure behind data, thereby characterizing the underlying distribution and facilitating the solutions.
Data-driven robust optimization could be considered a “hybrid” system that integrates the data-driven system based on ML to construct the uncertainty set from historical uncertainty data. The model-based system is based on the robust programming model to derive the optimal decisions from the information. More specifically, data serves as input to a data-driven system. Figure 1 presents the data-driven optimization paradigm framework. After that, the data-driven method constructs the uncertainty set to extract information from historical data fully. Constructing the uncertainty sets based upon historical data can be considered as an unsupervised learning problem from an ML perspective. So, data-driven robust optimization is a hybrid system that utilizes ML techniques to design data-driven uncertainty sets and develops a robust optimization problem from the data-driven set. Different effective unsupervised learning models such as the Dirichlet process mixture model, maximum likelihood estimation, principal component analysis, regular and conservative support vector clustering, Bayesian ML, and kernel density estimation were employed for uncertainty constructing, which could provide powerful representations of data distributions [38, 40, 41]. Uncertainty set is the set that can offer robust solutions with a conservatism level. Furthermore, this uncertainty set is finally given to the model-based system based on robust optimization to obtain robust solutions under uncertainty.
The schematic of the data-driven optimization paradigm framework.
ML methods of support vector clustering-based uncertainty set (SVCU) and conservative support vector clustering-based uncertainty set (CSVCU) have been applied to finding an enclosed hypersphere with minimum volume which is able to cover all data samples as tightly as possible as uncertainty sets. Conservative support vector clustering is the most suitable choice for obtaining robust solutions in cases with sufficient data to construct an uncertainty set enclosing future data with a high confidence level [42]. Furthermore, it is the most effective choice for obtaining lower conservative solutions. On the other hand, CSVCU is suitable for highly conservative decision-makers since it is the only set that can offer robust solutions with a high conservatism level, particularly when there is limited data [42]. A data-driven robust optimization under correlated uncertainty was proposed to hedge against the fluctuations generated from continuous production processes in an ethylene plant [43]. For capturing and enrich the valid information of uncertainties, a copula-based method is introduced to estimate the joint probability distribution and simulate mutual scenarios for uncertainties. A deterministic and data-driven robust optimization framework was proposed for energy systems optimization under uncertainty. The uncertainty set is constructed by support vector clustering based on real industrial data [39]. A data-driven robust optimization was applied to design and optimize the entire wastewater sludge to-biodiesel supply chain [42]. They develop a conservative support vector clustering (CSVS) method to construct an uncertainty set from limited data. The developed uncertainty set encloses the fuzzy support neighborhood of data samples, making it practical even when the available data is limited.
Chance constrained programming is a practical and convenient approach to control risk in decision-making under uncertainty. However, due to unknown probability distributions of uncertainty parameters, the solution obtained from a chance-constrained optimization problem can be biased. In practice, instead of knowing the actual distribution of an uncertainty parameter, only a set of historical/ or real-time uncertainty data, which can be considered as samples taken from the actual (while ambiguous) distribution, can be observed and stored. On the other hand, even if the probability distribution of an uncertainty parameter is available, the chance-constrained program is computationally cumbersome. Motivated by Chance constrained programming weaknesses, data-driven chance-constrained optimization emerges as a new data-driven optimization paradigm. The data-driven stochastic programming approach is a data-driven risk-averse strategy to handle uncertainties in the era of big data effectively.
In contrast to the data-driven stochastic programming approach, data-driven chance-constrained programming is another paradigm focusing on chance constraint satisfaction under the worst-case probability instead of optimizing the worst-case expected objective. Although both data-driven chance-constrained programs and DRO adopt ambiguity sets in the uncertainty models, they have distinct model structures. Specifically, the data-driven chance-constrained program features constraints subject to uncertainty in probability distributions. Simultaneously, DRO typically only involves the worst-case expectation of an objective function concerning a family of probability distributions [28]. In the data-driven stochastic programming approach, historical data is utilized to learn the uncertain parameters’ distributions.
Data-driven chance-constrained programs with moment-based ambiguity sets, distance-based ambiguity set, Prohorov metric-based ambiguity sets [44], φ-divergence based ambiguity set [45], kernel smoothing method [46], Wasserstein ambiguity set [47].
