\r\n\t
\r\n\tSince its discovery in the mid-50’s, a wide range of applications from low to high voltage appeared, putting polyimide as a key material to design more performing and reliable electrical devices and systems. On another hand, polyimide appears also essential for the development of new electronic devices where further considerations such as high power density, integration, higher temperature, thermal conduction management, energy storage, reliability or flexibility are required in order to sustain the growing electrical energy consumption needs of the global society.
\r\n\tConsequently, polyimide materials have and will have to face new exciting fundamental, technological and environmental challenges among which:
\r\n\t• a better understanding of its intrinsic electrical properties to identify current limitations and propose new advanced device designs,
\r\n\t• the development of innovative composites and nanocomposites structuration to tailor its physical properties by involving classical and original nanoparticles such as graphene layer, carbon nanotubes, metal, silicates, nitrides, etc.,
\r\n\t• the development of polyimide composites for energy storage, thermal management, reinforced nanodielectrics and corona-resistant nanocomposites,
\r\n\t• the development of new low and ultra-low dielectric constant polyimide for microelectronics (fluorinated polyimides, nanoporous, mesoporous),
\r\n\t• the development of new higher temperature reliable polyimide (high glass transition, high degradation temperature),
\r\n\t• the emergence of solvent-free processes to fit with environmental purposes
\r\n\tMoreover, many challenges regarding the aging mechanisms understanding under single or multiple constraints and the realistic lifetime prediction using robust physical modelling is a ubiquitous questioning in most of the electronic industries.
\r\n\tThis book will target to review both the state-of-the-art and new researches on Polyimide for Electronic and Electrical Engineering Applications. It will present interdisciplinary chapters on the state of knowledge of each topic under consideration through a combination of overviews and original unpublished research. Chapter proposals related to one of the following topics and their keywords (but not only restricted to them) are very welcome to be submitted for this book publication project:
\r\n\t• General Considerations and Technological Processes of Polyimide for Electronics and Electrical Systems
\r\n\tProcessability, Photosensitive and non-photosensitive polyimide, Curing temperature,
\r\n\tSpin-coating, Dip-coating, Extruded enameled wires, Other casting methods
\r\n\t• Polyimide in Microelectronic Applications
\r\n\tDielectric properties, Intermetal layer, Ultra Large-Scale Integration (ULSI), Low-k dielectrics, Fluorinated polyimide, Nanoporous polyimide, Flexible substrates, Thin film transistors, LCD devices, sensors and actuators (gas, humidity, pressure, tactile…)
\r\n\t• Polyimide in Medium and High Voltage Applications
\r\n\tElectrical insulation properties (conduction, breakdown), Digital isolators, Power electronics and devices, Power modules, Power integration, Passivation, Packaging, High voltage power systems, Enameled wires for fed-inverter rotating machine
Scheduling is essentially the short-term execution plan of a production planning model. Production scheduling consists of the activities performed in a manufacturing company in order to manage and control the execution of a production process. A schedule is an assignment problem that describes into details (in terms of minutes or seconds) which activities must be performed and how the factory’s resources should be utilized to satisfy the plan. Detailed scheduling is essentially the problem of allocating machines to competing jobs over time, subject to the constraints. Each work center can process one job at a time and each machine can handle at most one task at a time. A scheduling problem, typically, assumes a fixed number of jobs and each job has its own parameters (i.e., tasks, the necessary sequential constraints, the time estimates for each operation and the required resources, no cancellations). All scheduling approaches require some estimate of how long it takes to perform the work. Scheduling affects, and is affected by, the shop floor organization. All scheduling changes can be projected over time enabling the identification and analysis of starting time, completion times, idle time of resources, lateness, etc….
A right scheduling plan can drive the forecast to anticipate completion date for each released part and to provide data for deciding what to work on next. Questions about “Can we do it?” and/or “How are we doing?” presume the existence of approaches for optimisation. The aim of a scheduling study is, in general, to perform the tasks in order to comply with priority rules and to respond to strategy. An optimal short-term production planning model aims at gaining time and saving opportunities. It starts from the execution orders and it tries to allocate, in the best possible way, the production of the different items to the facilities. A good schedule starts from planning and springs from respecting resource conflicts, managing the release of jobs to a shop and optimizing completion time of all jobs. It defines the starting time of each task and determines whatever and how delivery promises can be met. The minimization of one or more objectives has to be accomplished (e.g., the number of jobs that are shipped late, the minimization set up costs, the maximum completion time of jobs, maximization of throughput, etc.). Criteria could be ranked from applying simple rules to determine which job has to be processed next at which work-centre (i.e., dispatching) or to the use of advanced optimizing methods that try to maximize the performance of the given environment. Fortunately many of these objectives are mutually supportive (e.g., reducing manufacturing lead time reduces work in process and increases probability to meeting due dates). To identify the exact sequence among a plethora of possible combinations, the final schedule needs to apply rules in order to quantify urgency of each order (e.g., assigned order’s due date - defined as global exploited strategy; amount of processing that each order requires - generally the basis of a local visibility strategy). It’s up to operations management to optimize the use of limited resources. Rules combined into heuristic[1] - approaches and, more in general, in upper level multi-objective methodologies (i.e., meta-heuristics[1] -), become the only methods for scheduling when dimension and/or complexity of the problem is outstanding [1]. In the past few years, metaheuristics have received much attention from the hard optimization community as a powerful tool, since they have been demonstrating very promising results from experimentation and practices in many engineering areas. Therefore, many recent researches on scheduling problems focused on these techniques. Mathematical analyses of metaheuristics have been presented in literature [2, 3].
This research examines the main characteristics of the most promising meta-heuristic approaches for the general process of a Job Shop Scheduling Problems (i.e., JSSP). Being a NP complete and highly constrained problem, the resolution of the JSSP is recognized as a key point for the factory optimization process [4]. The chapter examines the soundness and key contributions of the 7 meta-heuristics (i.e., Genetics Approaches, Ants Colony Optimization, Bees Algorithm, Electromagnetic Like Algorithm, Simulating Annealing, Tabu Search and Neural Networks), those that improved the production scheduling vision. It reviews their accomplishments and it discusses the perspectives of each meta approach. The work represents a practitioner guide to the implementation of these meta-heuristics in scheduling job shop processes. It focuses on the logic, the parameters, representation schemata and operators they need.
The two key problems in production scheduling are „priorities“ and „capacity“. Wight (1974) described scheduling as „establishing the timing for performing a task“ and observes that, in manufacturing firms, there are multiple types of scheduling, including the detailed scheduling of a shop order that shows when each operation must start and be completed [5]. Baker (1974) defined scheduling as „a plan than usually tells us when things are supposed to happen“ [6]. Cox et al. (1992) defined detailed scheduling as „the actual assignment of starting and/or completion dates to operations or groups of operations to show when these must be done if the manufacturing order is to be completed on time“[7]. Pinedo (1995) listed a number of important surveys on production scheduling [8]. For Hopp and Spearman (1996) „scheduling is the allocation of shared resources over time to competing activities“ [9]. Makowitz and Wein (2001) classified production scheduling problems based on attributes: the presence of setups, the presence of due dates, the type of products.
Practical scheduling problems, although more highly constrained, are high difficult to solve due to the number and variety of jobs, tasks and potentially conflicting goals. Recently, a lot of Advanced Production Scheduling tools arose into the market (e.g., Aspen PlantTM Scheduler family, Asprova, R2T – Resourse To Time, DS APS – DemandSolutions APS, DMS – Dynafact Manufacturing System, i68Group, ICRON-APS, JobPack, iFRP, Infor SCM, SchedulePro, Optiflow-Le, Production One APS, MQM – Machine Queue Management, MOM4, JDA software, Rob-ex, Schedlyzer, OMP Plus, MLS and MLP, Oracle Advanced Scheduling, Ortec Schedule, ORTEMS Productionscheduler, Outperform, AIMMS, Planet Together, Preactor, Quintiq, FactoryTalk Scheduler, SAP APO-PP/DS, and others). Each of these automatically reports graphs. Their goal is to drive the scheduling for assigned manufacturing processes. They implement rules and optimise an isolated sub-problem but none of the them will optimise a multi stage resource assignment and sequencing problem.
In a Job Shop (i.e., JS) problem a classic and most general factory environment, different tasks or operations must be performed to complete a job [10]; moreover, priorities and capacity problems are faced for different jobs, multiple tasks and different routes. In this contest, each job has its own individual flow pattern through assigned machines, each machine can process only one operation at a time and each operation can be processed by only one machine at a time. The purpose of the procedure is to obtain a schedule which aims to complete all jobs and, at the same time, to minimize (or maximize) the objective function. Mathematically, the JS Scheduling Problem (i.e., JSSP) can be characterized as a combinatorial optimization problem. It has been generally shown to be NP-hard[1] - belonging to the most intractable problems considered [4, 11, 12]. This means that the computation effort may grow too fast and there are not universal methods making it possible to solve all the cases effectively. Just to understand what the technical term means, consider the single-machine sequencing problem with three jobs. How many ways of sequencing three jobs do exist? Only one of the three jobs could be in the first position, which leaves two candidates for the second position and only one for the last position. Therefore the no. of permutations is 3!. Thus, if we want to optimize, we need to consider six alternatives. This means that as the no. of jobs to be sequenced becomes larger (i.e., n>80), the no. of possible sequences become quite ominous and an exponential function dominates the amount of time required to find the optimal solution [13]. Scheduling, however, performs the definition of the optimal sequence of n jobs in m machines. If a set of n jobs is to be scheduled on m machines, there are (n!)m possible ways to schedule the job.
It has to undergo a discrete number of operations (i.e., tasks) on different resources (i.e., machines). Each product has a fixed route defined in the planning phase and following processing requirements (i.e., precedence constraints). Other constraints, e.g. zoning which binds the assignment of task to fixed resource, are also taken into consideration. Each machine can process only one operation at a time with no interruptions (pre-emption). The schedule we must derive aims to complete all jobs with minimization (maximization) of an objective function on the given production plant.
Let:
JSSP, marked as
To accommodate extreme variability in different parts of a job shop, schedulers separate workloads in each work-centres rather than aggregating them [14]. Of more than 100 different rules proposed by researchers and applied by practitioners exist, some have become common in Operations Management systems: First come- First served, Shortest Processing Time, Earliest Due Date, Slack Time Remaining, Slack Time Remaining For each Operation, Critical Ratio, Operation Due Date, etc. [15]. Besides these, Makespan is often the performance feature in the study of resource allocation [16]. Makespan represents the time elapsed from the start of the first task to the end of the last task in schedule. The minimisation of makespan arranges tasks in order to level the differences between the completion time of each work phase. It tries to smooth picks in work-centre occupancy to obtain batching in load assignment per time. Although direct time constraints, such as minimization of processing time or earliest due date, are sufficient to optimize industrial scheduling problems, for the reasons as above the minimization of the makespan is preferable for general/global optimization performances because it enhances the overall efficiency in shop floor and reduces manufacturing lead time variability [17].
Thus, in JSSP optimization variant of
where t represent time (i.e. iteration steps)
and
The possible representation of a JS problem could be done through a Gantt chart or through a Network representation.
Gantt (1916) created innovative charts for visualizing planned and actual production [18]. According to Cox et al. (1992), a Gantt chart is „the earliest and best known type of control chart especially designed to show graphically the relationship between planned performance and actual performance” [19]. Gantt designed his charts so that foremen or other supervisors could quickly know whether production was on schedule, ahead of schedule or behind schedule. A Gantt chart, or bar chart as it is usually named, measures activities by the amount of time needed to complete them and use the space on the chart to represent the amount of the activity that should have been done in that time [7].
A Network representation was first introduced by Roy and Sussman [20]. The representation is based on “disjunctive graph model” [21]. This representation starts from the concept that a feasible and optimal solution of JSP can originate from a permutation of task’s order. Tasks are defined in a network representation through a probabilistic model, observing the precedence constraints, characterized in a machine occupation matrix M and considering the processing time of each tasks, defined in a time occupation matrix T.
JS processes are mathematically described as disjunctive graph G = (V, C, E). The descriptions and notations as follow are due to Adams et. al. [22], where:
V is a set of nodes representing tasks of jobs. Two additional dummy tasks are to be considered: a source(0) node and a sink(*) node which stand respectively for the Source (S) task τ0= 0, necessary to specify which job will be scheduled first, and an end fixed sink where schedule ends (T) τ*= 0;
C is the set of conjunctive arcs or direct arcs that connect two consecutive tasks belonging to the same job chain. These represent technological sequences of machines for each job;
E=
Each job-tasks pair (i,j) is to be processed on a specified machine M(i,j) for T(i,j) time units, so each node of graph is weighted with j operation’s processing time. In this representation all nodes are weighted with exception of source and sink node. This procedure makes always available feasible schedules which don’t violate hard constraints[1] -. A graph representation of a simple instance of JSP, consisting of 9 operations partitioned into 3 jobs and 3 machines, is presented in fig. 1. Here the nodes correspond to operations numbered with consecutive ordinal values adding two fictitious additional ones:S = “source node” and T = “sink node”. The processing time for each operation is the weighted value τij attached to the corresponding node,
Let sv be the starting time of an operation to a node v. By using the disjunctive graph notation, the JSPP can be formulated as a mathematical programming model as follows:
Minimize s* subject to:
Disjunctive graph representation. There are disjunctive arcs between every pair of tasks that has to be processed on the same machine (dashed lines) and conjunctive arcs between every pair of tasks that are in the same job (dotted lines). Omitting processing time, the problem specification is O={oij, (i,j)∈{1,2,3}2}, J={Ji={oij}, (i,j)=1,2,3}, M={Mj={oij}, (i,j)=1,2,3}. Job notation is used.
s* is equal to the completion time of the last operation of the schedule, which is therefore equal to Cmax. The first inequality ensures that when there is a conjunctive arc from a node v to a node w, w must wait of least τv time after v is started, so that the predefined technological constraints about sequence of machines for each job is not violated. The second condition ensures time to start continuities. The third condition affirms that, when there is a disjunctive arc between a node v and a node w, one has to select either v to be processed prior to w (and w waits for at least τv time period) or the other way around, this avoids overlap in time due to contemporaneous operations on the same machine.
In order to obtain a scheduling solution and to evaluate makespan, we have to collect all feasible permutations of tasks to transform the undirected arcs in directed ones in such a way that there are no cycles.
The total number of nodes,
The number of arcs defines the possible combination paths. Each path from source to sink is a candidate solution for JSSP. The routing graph is reported in figure 2:
Problem routing representation.
A logic has to be implemented in order to translate the scheduling problem into an algorithm structure. Academic researches on scheduling problems have produced countless papers [23]. Scheduling has been faced from many perspectives, using formulations and tools of various disciplines such as control theory, physical science and artificial intelligence systems [24]. Criteria for optimization could be ranked from applying simple priority rules to determine which job has to be processed next at the work-centres (i.e., dispatching) to the use of advanced optimizing methods that try to maximize the performance of the given environment [25]. Their way to solution is generally approximate – heuristics – but it constitutes promising alternatives to the exact methods and becomes the only one possible when dimension and/or complexity of the problem is outstanding [26].
Guidelines in using heuristics in combinatorial optimization can be found in Hertz (2003) [27]. A classification of heuristic methods was proposed by Zanakis et al. (1989) [28]. Heuristics are generally classified into constructive heuristics and improvement heuristics. The first ones are focused on producing a solution based on an initial proposal, the goal is to decrease the solution until all the jobs are assigned to a machine, not considering the size of the problem [29]. The second ones are iterative algorithms which explore solutions by moving step by step form one solution to another. The method starts with an arbitrary solution and transits from one solution to another according to a series of basic modifications defined on case by case basis [30].
Relatively simple rules in guiding heuristic, with exploitation and exploration, are capable to produce better quality solutions than other algorithms from the literature for some classes of instances. These variants originate the class of meta-heuristic approaches [31]. The meta-heuristics[1] -, and in general the heuristics, do not ensure optimal results but they usually tend to work well [32]. The purpose of the paper is to illustrate the most promising optimization methods for the JSSP.
As optimization techniques, metaheuristics are stochastic algorithms aiming to solve a broad range of hard optimization problems, for which one does not know more effective traditional methods. Often inspired by analogies with reality, such as physics science, Simulated Annealing [33] and Electromagnetic like Methods [34], biology (Genetic Algorithms [35], Tabu Search [36]) and ethnology (Ant Colony [37,], Bees Algorithm [38]), human science (Neural Networks [39]), they are generally of discrete origin but can be adapted to the other types of problems.
The methodology of a GAs - based on the evolutionary strategy- trasforms a population (set) of individual objects, each with an associated fitness value, into a new generation of the population occurring genetic operations such as crossover (sexual recombination) and mutation (fig. 3).