Ghosal and Wiesemann [48] applied for Data-driven chance-constrained programs in the capacitated vehicle routing problem (CVRP), which asks for the cost-optimal delivery of a single product geographically dispersed customers through a fleet of capacity-constrained vehicles. They model the customer demands as a random vector whose distribution is only known to belong to an ambiguity set.
The recent development in the data science field, AI, and ML techniques have enabled intelligent and automated DSS and real-time analytics coupled with computing power improvements. Thus, AI techniques are applied to big data sources to extract the knowledge-based rules or identify the underlying rules and patterns by ML techniques, to drive the systems toward set objectives. DL is an ML technique that can extract high levels of information and knowledge from massive data volumes. DL algorithms consist of multiple processing layers to learn representations of data with multiple abstraction levels [26]. For example, recently, DL techniques have been used to accurately forecasting customer demand, price, and inventory leading to optimization of supply chain performance. An intelligent forecasting system leads to optimize performance, reduce costs, and increase sales and profit. DL techniques can apply deep neural network architectures to solve various complex problems. The DL paradigm requires high computing power and a large amount of data for training. The recent advances in parallel architectures and GUP (Graphical Processing Unit) enabled the necessary computing power required in deep neural networks (DNN). The emergence of advanced IoT and blockchain technologies has also solved the need for a large amount of data to learn. IoT and blockchain result in massive amounts of streaming real-time data often referred to as “big data,” which brings new opportunities to control and manage supply chains [49]. Optimizing the parameters in DNN is a challenging undertaking. Several optimization algorithms such as Adam, Adagrad, RMSprop, have been proposed to optimize the network parameters in DNN and improve generalizability. This technique, which stabilizes the optimization, paved the way for learning deeper networks [50]. In real applications, uncertainty data exhibit very complex and highly nonlinear characteristics. DNN can be used to uncover useful patterns of uncertainty data for optimizing under uncertainty [28]. Deep data-driven optimization could be considered a “hybrid” system that integrates the deep data-driven system based on DL to forecast the uncertainty parameters. The model-based system is based on mathematical programming to drive the optimal decisions from predicted parameters (the deep data-driven system). In the DL-based system, DNN has been applied to analyze features, complex interactions, and relationships among features of a problem from samples of the dataset and learn model, which can be used for demand, inventory, and price forecasting. Kilimci et al. [51] developed an intelligent demand forecasting system based on the analysis and interpretation of the historical data using different forecasting methods, including support vector regression algorithm, time series analysis techniques, and DL models. In a study, the Auto-Regressive Integrated the backpropagation (BP) network method, recurrent neural network (RNN) method, and Moving Average (ARIMA) model were tested to forecast the price of agricultural products [52]. Yu et al. [53] developed an online big-data-driven forecasting model of Google trends to improve oil consumption prediction. Their proposed forecasting model considers traditional econometric models (LogR and LR) and typical AI techniques (BPNN, SVM, DT, and ELM).
Accurate automatic optimization heuristics are necessary for dealing with the complexity and diversity of modern hardware and software. ML is a proven technique for learning such heuristics, but its success is bound by the quality of the features used. Developers must handcraft these features through a combination of expert domain knowledge and trial and error. This makes the quality of the final model directly dependent on the skill and available time of the system architect. DL techniques are a better way to build heuristics. A deep neural network can learn heuristics over raw code entirely without using code features. The neural network simultaneously constructs appropriate representations of the code and learns how best to optimize, removing the need for manual feature creation. DNN can improve the accuracy of models without the help of human experts. Generally, this approach is a fundamental way to integrate forecast approaches into mathematical optimization models. First, a probabilistic forecast approach for future uncertainties is given by exploiting the advanced DL structures. Second, a model-based system based on mathematical programming is applied to derive the optimal decisions from the forecasting data. Comparison and evaluation of the forecasting models are significant since DL models can have different performances depending on the properties of the data [54, 55]. The performances of DL models differ according to the forecasting time, training duration, target data, and simple or ensemble structure [56, 57].