The theory of evolutionary computing was formalized by Holland in 1975 [40]. GAs are stochastic search procedures for combinatorial optimization problems based on Darwinian principle of natural reproduction, survival and environment’s adaptability [41]. The theory of evolution is biologically explained, the individuals with a stronger fitness are considered better able to survive.. Cells, with one or more strings of DNA (i.e., a chromosome), make up an individual. The gene (i.e., a bit of chromosome located into its particular locus) is, responsible for encoding traits (i.e., alleles). Physical manifestations are raised into genotype (i.e., disposition of genes). Each genotype has is physical manifestation into phenotype. According to these parameters is possible to define a fitness value. Combining individuals through a crossover (i.e., recombination of genetic characteristics of parents) across the sexual reproduction, the chromosomal inheritance process performs to offspring. In each epoch a stochastic mutation procedure occurs. The implemented algorithm is able to simulate the natural process of evolution, coupling solution of scheduling route in order to determinate an optimal tasks assignment. Generally, GA has different basic component: representation, initial population, evaluation function, the reproduction selection scheme, genetic operators (mutation and crossover) and stopping criteria. Central to success of any GA is the suitability of its representation to the problem at hand [42]. This is the encoding from the solution of the problem domain to the genetic representation.
During the last decades, different representation’s schemata for JS have been proposed, such as permutation with repetition. It uses sequence of repeated jobs identifier (e.g., its corresponding cardinal number) to represent solutions [43]. According to the instance in issue, each of the N jobs identifiers will be repeated M times, once for each task. The first time that job’s identifier, reading from left to right, will appear means the first task of that job. In this way, precedence constraints are satisfied. The redundancy is the most common caveat of this representation. A proposal of permutation with repetition applying a Generalized Order crossover (GOX) with band |2 3 1 1| of parent 1 moves from PARENT1 [3 2 3 1 1 1 3 2 2] and PARENT2 [2 3 2 1 3 3 2 1 1] to CHILD1 [2 3 1 1 3 2 3 2 1] and CHILD2 [3 2 1 3 2 1 1 3 2].
The Genetic Algorithms (GAs) model; 3a. the pseudo-code of a GA; 3b. the flow chart of a general GA.
A mutation operator is applied changing the genes into the same genotype (in order to generate only feasible solutions, i.e., without the rejection procedure). Mutation allows to diversify the search over a broader solution domain and it is needed when there is low level of crossover. Among solutions, the allocation with favourable fitness will have higher probability to be selected through the selection mechanisms.
Another important issue for the GA is the selection mechanism (e.g., Tournament Selection procedure and Roulette Wheel as commonly used [44] - their performances are quite similar attending in the convergence time). The tournament selection procedure is based on analogy with competition field, between the genotypes in tournament, the individual which will win (e.g., the one with the best fitness value) is placed in the mating pool. Likewise, in the roulette wheel selection mechanism each individual of population has a selection’s likelihood proportional to its objective score (in analogy with the real roulette item) and with a probability equal to one of a ball in a roulette, one of the solutions is chosen.
It is very important, for the GAs success, to select the correct ratio between crossover and mutation, because the first one allows to allows to diversify a search field, while a mutation to modify a solution.
If we are on a pic-nic and peer into our cake bitten by a colony of ants, moving in a tidy way and caring on a lay-out that is the optimal one in view of stumbling-blocks and length, we discover how remarkable is nature and we find its evolution as the inspiring source for investigations on intelligence operation scheduling techniques [45]. Natural ants are capable to establish the shortest route path from their colony to feeding sources, relying on the phenomena of swarm intelligence for survival. They make decisions that seemingly require an high degree of co-operation, smelling and following a chemical substance (i.e. pheromone[1] -) laid on the ground and proportional to goodness load that they carry on (i.e. in a scheduling approach, the goodness of the objective function, reported to makespan in this applicative case).
The same behaviour of natural ants can be overcome in an artificial system with an artificial communication strategy regard as a direct metaphoric representation of natural evolution. The essential idea of an ACO model is that „good solutions are not the result of a sporadic good approach to the problem but the incremental output of good partial solutions item. Artificial ants are quite different by their natural progenitors, maintaining a memory of the step before the last one [37]. Computationally, ACO [46] are population based approach built on stochastic solution construction procedures with a retroactive control improvement, that build solution route with a probabilistic approach and through a suitable selection procedure by taking into account: (a) heuristic information on the problem instance being solved; (b) (mat-made) pheromone amount, different from ant to ant, which stores up and evaporates dynamically at run-time to reflect the agents’ acquired search training and elapsed time factor.
The initial schedule is constructed by taking into account heuristic information, initial pheromone setting and, if several routes are applicable, a self-created selection procedure chooses the task to process. The same process is followed during the whole run time. The probabilistic approach focused on pheromone. Path’s attractive raises with path choice and probability increases with the number of times that the same path was chosen before [47]. At the same time, the employment of heuristic information can guide the ants towards the most promising solutions and additionally, the use of an agent’s colony can give the algorithm: (i) Robustness on a fixed solution; (ii) Flexibility between different paths.
The approach focuses on co-operative ant colony food retrieval applied to scheduling routing problems. Colorni et al, basing on studies of Dorigo et al. [48], were the first to apply Ant System (AS) to job scheduling problem [49] and dubbed this approach as Ant Colony Optimization (ACO). They iteratively create route, adding components to partial solution, by taking into account heuristic information on the problem instance being solved (i.e. visibility) and “artificial” pheromone trials (with its storing and evaporation criteria). Across the representation of scheduling problem like acyclic graph, see fig. 2, the ant’s rooting from source to food is assimilated to the scheduling sequence. Think at ants as agents, nodes like tasks and arcs as the release of production order. According to constraints, the ants perform a path from the row material warehouse to the final products one.
The Ant Colony Optimization (ACO) model; 4a. the pseudo-code of an ACO algorithm; 4b. the flow chart of a general ACO procedure.
Constraints are introduced hanging from jobs and resources. Fitness is introduced to translate how good the explored route was. Artificial ants live in a computer realized world. They have an overview of the problem instance they are going to solve across a visibility factor. In the Job Shop side of ACO implementation the visibility has chosen tied with the run time of the task (Eq. 7). The information was about the inquired task’s (i.e., j) completion time Ctimej and idle time Itimej from the previous position (i.e., i):
The colony is composed of a fixed number of agents ant=1,…, n. A probability is associated to each feasible movement (Sant(t)) and a selection procedure (generally based on RWS or Tournament procedure) is applied.
Where:
For each cycle the agents of the colony are going out of source in search of food. When all colony agents have constructed a complete path, i.e. the sequence of feasible order of visited nodes, a pheromone update rule is applied (Eq. 9):
Besides ants’ activity, pheromone trail evaporation has been included trough a coefficient representing pheromone vanishing during elapsing time. These parameters imitate the natural world decreasing of pheromone trail intensity over time. It implements a useful form of forgetting. It has been considered a simple decay coefficient (i.e.,
The laid pheromone on the inquired path is evaluated taking into consideration how many agents chose that path and how was the objective value of that path (Eq. 10). The weight of the solution goodness is the makespan (i.e., Lant). A constant of pheromone updating (i.e., Q), equal for all ants and user, defined according to the tuning of the algorithm, is introduced as quantity of pheromone per unit of time (Eq. 11). The algorithm works as follow. It is computed the makespan value for each agent of the colony (Lant(0)), following visibility and pheromone defined initially by the user (τij(0)) equal for all connections. It is evaluated and laid, according to the disjunctive graph representation of the instance in issue, the amount of pheromone on each arc (evaporation coefficient is applied to design the environment at the next step).
Visibility and updated pheromone trail fixes the probability (i.e., the fitness values) of each node (i.e., task) at each iteration; for each cycle, it is evaluated the output of the objective function (Lant(t)). An objective function value is optimised accordingly to partial good solution. In this improvement, relative importance is given to the parameters α and β. Good elements for choosing these two parameters are:
A colony of bees exploits, in multiple directions simultaneously, food sources in the form of antera with plentiful amounts of nectar or pollen. They are able to cover kilometric distances for good foraging fields [50]. Flower paths are covered based on a stigmergic approach – more nectar places should be visited by more bees [51].
The foraging strategies in colonies of bees starts by scout bees – a percentage of beehive population. They wave randomly from one patch to another. Returning at the hive, those scout bees deposit their nectar or polled and start a recruiting mechanism rated above a certain quality threshold on nectar stored [52]. The recruiting mechanism is properly a launching into a wild dance over the honeycomb. This natural process is known as waggle dance” [53]. Bees, stirring up for discovery, flutter in a number from one to one hundred circuits with a waving and returning phase. The waving phase contains information about direction and distance of flower patches. Waving phases in ascending order on vertical honeycomb suggest flower patches on straightforward line with sunbeams. This information is passed using a kind of dance, that is possible to be developed on right or on left. So through this dance, it is possible to understand the distance from the flower, the presence of nectar and the sunbeam side to choose [54].
The waggle dance is used as a guide or a map to evaluate merits of explored different patches and to exploit better solutions. After waggle dancing on the dance floor, the dancer (i.e. the scout bee) goes back to the flower patch with follower bees that were waiting inside the hive. A squadron moves forward into the patches. More follower bees are sent to more promising patches, while harvest paths are explored but they are not carried out in the long term. A swarm intelligent approach is constituted [55]. This allows the colony to gather food quickly and efficiently with a recursive recruiting mechanism [56].
The Bees Algorithm (i.e., BA) is a population-based search; it is inspired to this natural process [38]. In its basic version, the algorithm performs a kind of neighbourhood search combined with random search. Advanced mechanisms could be guided by genetics [57] or taboo operators [58]. The standard Bees Algorithm first developed in Pham and Karaboga in 2006 [59, 60] requires a set of parameters: no. of scout bees (n), no. of sites selected out of n visited sites (m), no. of best sites out of m selected sites (e), no. of bees recruited for the best e sites (nep), no. of bees recruited for the other m-e selected sites (nsp), initial size of patches (ngh). The standard BA starts with random search.
The honey bees‘ effective foraging strategy can be applied in operation management problems such as JSSP. For each solution, a complete schedule of operations in JSP is produced. The makespan of the solution is analogous to the profitability of the food source in terms of distance and sweetness of the nectar. Bees, n scouts, explore patches, m sites - initially a scout bee for each path could be set, over total ways, ngh, accordingly to the disjunctive graph of fig. 2, randomly at the first stage, choosing the shorter makespan and the higher profitability of the solution path after the first iteration.
The Bees Algorithm model; 6a. the BA pseudo code; 6b. the flow chart of a general BA procedure.
Together with scouting, this differential recruitment is the key operation of the BA. Once a feasible solution is found, each bee will return to the hive to perform a waggle dance. The output of the waggle dance will be represented by a list of “elite solutions”, e best selected sites, from which recruited bees, nep, are chosen for exploration from the population into the hive. Researches of patches are conducted, other nsp bees, in the neighbourhood of the selected sites, m-e sites. System maintains, step repetition: imax, where each bee of the colony of bees will traverse a potential solution. Flower patches, e sites, with better fitness (makespan) will have a higher probability for “elite solutions”, promoting the exploitation to an optimal solution.
The Electromagnetic Like Algorithm is a population based meta-heuristics proposed by Birbil and Fang [61] to tackle with combinatorial optimisation problems. Algorithm is based on the natural law of attraction and repulsion between charges (Coulomb’s law) [62]. EM simulates electromagnetic interaction [63]. The algorithm evaluates fitness of solutions considering charge of particles. Each particle represents a solution. Two points into the space had different charges in relation to what electromagnetic field acts on them [64]. An electrostatic force, in repulsion or attraction, manifests between two points charges. The electrostatic force is directly proportional to the magnitudes of each charge and inversely proportional to the square of the distance between the charges. The fixed charge at time iteration (t) of particle i is shown as follows:
Where t represents the iteration step, qi\n\t\t\t\t\t(t) is the charge of particle i at iteration t, f(xi,,t), f(xbest,t), and f(xk,t) denote the objective value of particle i, the best solution, and particle k from m particles at time t; finally, n is the dimension of search space.The charge of each point i, qi(t), determines point’s power of attraction or repulsion. Points (xi) could be evaluated as a task into the graph representation (fig. 2).
The particles move along with total force and so diversified solutions are generated. The following formulation is the resultant force of particle i:
The following notes described an adapted version of EM for JSSP. According to this application, the initial population is obtained by choosing randomly from the list or pending tasks, as for the feasibility of solution, particles’ path. The generic pseudo-code for the EM is reported in figure 6. Each particle is initially located into a source node (see disjunctive graph of figure 2). Particle is uniquely defined by a charge and a location into the node’s space. Particle’s position in each node is defined in a multigrid discrete set. While moving, particle jumps in a node based on its attraction force, defined in module and direction and way. If the force from starting line to arrival is in relation of positive inequality, the particles will be located in a plane position in linear dependence with force intensity. A selection mechanism could be set in order to decide where particle is directed, based on node force intensity. Force is therefore the resultant of particles acting in node. A solution for the JS is obtained only after a complete path from the source to the sink and the resulting force is updated according to the normalized makespan of different solutions.
The Electromagnetic like Method; 6a. the EM pseudo code; 6b. the flow chart of a general EM procedure.
The simulated annealing was presented by Scott Kirkpatrick et al. in 1983 [65] and by Vlado Černý in 1985 [66]. This optimization method is based on works of Metropolis et al., [67] which allows describing the behaviour of a system in thermodynamic equilibrium at a certain temperature. It is a generic probabilistic metaheuristic used to find a good approximation to the global optimum of a given objective function. Mostly it is used with discrete problems such as the main part of the operations management problems.
Name and inspiration come from annealing in metallurgy, a technique that, through the heating and a controlled process of cooling, can increase the dimensions of the crystals inside the fuse piece and can reduce the defects inside the crystals structure. The technique deals with the minimization of the global energy E inside the material, using a control parameter called temperature, to evaluate the probability of accepting an uphill move inside the crystals structure. The procedure starts with an initial level of temperature T and a new random solution is generated at each iteration, if this solution improves the objective function, i.e., the E of the system is lower than the previous one. Another technique to evaluate the improvement of the system is to accept the new random solution with a likelihood according to a probability exp(-ΔE), where ΔE is the variation of the objective function. Afterwards a new iteration of the procedure is implemented.
As follows there is the pseudo-code of a general simulated annealing procedure:
The Simulated Annealing model; 7a. the SA pseudo code; 7b. the flow chart of a general SA procedure
For the scheduling issues, the application of the SA techniques requires the solutions fitness generated by each iteration, that is generally associated to the cost of a specific scheduling solution; the cost is represented by the temperature that is reduced for each iteration [68]. The acceptance probability can be measured as following:
Another facet to be analysed is the stopping criteria, which can be fixed as the total number of iterations of the procedure to be computed.
Tabu search (Glover, 1986) is an iterative search approach characterised by the use of a flexible memory [69]. The process with which tabu search overcomes local optimality is based on the evaluation function that chooses the highest evaluation solution at each iteration. The evaluation function selects the move, in the neighbourhood of the current solution, that produces the most improvement or the least deterioration in the objective function. Since, movement are accepted based on a probability function, a tabu list is employed to store characteristics of accepted moves so to classify them as taboo (i.e., to be avoided) in the later iteration. This is used to dodge cycling movements. A strategy called forbidding is employed to control and update the tabu list. This method was formalized by Glover [69]. An algorithm based on tabu search requires some elements: (i) the move, (ii) the neighbourhood, (iii) an initial solution, (iv) a search strategy, (v) a memory, (vi) an objective function and (vii) a stop criterion. The of TS is based on the definition of a first feasible solution S, which is stored as the current seed and the best solution, at each iteration, after the set of the neighbours is selected between the possible solutions deriving from the application of a movement. The value of the objective function is evaluated for all the possible movements, and the best one is chosen. The new solution is accepted even if its value is worse than the previous one, and the movement is recorded in a list, named taboo list.
For the problem of the scheduling in the job shops, generally a row of assignments of n jobs to m machines is randomly generated and the cost associated is calculated to define the fitness of the solution [70]. Some rules of movements can be defined as the crossover of some jobs to different machines and so on, defining new solutions and generating new values of the objective functions. The best solution between the new solutions is chosen and the movement is recorded in a specific file named taboo list. The stopping criterion can be defined in many ways, but simplest way is to define a maximum number of iterations [71].
In figure 8 are reported the pseudo-code and the flowchart for the application of TS to JSSP.
The Tabu Search approach; 8a. the TS pseudo code; 8b. the flow chart of a general TS procedure.
Neural networks are a technique based on models of biological brain structure. Artificial Neural Networks (NN), firstly developed by McCulloch and Pitts in 1943, are a mathematical model which wants to reproduce the learning process of human brain [72]. They are used to simulate and analyse complex systems starting from known input/output examples. An algorithm processes data through its interconnected network of processing units compared to neurons. Consider the Neural Network procedure to be a “black box”. For any particular set of inputs (particular scheduling instance), the black box will give a set of outputs that are suggested actions to solve the problem, even though output cannot be generated by a known mathematical function. NNs are an adaptive system, constituted by several artificial neurons interconnected to form a complex network, those change their structure depending on internal or external information. In other words, this model is not programmed to solve a problem but it learns how to do that, by performing a training (or learning) process which uses a record of examples. This data record, called training set, is constituted by inputs with their corresponding outputs. This process reproduces almost exactly the behaviour of human brain that learns from previous experience.
The basic architecture of a neural network, starting from the taxonomy of the problems faceable with NNs, consists of three layers of neurons: the input layer, which receives the signal from the external environment and is constituted by a number of neurons equal to the number of input variables of the problem; the hidden layer (one or more depending on the complexity of the problem), which processes data coming from the input layer; and the output layer, which gives the results of the system and is constituted by as many neurons as the output variables of the system.