In a study, Nam et al. [54, 55] applied DL-based models to forecast fluctuating electricity demand and generation in renewable energy systems. This study compares and evaluates DL models and conventional statistical models. The DL models include DNN, long short-term memory, gated recurrent unit, and the disadvantages of conventional statistical models such as multiple linear regression and seasonal autoregressive integrated moving average. In another study, the operation of a cryogenic NGL recovery unit for the extraction of NGL has been optimized by implementing data-driven techniques [58]. The proposed approach is based on an optimization framework that integrates dynamic process simulations with two DL-based surrogate models using a long short-term memory (LSTM) layout with a bidirectional recurrent neural network (RNN) structure. Kilimci et al. [51] developed an intelligent demand forecasting system. This improved model is based on analyzing and interpreting the historical data using different forecasting methods, including time series analysis techniques, support vector regression algorithm, and DL models.
Accessing a sufficient amount of data for some optimization models is a practical challenge. For example, the quality of scenario-based optimization frameworks strongly depends on access to a sufficient amount of uncertain data. However, in practice, the amount of uncertainty data sampled from the underlying distribution is limited. On the other hand, acquiring a sufficient amount of uncertainty data is extremely time-consuming and expensive in some cases, which leads to the limited application of some approaches [59]. To deal with the practical challenge of requiring an insufficient amount of data, deep generative models emerge as a new paradigm to generate synthetic uncertainty data with the aim of better decisions with insufficient uncertainty data. DL techniques could be applied to learn the useful intrinsic patterns from the available uncertainty data and generate synthetic uncertainty data. More specifically, in deep generative models, the correct data distribution is mimicked either implicitly or explicitly by the DL techniques. Then the learned distribution is used to generate new data points referred to as synthetic data [28]. After that, these synthetic data serve as input to an optimizing model to derive the optimal decisions. Some of the most commonly used deep generative models are variational autoencoders generative and adversarial networks [26]. These synthetic uncertainty data generated by the DL techniques can be potentially useful in the scenario-based optimization model.
DL models are a class of approximate models proven to have strong predictive capabilities for representing complex phenomena [60]. Approximate models are currently experiencing a radical shift due to the advent of DL. However, our research into the existing literature reveals a scarcity of research utilizing DL in approximate modeling. The introduction of DL models into an optimization formulation provides a means to reduce the problem complexity and maintain model accuracy [60]. Recently it has been shown that DL models in the form of neural networks with rectified linear units can be exactly recast as a mixed-integer linear programming formulation. DL is a method to approximate complex systems and tasks by exploiting large amounts of data to develop rigorous mathematical models [60].
Using DNN to model real-world problems is a powerful tool, as they provide an efficient abstraction that can be used to analyze the structure of the task at hand. The rigorous mathematical model is developed based on neural networks modeling complex systems and optimizing their operations in the deep data-driven model framework. This approximate model is developed by exploiting large amounts of data using DL techniques. Then the solving method is applied to obtain the optimal solutions of the developed optimization model. Developing an optimal solution to the approximate model remains challenging [60].
Pfrommer et al. [61] utilized a stochastic genetic algorithm to optimize a composite textile draping process where a neural network was utilized as a surrogate model. Marino et al. [62] presented an approach for modeling and planning under uncertainty using deep Bayesian neural networks (DBNNs). They use DBNNs to learn a stochastic model of the system dynamics. Planning is addressed as an open-loop trajectory optimization problem. In the study, DL-based surrogate modeling and optimization were proposed for microalgal biofuel production and photobioreactor design [63]. This surrogate model is built upon a few simulated results from the physical model to learn the sophisticated hydrodynamic and biochemical kinetic mechanisms; then adopts a hybrid stochastic optimization algorithm to explore untested processes and find optimal solutions. Tang & Zhang [64] developed a deep data-driven framework for modeling combustion systems and optimizing their operations. First, they developed a deep belief network to model the combustion systems. Next, they developed a multi-objective optimization model by integrating the deep belief network-based models, the considered operational constraints, and the control variable constraints.
In conventional data-driven optimization frameworks, a set of uncertainty data serves as input to the data-driven system, in which learning typically takes place once by using learning techniques. This approach fails to account for real-time uncertainty data [28]. For example, in the DRO method, the uncertainty set of probability distributions is constructed from uncertainty data. Once the uncertainty sets of probability distributions are obtained, they remain fixed for the model-based system based on mathematical programming and are not updated or refined. However, in real practice, a vast number of uncertainty data are generated and collected sequentially in an online fashion; therefore, data-driven systems should be developed to analyze the real-time data. An online-learning-based data-driven optimization framework emerges as a new data-driven optimization paradigm. Learning takes place iteratively to account for real-time data, and the data-driven system is updated in an online fashion. The framework of online-learning-based data-driven optimization could be considered a hybrid system that integrates the online data-driven and model-based systems. In the online data-driven system, the real-time uncertainty data should be saved and analyzed sequentially based on ML to extract sequentially useful and relevant information from the real-time data. The online data-driven system (such as the uncertainty sets, probability distributions sets, and forecasting data) that serve as input to a model-based system should be updated in an online fashion. Then in the model-based system, the optimal decisions are made sequentially from the real-time information based on mathematical programming. There is a “feedback” channel for information flow returning from the model-based system to the data-driven system in this framework. The information flow is fed into the mathematical programming problem from the ML results. Using the feedback control strategy delivers amazingly superior system performance (e.g., stability, robustness to disturbances, and safety) [28]. Figure 2 presents the potential schematic of the online learning-based data-driven optimization system.
The schematic of the “closed-loop” online learning-based data-driven optimization framework.
The online-learning-based data-driven optimization framework, updating the data-driven systems, and developing efficient algorithms to solve online learning-based mathematical programming problems have become challenging.
RL has transformed AI, especially after the success of Google DeepMind. This branch of ML epitomizes a step toward building autonomous systems by understanding the visual world. Deep RL is currently applied to different sorts of problems that were previously obstinate. In this subsection, the authors will analyze Deep RL and its applications in optimization.
RL is one of the ML areas recently applied to tackle complex sequential decision problems. RL is concerned with how a software agent should choose an action to maximize a cumulative reward. RL is considered an optimal solution in addressing challenges where many factors must be taken into account, like supply chain management. For example, Q-learning is a type of RL algorithm that is applied to tackle simple optimization problems. In this approach, the Q-value has been applied to any state of the system. Although the classical RL algorithms guarantee optimal policy, these algorithms cannot promptly solve large states or actions. Many problems in the real world have large and action spaces. Applying RL algorithms for solving large problems would be nearly impossible, as these models would be costly to train. Therefore, deep RL emerges as a new method in which DNN is used to approximate any of the following RL components. Recently, deep Q-network (DQN) algorithms have been used in different areas. For example, deep Q-network (DQN) algorithms have been applied to solve supply chain optimization problems. These DQNs operate as the decision-maker of each agent. That results in a competitive game in which each DQN agent plays independently to minimize its own cost. Instead, recently a unified framework has been proposed in which the agents still play independently from one another. Still, in the training phase, this model uses a feedback scheme so that the DQN agent learns the total cost for the whole network and, over time, learns to minimize it.
Like other types of reinforcement ML technique, multi-agent RL is a system of agents (e.g., robots, machines, and cars) interacting within a common environment. Each agent decides each time-step and works along with the other agent(s) to achieve a given goal. The agents are learnable units that want to learn policy on the fly to maximize the long-term reward through the interaction with the environment. Recently the multi-agent RL techniques have been applied to develop the supply chain management (SCM) systems that perform optimally for each entity in the chain. A supply chain can be defined as a network of autonomous business entities collectively responsible for procurement, manufacturing, storing, and distribution [65]. Entities in a supply chain have different sets of environmental constraints and objectives.
One of the biggest challenges of the development of MAS based supply chain is designing agent policies. To address designing agent policies, recently, automatic policy designing by RL has drawn attention. RL is considered an optimal solution in addressing challenges where a huge number of factors must be taken into account, like SCM. RL technique does not require datasets covering all environments, constraints, operations, and entity operation results. A multi-agent RL (MARL)-based SCM system can enable agents to learn automatically policies that optimize the supply chain performance using RL concerning certain constraints, environments, and objectives to optimize the performance. More specifically, the RL technique enables an agent to learn a policy by correcting necessary data itself during trial-and-error on the content of operations [66]. All agents also simultaneously cooperate to optimize the performances of the entire supply chain. RL technique can be applied for a certain problem when all processes concerning the problem satisfy a Markov property. Environmental change for a certain agent depends on the previous state of the environment and the agent’s action. It is impossible to assume the Markov property because an agent’s environmental change depends on the previous state for the agent and the other agent’s actions.