The error of NNs is set according to a testing phase (to confirm the actual predictive power of the network while adjusting the weights of links). After having built a training set of examples coming from historical data and having chosen the kind of architecture to use (among feed-forward networks, recurrent networks), the most important step of the implementation of NNs is the learning process. Through the training, the network can infer the relation between input and output defining the “strength” (weight) of connections between single neurons. This means that, from a very large number of extremely simple processing units (neurons), each of them performing a weighted sum of its inputs and then firing a binary signal if the total input exceeds a certain level (activation threshold), the network manages to perform extremely complex tasks. It is important to note that different categories of learning algorithms exists: (i) supervised learning, with which the network learns the connection between input and output thank to known examples coming from historical data; (ii) unsupervised learning, in which only input values are known and similar stimulations activate close neurons otherwise different stimulations activate distant neurons; and (iii) reinforcement learning, which is a retro-activated algorithm capable to define new values of the connection weights starting from the observation of the changes in the environment. Supervised learning by back error propagation (BEP) algorithm has become the most popular method of training NNs. Application of BEP in Neural Network for production scheduling is in: Dagli et al. (1991) [73], Cedimoglu (1993) [74], Sim et al. (1994) [75], Kim et al. (1995) [76].
The mostly NNs architectures used for JSSP are: searching network (Hopfield net) and error correction network (Multi Layer Perceptron). The Hopfield Network (a content addressable memory systems with weighted threshold nodes) dominates, however, neural network based scheduling systems [77]. They are the only structure that reaches any adequate result with benchmark problems [78]. It is also the best NN method for other machine scheduling problems [79]. In Storer et al. (1995) [80] this technique was combined with several iterated local search algorithms among which space genetic algorithms clearly outperform other implementations [81]. The technique’s objective is to minimize the energy function E that corresponds to the makespan of the schedule. The values of the function are determined by the precedence and resource constraints which violation increases a penalty value. The Multi Layer Perceptron (i.e., MLP) consists in a black box of several layers allowing inputs to be added together, strengthened, stopped, non-linearized [82], and so on [83]. The black box has a great no. of knobs on the outside which can be filled with to adjust the output. For the given input problem, the training (network data set is used to adjust the weights on the neural network) is set as optimum target. Training an MLP is NP-complete in general.
In figure 9 it is possible to see the pseudo-code and the flow chart for the neural networks.
The NNs model; 9a. the implemented NNs pseudo code; 9b. the flow chart of generic NNs.
In this chapter, it was faced the most intricate problem (i.e., Job Shop) in order to explain approaches for scheduling in manufacturing. The JSP is one of the most formidable issues in the domain of optimization and operational research. Many methods were proposed, but only application of approximate methods (metaheuristics) allowed to efficiently solve large scheduling instances. Most of the best performing metaheuristics for JSSP were described and illustrated.
The likelihood of solving JSP can be greatly improved by finding an appropriate problem representation in computer domain. The acyclic graph representation is a quite good way to model alternatives in scheduling. How to fit approaches with problem domain (industrial manufacturing system) is generally a case in issue. Approaches are obviously affected by data and the results are subject to tuning of algorithm’s parameters. A common rule is: less parameters generate more stable performances but local optimum solutions. Moreover, the problem has to be concisely encoded such that the job sequence will respect zoning and sequence constraints. All the proposed approaches use probabilistic transition rules and fitness information function of payoff (i.e., the objective function).
ACO and BE manifest common performances in JSSP. They do not need a coding system. This factor makes the approaches more reactive to the particular problem instance in issue. Notwithstanding, too many parameters have to be controlled in order to assure diversification of search. GAs surpasses their cousins in the request for robustness. The matching between genotype and phenotype across the schemata must be investigated in GAs in order to obtain promising results. The difficult of GA is to translate a correct phenotype from a starting genotype. A right balancing between crossover and mutation effect can control the performance of this algorithm. The EM approach is generally affected by local stability that avoid global exploration and global performance. It is, moreover, subject to infeasibility in solutions because of its way to approach at the problem. SA and TS, as quite simpler approaches, dominate the panorama of metaheuristics proposal for JS scheduling. They manifest simplicity in implementation and reduction in computation effort but suffer in local optimum falls. These approaches are generally used to improve performances of previous methodologies and they enhance their initial score. The influence of initial solutions on the results, for overall approaches, is marked. Performances of NNs are generally affected by the learning process, over fitting. Too much data slow down the learning process without improving in optimal solution. Neural Network is, moreover, affected by difficulties in including job constraints with network representation. The activating signal needs to be subordinated to the constraints analysis.
Based on authors experience and reported paragraphs, it is difficult to definitively choose any of those techniques as outstanding in comparison with the others. Measurement of output and cost-justification (computational time and complexity) are vital to making good decision about which approach has to be implemented. They are vital for a good scheduling in operations management. In many cases there are not enough data to compare – benchmark instances, as from literature for scheduling could be useful - those methods thoroughly. In most cases it is evident that the efficiency of a given technique is problem dependent. It is possible that the parameters may be set in such way that the results of the algorithms are excellent for those benchmark problems but would be inferior for others. Thus, comparison of methods creates many problems and usually leads to the conclusion that there is no the only best technique. There is, however, a group of several methods that dominates, both in terms of quality of solutions and computational time. But this definition is case dependent.
What is important to notice here is: performance is usually not improved by algorithms for scheduling; it is improved by supporting the human scheduler and creating a direct (visual) link between scheduling actions and performances. It is reasonable to expect that humans will intervene in any schedule. Humans are smarter and more adaptable than computers. Even if users don’t intervene, other external changes will happen that impact the schedule. Contingent maintenance plan and product quality may affect performance of scheduling. An algorithmic approach could be obviously helpful but it has to be used as a computerised support to the scheduling decision - evaluation of large amount of paths - where computational tractability is high. So it makes sense to see what optimal configuration is before committing to the final answer.
Improving the nutritional values and stability of quality is a very important parameter in food product quality assurance for a healthy life of human beings. Consumers are looking for fresh and good characteristics in their food with nutrient content and high sensorial quality. Now, consumers are more aware of the processing techniques used in the processing of their food, and they prefer natural products free of additives and chemicals. Therefore, there is a need for alternative technologies for food processing. Recently, various modern thermal and nonthermal technologies such as pulsed light, pulsed electric field, high and low hydrostatic pressure, microwave, ohmic heating, freezing, pasteurizing, ionizing radiation, etc. have been used to improve the physicochemical characteristics, extend the shelf life of food products, and control food quality by inactivating microorganisms at sublethal or ambient temperatures. One of the nonthermal technologies that can be used also is the application of ultrasonic (high-power and low-power ultrasonic with low and high frequency); especially it has shown a negligible effect on the nutrient value of food products [1, 2]. Applications of ultrasonic technology for food processing aim to offer consumers high-quality foods. The ultrasonic is considered to be a promising and emerging technology that can be used in food processing technology and many industrial applications by regulating frequency [3]. According to sound wave ranges used, the ultrasonic can be divided into low-power high-frequency ultrasonic and high-power low-frequency ultrasonic [4]. Low-power ultrasonic with high frequency is used for nondestructive quality evaluation of physicochemical characteristics of fruit, vegetables, and food products during processing or storage. The high-power ultrasonic with low frequency is used to improve the physicochemical properties of food products and in food processing such as humidification, hydrothermal treatments, extraction, drying, freezing, and inactivation of microorganisms of food products [3]. The ultrasonic technology has been also used in the industry of food products to develop many reliable and effective processing applications of food. The most common applications of ultrasonic in the industry of food include extraction of intracellular and material cell destruction. Depending on the ultrasonic intensity, the ultrasonic is used for the deactivation or activation of enzymes, homogenization and mixing, dispersion, stabilization, crystallization and dissolution, emulsification, hydrogenation, preservation, ripening, meat tenderization, oxidation, as a solid-liquid extraction adjuvant to accelerate and to improve the extraction, and atomization and degassing of food processing [5]. The objectives of ultrasonic research are to analyze and study the phenomena of undesirable and desirable degradation resulting from the applications of ultrasonic wave treatments in foods. The processing using ultrasonic may impact the chemical composition texture of foods [6].
Generally, ultrasonic applications are environmentally friendly and offer an advantage in the selectivity, yield, and productivity, with enhanced quality, reduced physical and chemical hazards, and short processing time. Before the commercialization of some food products such as vegetables and fruit, oils and fat, cocoa-sugar and coffee, meal and flours, dairy, and meat which are complex mixtures of proteins, sugars, lipids, vitamins, aromas, fibers, antioxidants, pigments, and mineral and organic compounds have to be processed and preserved using ultrasonic applications for food meals and to extraction of food ingredients [7]. The main purpose of this chapter is to provide an overview of the basic principles and current applications of low-intensity and high-intensity ultrasonic waves as a modern nonthermal technique for food product processing technology to improve its quality.