There are two problems in developing a MARL technique for SCM: Building Markov decision processes for a supply chain and then avoiding learning stagnation among agents in learning processes. For solving these problems, a learning management method with deep neural network (DNN)-weight evolution (LM-DWE) has been applied [67]. Fuji et al. [67] developed a multi-agent RL technique to develop a supply chain management (SCM) system that enables agents to learn policies that optimize SC performance. They applied a learning management method with deep-neural-network (DNN)-weight evolution (LM-DWE) in the MARL for SCM. An RL framework-FeedRec has been used in a study to optimize long-term user engagement [68]. They used hierarchical LSTM to design the Q-Network to model the complex user behaviors; they also used Q Network to simulate the environment. Zhang et al. [69] proposed a multi-agent learning (MAL) algorithm and applied it for optimizing online resource allocation in cluster networks.
Optimization in current DSS has a highly interdisciplinary nature related to integrating different techniques and paradigms for solving complex real-world problems. The design of efficient NP-hard combinatorial optimization problems is a fascinating issue and often requires significant specialized knowledge and trial-and-error. NP-hard problems are solved with exact methods, heuristic algorithms, or a combination of them. Although exact methods provide optimal answers, they have the limitation of performing inefficiently in time complexity. Heuristics are used to improve computational time efficiency and provide decent or near-optimal solutions [70]. According to the definition of Burke et al. [71], a hyper-heuristic is a searching mechanism that aims to select or generate appropriate heuristics to solve an optimization problem. However, the effectiveness of general heuristic algorithms is dependent on the problem being considered, and high levels of performance often require extensive tailoring and domain-specific knowledge. ML strategies have become a promising route to addressing these challenges, which led to the development of meta-algorithms to various combinatorial problems.
Solution approaches meta-heuristics and hyper-heuristics have been developed to tackle the NP-hard combinatorial optimization problem [72]. Recently, hyper-heuristics arise in this context as efficient methodologies for selecting or generating (meta) heuristics to solve NP-hard optimization problems. Hyper-heuristics are categorized into heuristic selection (Methodologies to select) and heuristic generation (Methodologies to generate) [71]. Deep RL is a possible learning method that can automatically solve various optimization problems [73]. Encouragingly, characteristics of the deep RL method have been found in comparison with classical methods, e.g., strong generalization ability and fast solving speed. RL methods can be used at different levels to solve combinatorial optimization problems. They can be applied directly to the problem, as part of a meta-heuristic, or as part of hyper-heuristics [74]. Utilizing advanced computation power with meta-heuristics algorithms and massive-data processing techniques has successfully solved various NP-hard problems. However, meta-heuristic approaches find good solutions which, do not guarantee the determination of the global optimum. Meta-heuristics still face the limitations of exploitation and exploration, which consists of choosing between a greedy search and a wider exploration of the solution space.
A way to guide Meta-heuristic algorithms during the search for better solutions is to generate the initial population of a genetic algorithm by using a technique of Q-Learning algorithm.
The hyper-heuristic for heuristic selection can use RL algorithms, enabling the system to autonomously select the meta-heuristic to use in the optimization process and the respective parameters. For example, Falcão et al. [74] proposed a hyper-heuristic module for solving scheduling problems in manufacturing systems. The proposed hyper-heuristic module uses an RL algorithm, which enables the system to autonomously select the meta-heuristic to use in the optimization process and the respective parameters. Cano-Belmán et al. [75] proposed a heuristic generation scatter search algorithm to address a mixed-model assembly line sequencing problem. Khalil et al. (Dai et al., 2017) developed a neural combinatorial optimization framework that utilizes neural networks and RL to tackle combinatorial optimization problems. The developed meta-algorithm automatically learns good heuristics for a diverse range of optimization problems over graphs. Mosadegh et al. [72] proposed novel hyper-simulated annealing (HSA) to tackle the NP-hard problem. They developed new mathematical models to describe a mixed-model sequencing problem with stochastic processing times (MMSPSP). The HSA applies a Q-learning algorithm to select appropriate heuristics through its search process [72]. The main idea is to conduct simulated annealing (SA)-based algorithms to find a suitable heuristic among available ones creating a neighbor solution(s).