The sound wave type is determined by its frequency. Figure 1 shows the sound spectrum which displays the various frequencies present in a sound. “Infrasound” indicates a sound wave below the human hearing range. This frequency of sound is used by submarine sonar devices and whales. The frequency of the sound for the human hearing ranges from 20Hz to 20 kHz [8, 9]. The sound signal arises from many sources, e.g., the air turbulence or gases, passage through fluids, and by the impact of solid against another solid similar or non-similar. Because the sound is a natural phenomenon of waves, it may contain only one frequency as a sine wave with pure steady state (Figure 2) or contain complex frequencies such as the noise generated by many sound sources, e.g., machines and engines. The frequency of sound (f) is sound pressure times number. The sound frequency also may be identified by the frequency of angular (ω) expressed in radians per second as shown in Eq. (1). The period (T) is the time amount for a cycle of the single [10]:
Sound spectrum.
The pure steady-state sine wave pulses.
Actually, the amplitude of the sound wave is strongly affected by the particles near the source of the sound waves, and on the contrary, the deeper particles are in the treated medium, the lower the sound wave amplitude. This reduction in sound wave amplitude at the deep is due to the attenuation produced by the treated medium. As a result, the sound amplitude versus wavelength distance is actually an exponentially sinusoid degenerate (Figure 3). The wavelength ((λ) is the distance between peaks of successive amplitude) is related to frequency (f) through the traveling wave velocity (c) as shown in Eq. (2) [11]:
Sinusoidal ultrasound wave.
Ultrasonic is a wave of sound with a frequency greater than the human hearing limit. Ultrasonic is considered an energy form generated by a longitudinal mechanical wave with one-dimensional propagation and frequency of vibration above 20,000 cycles per second (20 kHz) as shown in Figure 4. Ultrasonic waves can be categorized according to its frequency into two categories that are: (1) Low-frequency category which has frequency ranging from 20 to 1000 kHz. The applications of this category are used at high-power intensities in industrial applications, ultrasonic therapy, sonochemistry, and nanotechnology. (2) High-frequency category which has a frequency above 1 MHz and is being used at low-power intensities for nondestructive quality evaluation, imaging, and diagnostic applications [6, 12].
Ultrasonic frequencies classification.
Use of ultrasonic application provides a good way to reach higher rates for the chemical and physicochemical process, shorter processing times and pathways of reaction. Interaction mechanisms between the product material and ultrasonic waves vary as a function of the input power of the ultrasonic. The pulse of ultrasonic speed depends on the acoustic properties of the medium of treated material. The speed of sound propagation in solid materials is higher than the sound propagation speed in liquids and greater in liquids than in gases [9].
The main equipment of ultrasonic consists of a transducer, electrical power generator, and sound emitter devices. The emitter’s function is to physically send the waves of ultrasonic to the medium. There are two types of ultrasonic systems used in the industry of food products: one using the bath as a traditional method and other using the horn as the sound emitter. The horn-based system is utilized in many applications from ultrasonic application in food processing and cleaning of plant surfaces for the process of food to application of ultrasonic for welding of metals [11].
The transducer is the most important part of ultrasonic systems; the role of the transducer in the system is to generate the actual ultrasonic waves by converting the mechanical or electrical energy into sound energy at ultrasonic frequencies by vibrating mechanically. The ultrasonic transducer contacts to an electrical generator with 20 kHz frequency to transform electrical energy into ultrasonic energy by mechanical vibration at the same frequency (20 k cycles per second) [13]. The most applicable methods of ultrasound generation are carried out using ultrasonic transducers depending on the principle of the electrostrictive transformer. The principle of the methods is based on ferroelectric materials’ elastic deformation within a high-frequency electrical field which results in molecules’ polarized mutual attraction in the field. Then, the high-frequency alternating current is transmitted via two electrodes to ferroelectric material. After generating mechanical oscillation, the waves of sound are transmitted to the amplifier to generate the ultrasound [14].
The ultrasonic transducer is an electronic device that generates and receives the waves of sound. The transducer basically functions as a converter of energy, where it converts a form of acoustical energy into other energy forms (e.g., mechanical, electrical, or thermal energy). In addition, the transducer is reversible in either direction to convert electrical or mechanical energy to sound energy or vice versa. The most high-intensity ultrasonic generators are essentially magnetostrictive devices crystal oscillators in use. The categories of ultrasonic transducers fall into the following [10]:
Crystal oscillators are work through the effect of piezoelectric (reversible).
Magnetostrictive equipment are works based on the phenomenon of magnetostriction (reversible also).
Mechanical transducers that operate as generators and receivers.
Electromagnetic transducers are work based on the principle of the audio loudspeaker (but only work in the lower frequencies range of ultrasonic).
Other different types are thermal, optical, and chemical transducers.
Ultrahigh transducers that operate in the frequency range at megahertz or gigahertz.
Generally, the main transducers used in the most ultrasonic application can be summarized into three types: piezoelectric, magnetostrictive, and liquid-driven. The piezoelectric and magnetostrictive transducers convert magnetic and electrical energy into ultrasonic energy. The liquid-driven transducers depend on mechanical energy to generate ultrasonic energy [15].
The electrical generators are used to supply the ultrasonic systems with the required electrical energy to drive the transducer. Generally, the electrical generator produces a suitable power rating for the ultrasound system and allows the power to be set only indirectly through current (I) and voltage (V) settings. The current represents the electron charge traversing an area over some time interval and measured in amp, the voltage represents the stored energy in the electrons and measured in volt, and the electrical power is the output of current and voltage. Electrical generators that are designed and operate in the low frequency ranged from 10 to 40 kHz for ultrasonic generally focusing on industrial therapeutic applications, welding, cleaning, and disinfecting applications [11, 13].
The function of the emitter (reactor) is to radiate the waves of ultrasonic which are produced by the transducer into the treated medium. In addition, the role of the emitter may also be to amplify the ultrasonic vibrations when radiating them in some ultrasonic system. The main types of emitters are horns and baths; the horns often require a sonotrode to attach with the horn tip. The baths (Figure 5) usually consist of a stainless steel tank fixed with its base one or more transducers. The stainless steel tank holds a liquid case sample, and the transducers radiate ultrasonic directly into the sample [15].
Ultrasonic bath.
Although the ultrasonic has been used in the twentieth century, most of the new and improved ultrasonic applications has reached practically only in the past few years. Ultrasonic applications can be classified into two categories as high intensity and low intensity. High-intensity applications deliberately affect the contents of the propagation medium. Uses of high intensities include liquid atomization, material machining, medical surgery and therapy, material cleaning, plastics and metals welding, biological cell disruption, and material homogenization. Low-intensity applications carry the objective of transmitting energy through a medium in order to convey information through the treated medium or to obtain information about the medium. Uses of low intensities include nondestructive testing, medical diagnosis, elastic property measurements of materials and agricultural products, and acoustical holography. Nowadays, ultrasonic application technology has extremely affected the meat industry, with a controlling role in the classification of the product quality. It is being used to measure the fat layer thickness in live animals, and it is also utilized to predict carcass traits as a livestock management part, and it has been used to improve homogenized milk quality. In addition, the ultrasonic application technology is utilized in the pest control that includes the expulsion or killing of insects [10, 11]. The potential uses of ultrasonic applications technology for improving the nutritional and quality aspects of food have been highlighted by Ashokkumar [16]. The ultrasonic application technology offers a huge potential to bioprocessing industries and foods. Developing custom-made and new equipment is an issue to be addressed by food technologists, physicists, and engineers [16].
In addition, ultrasonic applications have been used for food processing as an important alternative processing method of conventional thermal. Ultrasonication process can preserve and pasteurize food products by inactivation of microorganisms and many enzymes at normal conditions of temperature to guarantee the safety and stability of foods for improving food quality. The changes in ultrasonic physical properties, such as attenuation and scattering caused by treated food product materials, have been also used in applications of food quality assurance [17]. The potential applications of ultrasonic are not only affected by the medium (gas, liquid, solid, or supercritical) but also the treatments variables (flow regime, temperature, ultrasonic intensity, etc.) and the structure of product which could affect the magnitude of the changes induced by ultrasonic processing [18]. Ultrasound can be divided into different frequency ranges. Most ultrasonic applications in the food processing technology involved nondestructive measurements which referred especially to the assessment of product quality; such applications use low power less than 1 W/cm2and high-frequency ultrasonic of 100 kHz to 1 MHz. Low-intensity ultrasonic is commonly applied as an analytical method to provide information on the food product’s physicochemical properties such as acidity, ripeness, firmness, content of sugar, etc. The high power levels used (typically in the range 10–1000 W/cm2) with low frequency (16–100 kHz) are used to make physical or chemical changes in the food to improve its properties [11, 19].