The first case study focuses on the production schedule. The data-driven robust optimization applied for an ethylene plant is predicted to hedge against the fluctuations generated from continuous production processes. For capturing and enrich the valid information of uncertainties, copulas are introduced to estimate the joint probability distribution and simulate mutual scenarios for uncertainties [43]. For this purpose, cutting planes are generated to remove unnecessary uncertain scenarios in the uncertainty sets. Then robust formulations induced by the cut set are proposed to reduce conservatism and improve the robustness of scheduling solutions. They consider the robust counterpart induced by the classical uncertainty set, where the difference to the best possible solution over all scenarios is to be minimized. Instead of focuses on simple uncertainty sets that are either finite or hyperboles, they considered problems with more flexible and realistic ellipsoidal uncertainty sets. In this research, the cut sets of flexible uncertainty sets are proposed. They used the historical data to correct the uncertainties and drive the reformulation of constraints with uncertainties. The new robust formulations induced by cut sets are derived for linear programming (LP) and mixed-integer linear programming (MILP) problems. Through the real-world ethylene plant example, the correlations between uncertain consumption rates of furnaces are analyzed.
In this research, Decision-makers prefer to obtain robust solutions immune to most high-frequency uncertain scenarios. Since in production scheduling problems, many uncertainties are associated with the entire production network, a process, or equipment, which makes them correlated and difficult to be separated. So, in this optimization research, uncertainties are assumed to be dependent. In this research, the cut sets of flexible uncertainty sets are proposed.
Deterministic solutions are regarded as theoretically optimal at most times, and robust solutions provide references for decision-makers, which may not be optimal but feasible and applicable. It is always neglected that stricter descriptions of uncertainties could also create great profits. The full coverage of uncertain values usually leads to unpractical and conservative results. The improper simplification of uncertainty scenarios will cause infeasibility when the solutions are implemented in the volatile production process. Thus, historical data should be introduced to correct the uncertainties and drive the reformulation of constraints with uncertainties. For eliminating the worst-case formulation scenario for robust optimization and decrease conservatism, the cut set of flexible uncertainty sets is constructed by introducing cutting planes. Cutting planes are generated to construct cut sets for the outer approximation of most uncertain scenarios. Since the size of the uncertainty set directly influences the quality of robust solutions, in this research, the more uncertain values are considered.
They stated that utilizing the data-driven robust optimization approach causes the decision-makers to have the ability to decide how many uncertain scenarios are considered in the model and to provide effective, economical, and robust scheduling plans. Finally, it causes fluctuations in the production performance captured and controlled below a lower level of conservatism.
Designing and optimizing the wastewater sludge-to-biodiesel supply chain facilitates the development of its large-scale production [42]. Hence, this case study evaluates Data-driven robust optimization for supply chain designing and optimization. The entire wastewater sludge-to-biodiesel supply chain over multiple periods is systematically designed and optimized based on the uncertainty sets constructed from the data of uncertain parameters. In this research, a data-driven robust optimization has been adopted, which constructs the uncertainty sets from the data of uncertain parameters utilizing support vector clustering. In contrast, the conventional uncertainty sets are driven without incorporating the data, which results in a high cost of robustness. The developed uncertainty set in this research encloses the fuzzy support neighborhood of data samples that makes it practical even when the available data is limited. The research results show that the proposed data-driven robust optimization approach can yield robust supply chain decisions with the same degree of robustness but at a lower cost than robust conventional optimization approaches.
Our third case study relates to forecasting fluctuating electricity demand and generation variation, aiming to develop an energy forecasting model with renewable energy technologies [54, 55]. Wind and solar energy sources are erratic and difficult to implement in renewable energy systems; therefore, circumspection is needed to implement renewable energy systems and policies. This translates into the DL-based models for forecasting fluctuating electricity demand and generation in renewable energy systems.
This study compares and evaluates DL models and conventional statistical models. The DL models include DNN, long short-term memory, gated recurrent unit, and the disadvantages of conventional statistical models such as multiple linear regression and seasonal autoregressive integrated moving average. Thus, they thoroughly compare and evaluate the forecasting models and select the best forecasting model for future electricity demand and renewable energy generation. They then utilized the proposed model for renewable energy scenarios for Jeju Island’s policy design to achieve their energy policy. The optimal scenario is assessed by considering its strengths, weaknesses, opportunities, and threats analysis while also considering techno-economic-environmental domestic and global energy circumstances.
Data-driven optimization refers to the art and science of integrating the data-driven system based on ML to convert (big) data into relevant and useful information and insights, and the model-based system based on mathematical programming to derive the optimal and more accurate decisions from the information. As a direct implication, the generic approach proposed in data-driven optimization can be utilized to create an automated, data-driven, and intelligent DSS, which would increase the quality of decisions both in terms of efficiency and effectiveness. Recent advances in DL as a predictive model have received great attention lately. One of the distinguishing features of DNN is its ability to “learn” better predictions from large-scale data than ML methods. Hence, one of the primary messages of this overview chapter is to review the applicability of DL in improving DSS across core areas of supply chain operations.
Much data is generated at ever-faster rates by companies and organizations [76]. Applying the advanced DL techniques for predictive analytics becomes a promising issue for further research to improve the decision-making process. Although the conventional data-driven optimization paradigm has made significant progress for hedging against uncertainty, it is foreseeable that data-driven mathematical programming frameworks would proliferate in the next few years due to the generation of large volumes of data and the complexity of relationships among elements. Nowadays, the increase in data acquisition and availability and the emergence of DL makes it imperative to develop data-driven mathematical programming to approximate complex systems under uncertainty. More specifically, a deep data-driven model paradigm, in which the rigorous mathematical model is developed based on neural networks to modeling complex systems and optimizing their operations, could be a promising research direction.
Furthermore, there are some research challenges associated with conventional data-driven optimization frameworks. For example, updating the data-driven system and learning based on real-time data in the data-driven model frameworks can be a key research challenge. Future research could be directed toward designing the data-driven system, in which learning takes place sequentially to extract useful and relevant information from real-time uncertainty data. The data-driven systems should be updated in an online fashion.
Developing the mathematical programming problems for an online-learning-based data-driven optimization paradigm creates another challenge. The model-based system can be devised based on the deep data-driven model paradigm and be leveraged the power of DL. Additionally, deep RL can be applied to developing efficient algorithms to solve the resulting online-learning-based mathematical programming problems. Applying deep RL in the paradigm of learning-while-optimizing also could be another promising research direction. Besides, multi-agent RL techniques could be explored by taking advantage of DL to develop complex systems and optimize their performance based on real-time data.
Also, RL is another ML area that has recently been used to model complex systems and problems and to optimize their performance and behaviors. RL is also considered an optimal solution in addressing challenges where many factors must be taken into account. More specifically, deep RL emerges as a new method to solve the various optimization problems automatically. Thereby, applying RL in optimization problems deserves further attention in future research.
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He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. 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Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. 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He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356823",title:"MSc.",name:"Seonghee",middleName:null,surname:"Min",slug:"seonghee-min",fullName:"Seonghee Min",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Daegu University",country:{name:"Korea, South"}}},{id:"353307",title:"Prof.",name:"Yoosoo",middleName:null,surname:"Oh",slug:"yoosoo-oh",fullName:"Yoosoo Oh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Yoosoo Oh received his Bachelor's degree in the Department of Electronics and Engineering from Kyungpook National University in 2002. He obtained his Master’s degree in the Department of Information and Communications from Gwangju Institute of Science and Technology (GIST) in 2003. In 2010, he received his Ph.D. degree in the School of Information and Mechatronics from GIST. In the meantime, he was an executed team leader at Culture Technology Institute, GIST, 2010-2012. In 2011, he worked at Lancaster University, the UK as a visiting scholar. In September 2012, he joined Daegu University, where he is currently an associate professor in the School of ICT Conver, Daegu University. Also, he served as the Board of Directors of KSIIS since 2019, and HCI Korea since 2016. From 2017~2019, he worked as a center director of the Mixed Reality Convergence Research Center at Daegu University. From 2015-2017, He worked as a director in the Enterprise Supporting Office of LINC Project Group, Daegu University. His research interests include Activity Fusion & Reasoning, Machine Learning, Context-aware Middleware, Human-Computer Interaction, etc.",institutionString:null,institution:{name:"Daegu Gyeongbuk Institute of Science and Technology",country:{name:"Korea, South"}}},{id:"262719",title:"Dr.",name:"Esma",middleName:null,surname:"Ergüner Özkoç",slug:"esma-erguner-ozkoc",fullName:"Esma Ergüner Özkoç",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Başkent University",country:{name:"Turkey"}}},{id:"346530",title:"Dr.",name:"Ibrahim",middleName:null,surname:"Kaya",slug:"ibrahim-kaya",fullName:"Ibrahim Kaya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"419199",title:"Dr.",name:"Qun",middleName:null,surname:"Yang",slug:"qun-yang",fullName:"Qun Yang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Auckland",country:{name:"New Zealand"}}}]}},subseries:{item:{id:"38",type:"subseries",title:"Pollution",keywords:"Human activity, Pollutants, Reduced risks, Population growth, Waste disposal, Remediation, Clean environment",scope:"\r\n\tPollution is caused by a wide variety of human activities and occurs in diverse forms, for example biological, chemical, et cetera. In recent years, significant efforts have been made to ensure that the environment is clean, that rigorous rules are implemented, and old laws are updated to reduce the risks towards humans and ecosystems. However, rapid industrialization and the need for more cultivable sources or habitable lands, for an increasing population, as well as fewer alternatives for waste disposal, make the pollution control tasks more challenging. Therefore, this topic will focus on assessing and managing environmental pollution. It will cover various subjects, including risk assessment due to the pollution of ecosystems, transport and fate of pollutants, restoration or remediation of polluted matrices, and efforts towards sustainable solutions to minimize environmental pollution.