Generally, the ultrasonic applications are separated into two categories: the first category is low-intensity ultrasonic (called nondestructive or high-frequency ultrasound), and the second category is high-intensity ultrasonic (called low-frequency or power ultrasound) [20].
Low-intensity high-frequency ultrasonic is a nondestructive technique which is applied for detection purposes and provides information about the physicochemical characteristics of food products such as structure, firmness, composition, flow rate, physical state, etc. [21]. The action of ultrasonic waves is dependent on the input power. So the low-power ultrasonic is considered a noninvasive nondestructive method, and it is a useful technique for characterizing the physicochemical properties of food products, determining the food components type and contents, and measuring the emulsions droplet size. Irradiation of food products by low-power ultrasonic did not create any physical changes, at variance the high-power ultrasonic created the changes [9]. It is also used as a processing method in the industry of food to describe the components of food products, often in line with quality assurance. The nondestructive test basically is done by sending waves of ultrasonic through the medium without causing any permanent electrical, chemical, or physical changes in the food products. This is due to the use of too low ultrasonic intensity (<1 W/cm2), so there is no change in the foods by using this [11, 20, 21].
When ultrasonic waves pass through the medium, the particles in the medium oscillate mechanically in response to the low-intensity (low-energy) ultrasound. After that, the particles exposed to the waves of ultrasonic simply return to their position of equilibrium when the ultrasonic source is stopped. The distance to the location of reflection can be calculated by measuring the attenuation coefficient and frequency properties of ultrasonic to evaluate the physicochemical properties and to allow detection of compositional changes in the food products [11, 21]. In using low-intensity ultrasonic to characterize vegetable and fruit properties, there must be a relationship between the property to be measured and any measurable parameter of ultrasonic (e.g., impedance, attenuation, or velocity). The particular parameter that often influences the properties of ultrasonic in vegetables and fruits is the presence of intercellular air spaces that causes a resonant phenomenon over ultrasonic frequencies in a wide range. The appropriate frequency which transmits normally through vegetables and fruits is above 1 MHz at low intensity to avoid the damage in plant tissue [22].
On the other hand, there are other indirect applications for high-frequency ultrasonic in food processing area such as applications of ultrasonic in humidifiers or misting devices which are used in humidification or hydration of fresh fruit and vegetables or humidification systems of meat in the cold storage rooms for improving the quality of the product and decreasing the weight loss during the storage period. The operating principle of ultrasonic humidifiers depends on converting the electrical energy into periodically mechanical vibration by piezoelectric transducers and horn, which vibrates at high frequencies. The piezoelectric transducers are placed at the bottom of the water in order to produce high-frequency waves that propagate upward into the water. Then the ultrasonic wave rarefaction cycle causes cavitation; in addition, the water over the piezoelectric transducer will produce a wavy layer. If the ultrasonic waves have enough energy that can overcome the water surface tension, then droplets will be generated from the water top surface. When the vibrating surface amplitude is increased to a level that the ultrasonic waves collapse and are unstable, the droplets will be ejected away from the water surface into a mist. The droplet’s size is dependent on the frequency of vibration and water depth above the piezoelectric transducers [23, 24].
The diameter of the atomized droplets is calculated based on the properties of the ultrasonic generator by Eq. (3) [25, 26, 27]:
where
Generally, low-intensity ultrasonic applications can invaluably improve quality control in food production and monitor the changes that occur during humidification, emulsifying, freezing, or drying of food products. Some food manufacturers use nondestructive ultrasonic applications to locate foreign particles such as organic residues, bacterial infections, or glass in solid and liquid food products during and even after food packaging [28]. Low-intensity ultrasonic has been used successfully at ultrasonic wave frequency of 150 kHz as a noninvasive and nondestructive means of evaluating the commercial poultry egg quality at different conditions of storage using the velocity ultrasound phase within the material of eggs to recognize the differences between the aged and fresh eggs [29].
Low-intensity ultrasonic applications are considered one of the efficient tools for nondestructive quality evaluation of fresh fruits and vegetables. These applications are characterized as a reliable and fast technique for correlating fruit and vegetable properties and specific indices of quality with the different growth stages, after maturation, during storage, and after storage to be ready for marketing and consumption while ensuring its quality. Commercial application of ultrasonic applications will be useful to consumers and growers due to the public demand for high-quality and uniform agricultural products [30]. High-frequency ultrasonic technique using a contact transducer of 100 kHz as a nondestructive tool to determine fruit quality of navel oranges was applied successfully after fruit harvesting with a high accuracy level. Water content and density of the fruit can be determined accurately regardless of the other physical properties such as maturity, size, and the peel uniformity by isolating the results section which relates straight to the fruit acoustic properties. There is a high level of correlation between orange firmness and the reflected energy quantity of ultrasonic. Using ultrasonic technique, substandard individual fruit can be identified and sorted to be discarded at any harvest time and during processing or in a storage room. On the contrary, the methods of traditional destructive can be applied only on a limited sample of fruit after harvesting [31]. The measurements of the ultrasonic velocity (high-frequency) and attenuation which was conducted at 25 MHz on samples of mango juice showed a big variability with a maturity of fruit at picking and after picking at ripening stage in relation to texture of fruit, the content of total soluble solids (TSS), and changes in biochemical composition [32]. Many research has been done on nondestructive applications of ultrasonic technologies in food processing, but further future research is needed in this area in order to develop new automated ultrasonic equipment.
Applications of high-intensity ultrasonic or power ultrasonic are used to change the physical or chemical properties of food products as well as to promote the reactions of chemicals, produce emulsions, inhibit enzymes, disrupt cells, crystallization processes modification, etc. [21]. The use of high-intensity low-frequency ultrasonic waves generating sonotrodes was initially proposed for cleaning, emulsification, and bacterial lysing. The high-intensity ultrasonic wave (high-power) equipment using sonotrodes operating was further developed for processes of chemicals (up to 6 kW). In recent, ultrasonic systems are developed to generate high mega-sonic frequencies of ultrasound (400 kHz) with a high power level (>100 W). Therefore, the high-intensity ultrasonic wave is suitable for many applications in food products [33].
The high-intensity ultrasonic fundamental effect on the fluid material is for effective hydrostatic pressure on the medium and the imposition of acoustic pressure. The acoustic pressure (Pa) is a sine wave dependent on the ultrasonic frequency (f), time (t), and the wave pressure amplitude at the maximum (Pa-max) [Eq. (4)]. The maximum wave pressure is proportional to the transducer power input [34]:
The application of high-intensity ultrasonic (high power level = 75 W) was developed and tested to assist in convective heat transfer during food drying. The application of ultrasonic is based on the ultrasonic energy transmission through airborne contacts and solid contact series between the ultrasonic transducer and the tray of the food product as a vibration surface of ultrasonic transmitting. The slices of apple were dried using this method without compromising the quality of the product. The results indicated that using the ultrasonic application during apple drying led to the following: processing time was accelerated, consumption energy was reduced, production throughput was increased, and the quality of the product was not affected by ultrasonic processing. The results also indicated that the ultrasonic treatments led to improve the convective drying process efficiency when using high-power ultrasonic at low temperature. These results are very useful at the need to dehydrate heat-sensitive products effectively or to decrease food drying time in order to preserve the physicochemical and nutritional properties of food products [35]. Pasteurization of many food products by an ultrasonic application at 50°C has a preserving potential on the food quality in terms of color, flavor, and physicochemical properties compared to the techniques of conventional pasteurization at high temperatures [36]. The propagation of ultrasonic in a medium causes chemical and physical impacts, and these impacts have been used to improve the efficiency of the operations of various food processing technologies, and it has been also used as diagnostic technology in food quality control. The high-intensity ultrasonic application was applied to control ice crystal’s size distribution in low-temperature processes and related applications such as thawing, freezing, freeze-drying, and freeze concentration. It has been led to improve the freezing process efficiency, accelerate the freezing rate, and ensure frozen food quality [37].
High-intensity ultrasonic is being applied as an efficient preservation tool in fields of food processing for fruits and vegetables, honey, cereal products, proteins, gels, enzymes, cereal technology, dairy technology, water treatment, microbial inactivation, etc. [38]. In a previous study, the researchers have studied the effects of high-intensity ultrasonic at different levels of power ultrasound of 0, 200, 400, and 600 W as nonthermal processing on microbial inactivation (aerobic mesophilic, molds, yeasts, and coliforms), microstructure (particle size distribution and optical microscopy), rheology, color, and kinetic stability of the inulin-enriched whey beverage. The result obtained by applying ultrasonic power of 600 W was comparable to applying a high temperature of 75°C at short treatment time of 15 s concerning the total microbial inactivation. In addition, the high-intensity ultrasonic was better than the high-temperature short-time ultrasonic in improving kinetic stability of beverage, decreasing consistency and viscosity, avoiding phase separation, disrupting fruit and milk cells, and decreasing particle size. Therefore, nonthermal processing by high-intensity ultrasonic seems to be a promising technology for the production of probiotic dairy beverages. However, further future studies concerning the ultrasonic application effect on nutritional properties of this product must be evaluated before marketing [39]. Sterilization and improved emulsification can be conducted at lower temperatures than conventional treatments at high temperatures using high-intensity ultrasonic to produce a stable food product by retaining the useful bioactive ingredients and preventing spoilage of treated food. Applications of high-intensity ultrasonic in the fractionation of fat, dairy beverages production, and disruption of casein offer the potential of decreased treatment times; properties of the possible product have more advantages than those produced through conventional thermal techniques. Therefore, using ultrasonic applications in this area will lead to economic savings to producers in terms of producing value-added products and processing times and temperature. The consumers were satisfied with ultrasonic application studies for processing of food products to improve the quality of final products in terms of flavor, color, texture, and other physicochemical characteristics [40].
High-intensity ultrasonic treatment is a good process to inactivate enzymes and microorganisms at combined pressure and heat treatments as a hurdle technology. This combination is a successful application in lower temperatures for the inactivation process which provides a good solution for food product producers to secure fresh-like foods [41]. The impact of power ultrasound on the fruit and vegetable quality during drying and pre-treatment has been assessed. The indicators of fruit quality such as the losses of leaching, rehydration capacity, shrinkage of fruit, and the final product’s organoleptic characteristics have been also evaluated. The result showed that enzyme inactivation and leaching losses during blanching using high-intensity high-power ultrasonic at low temperature are similar to the result found using conventional treatments, but there is a significant reduction in the ultrasonic treatment time. Ultrasonic application in the drying of strawberries and carrots produces a highly significant reduction in the time of processing while providing high-quality final products. The final products’ quality was equivalent or superior to final products obtained in convective dryer prototype under similar conditions, was higher than marketed products, and was similar to the produced products by freeze-drying [42]. The impact of low-frequency high-power ultrasound (40 kHz, 130 W) on bean in terms of kinetics of hydration and cooking times was studied. Treatment of bean samples by ultrasonic waves for 30 min at 30°C 30 min occurs a significant increase in the effective diffusivity up to 45 times and reduces the time which obtains the equilibrium moisture content by 58.8% and the reduction percentage in cooking time reached 43% [43].
Generally, high-power ultrasonic has become an efficient technique for some commercial applications, such as homogenization, emulsification, crystallization, extraction, dewatering, low-temperature pasteurization, deforming, degassing, viscosity alteration, reduction of particle size, and inactivation or activation of enzymes. In addition, due to the need for inactivation of enzymes and microorganisms without destroying food nutrients, the high-power ultrasound applications are the best processing methods as a nonthermal alternative method to thermal processing treatments for food product preservation. This is due to continuous development and improvement in the design and manufacturing of ultrasonic equipment, but high-power ultrasonic for food processing like most innovative technologies in this field is not an effective technique for large-scale commercial application. Therefore, there is a need to conduct research on high-power ultrasonic for it to become an efficient large-scale commercial technology for processing food products [36, 44].
The cavitation phenomenon (liquid rupture) is easily observed in water boiling, turbines, hydrofoils, and in seawater in the proximity of a rotating propeller of the ship. It happens in those liquid regions that are subject to rapidly vacillating pressures with high amplitude. Cavitations also happen in a liquid exposed to high-energy ultrasonic, considering that the sound travels through a small volume of fluid or water. During the negative half of the pressure cycle, the liquid is exposed to tensile stress, and during the positive half of the pressure cycle, the liquid is exposed to compression stress. Therefore, the bubbles entrapped in the liquid will extend and retract alternatively. When the amplitude of pressure is sufficiently large and the bubble initial radius is minimal than the critical value, R0 is given using the following equation [10]:
where ω is a signal angular frequency, Po is the hydrostatic pressure in a liquid, γ is a principal specific heat ratio of the gas in a bubble, Tts is a surface tension at the bubble surface, and 𝜌 is the intensity of liquid.
The sound pressure load (<10 Pa) exerted on the ear of human is very small, but the pressure of ultrasonic (MPa) in liquids can be high enough to create the cavitation phenomenon (can destroy the treated medium). Ultimately, the cavitations lead to free radical production and sonochemical that react chemically with media (liquid) and also lead to the destruction of microbiological cells [11]. The ultrasound passage in liquid products generates a physical effect and mechanical agitation due to acoustic cavitation [16]. The food industry has usually depended on the heating methods for enzyme and microorganism inactivation for preservation of food products. Despite thermal method actually leading to destroy some spores, kill microorganisms, and inactivate some enzymes, food may lose their organoleptic and nutritional properties during the process. On the contrary, the inactivation mechanism using the ultrasonic application depends on the generation of physical forces due to the phenomenon of cavitation [17]. Transmitted, dispersed, and reflected pulses of acoustic can be used in food product quality assurance. Using ultrasonic application for enzymatic inactivation of some food products is very important for the preservation of quality which is a requisite for secure food material stabilization. The physical and chemical forces generated by ultrasonic cavitation raise severe damage to the microorganism’s cell wall, leading to microorganism inactivation. In addition, ultrasonic cavitation effects in liquid foods lead to disrupting the functional and structural components up to microorganisms cell lysis [17]. The applications of ultrasonic that are used for flaw detection in food processing for quality assurance of food products must be designed with ensuring that no cavitation possibly occurs. On the contrary, there are other applications of ultrasonic, depending on inertial cavitation to produce desirable changes in food products. These changes are produced by cavitation, such as microorganism inactivation and release of nutritional compounds and oils through the erosion of the cellular structure of the treated product cell [11]. The released energy during cavitation has a great ability to improve food products’ safety by destroying the pathogenic and food spoilage microorganisms and foreign material detection in food products. Although the applications of cavitation are well applied in many different industries other than food processing, the application of cavitation in processing of dairy products and its ingredients is recently gaining much attention, and it has a large potential to become a promising method in the near future in dairy product processing area such as reduction of viscosity, homogenization, making of yogurt, cream, and cheese, waste management, microbial inactivation, food safety, etc. Power ultrasound cleaning application at low frequency generally operates between 20 and 50 kHz. The cleaning effect of ultrasound depends on cavitation. Increasing the cavitation in cleaning liquid increases the ultrasonic cleaning effect. The most important parameters affecting the cavitation are ultrasonic frequency and temperature [41, 45].
Ultrasonic applications can be considered promising and applicable as a green technology for food safety and quality assurance purposes of food products. Ultrasonic applications are divided into two categories according to its intensity and frequency: high frequency with low intensity (power ultrasound) and low frequency with high intensity (nondestructive). High-intensity applications deliberately affect the contents of the propagation medium. Uses of high intensities offer an advantage in the selectivity, yield, and productivity, with enhanced quality, reduced physical and chemical hazards, and short processing time. Before the commercialization of some food products such as vegetables and fruit, oils and fat, cocoa-sugar and coffee, meal and flours, dairy, and meat which are complex mixtures of proteins, sugars, lipids, vitamins, aromas, fibers, antioxidants, pigments, and mineral and organic compounds have to be processed and preserved using ultrasonic applications for food meals and to extraction of food ingredients. Low-intensity applications are used for nondestructive quality evaluation of food products. The physical and chemical forces generated by ultrasonic cavitation raise severe damage to the cell wall of microorganisms, leading to their inactivation. A major advantage of the ultrasonic applications in food processing is that it is perceived as benign by the consumers. On the contrary, other processing technologies such as gamma radiation, microwaves, ohmic heating, and pulsed electric field can be cautiously considered by some of the population. Generally, the sound waves are considered nontoxic, safe, and environmentally friendly; this gives the use of ultrasonic major advantage over other modern processing techniques. In addition, it is characterized by the low cost of construction, low power consumption, simplicity compared to other technologies, and suitability for solid and liquid food products. Despite conducting a lot of research on applications of ultrasonic technologies for food products, there is still a need for more future research in order to utilize this technology on a fuller industrial scale to produce high-quality and safe food products.
The authors gratefully acknowledge the financial support for the project number (DPRC-7-2018) by the Date Palm Research Center of Excellence, King Faisal University, KSA.
The authors declare no conflict of interest.
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