",coverUrl:"https://cdn.intechopen.com/series_topics/covers/38.jpg",hasOnlineFirst:!1,hasPublishedBooks:!0,annualVolume:11966,editor:{id:"110740",title:"Dr.",name:"Ismail M.M.",middleName:null,surname:"Rahman",slug:"ismail-m.m.-rahman",fullName:"Ismail M.M. Rahman",profilePictureURL:"https://mts.intechopen.com/storage/users/110740/images/2319_n.jpg",biography:"Ismail Md. Mofizur Rahman (Ismail M. M. Rahman) assumed his current responsibilities as an Associate Professor at the Institute of Environmental Radioactivity, Fukushima University, Japan, in Oct 2015. He also has an honorary appointment to serve as a Collaborative Professor at Kanazawa University, Japan, from Mar 2015 to the present. \nFormerly, Dr. Rahman was a faculty member of the University of Chittagong, Bangladesh, affiliated with the Department of Chemistry (Oct 2002 to Mar 2012) and the Department of Applied Chemistry and Chemical Engineering (Mar 2012 to Sep 2015). Dr. Rahman was also adjunctly attached with Kanazawa University, Japan (Visiting Research Professor, Dec 2014 to Mar 2015; JSPS Postdoctoral Research Fellow, Apr 2012 to Mar 2014), and Tokyo Institute of Technology, Japan (TokyoTech-UNESCO Research Fellow, Oct 2004–Sep 2005). \nHe received his Ph.D. degree in Environmental Analytical Chemistry from Kanazawa University, Japan (2011). He also achieved a Diploma in Environment from the Tokyo Institute of Technology, Japan (2005). Besides, he has an M.Sc. degree in Applied Chemistry and a B.Sc. degree in Chemistry, all from the University of Chittagong, Bangladesh. \nDr. Rahman’s research interest includes the study of the fate and behavior of environmental pollutants in the biosphere; design of low energy and low burden environmental improvement (remediation) technology; implementation of sustainable waste management practices for treatment, handling, reuse, and ultimate residual disposition of solid wastes; nature and type of interactions in organic liquid mixtures for process engineering design applications.",institutionString:null,institution:{name:"Fukushima University",institutionURL:null,country:{name:"Japan"}}},editorTwo:{id:"201020",title:"Dr.",name:"Zinnat Ara",middleName:null,surname:"Begum",slug:"zinnat-ara-begum",fullName:"Zinnat Ara Begum",profilePictureURL:"https://mts.intechopen.com/storage/users/201020/images/system/201020.jpeg",biography:"Zinnat A. Begum received her Ph.D. in Environmental Analytical Chemistry from Kanazawa University in 2012. She achieved her Master of Science (M.Sc.) degree with a major in Applied Chemistry and a Bachelor of Science (B.Sc.) in Chemistry, all from the University of Chittagong, Bangladesh. Her work affiliations include Fukushima University, Japan (Visiting Research Fellow, Institute of Environmental Radioactivity: Mar 2016 to present), Southern University Bangladesh (Assistant Professor, Department of Civil Engineering: Jan 2015 to present), and Kanazawa University, Japan (Postdoctoral Fellow, Institute of Science and Engineering: Oct 2012 to Mar 2014; Research fellow, Venture Business Laboratory, Advanced Science and Social Co-Creation Promotion Organization: Apr 2018 to Mar 2021). 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