Alternative configurations of most common MMJIT models
\r\n\tThe WHO classification in 2007; was based on the histogenesis and cell origin of the tumor. In the latest classification made in 2016; to better characterize the tumor and obtain better data on its prognosis; The combination of molecular and genetic biomarkers and histopathological features of the tumor was used. Despite all current treatment approaches, the median survival time is around 12 months in most GBM patients. Compared with the situation of some types of successfully treated cancers; the survival time of GBM patients is not at an acceptable level today. In the treatment of CNS tumors; surgery, chemotherapy, and radiation treatments (x-rays, gamma rays, electron and proton beams) are used. The therapeutic potential of chemotherapy; New strategies are needed to increase drug concentration at the diseased site, as this largely depends on the ability of the chemotherapeutic agent to achieve effective concentrations at tumor localization. Based on our better understanding of the genetic and molecular characteristics of CNS tumors; Targeted therapies, including vaccines, and treatment protocols such as immunotherapy are promising developments.
\r\n\r\n\tThis book supposes to be written by many authors who have an internationally honored place in their field to share their ideas about the treatment of CNS tumors. Surgery, Radiotherapy, Chemotherapy and Antiangiogenic Therapy Protocols, Immunotherapy, Molecular Therapy, Specific target-agents therapy with Nanoparticles and Gene Therapy for CNS tumors among the book chapters.
\r\n\tIn these sections; there are many practical pieces of information that can help the students who graduated from the Medicine Faculty and specialist doctors who are interested in Neurosurgery.
Since the 80’s, the Japanese production techniques and philosophies spread among the Western manufacturing companies. This was possible because the Toyota Motor Company experience was indeed a success. The so-called “Toyota Production System” (TPS) seemed to be the “one best way” to manage a manufacturing production site.
On the other side, it is also well known that not every implementation of Lean Production was a success, especially in Western companies: some enterprises – together with the consultancy firms that should have supported them – forgot that there are some main hypotheses and issues to comply with, in order to achieve Toyota-like results. On top of this, certain requisites are not related to a mere managerial approach, but depend on exogenous conditions, e.g. market behavior or supplier location; thus, not every company can successfully implement a TPS system.
One critical requirement for a TPS approach to be effective is that the production plan should be leveled both in quantity and in mix. This is indicated by the Japanese term
At first, the original Toyota Production System is briefly recalled, with specific regard to the
Secondly,
Then, a review on the mixed-model JIT scheduling problem (MMJIT), along with the related solving approaches, is presented. Despite the huge literature on MMJIT mathematical programming approaches, here it will be described why the real-world production systems still prefer the simpler kanban approach and the old (1983) Goal Chasing Method algorithm. In the end, an overview on simulators advantages to test alternative heuristics to manage JIT production is presented.
Just-in-Time was first proposed within the
transportation;
inventory;
motion;
waiting;
over-processing;
over-producing;
defects.
The TPS catchphrase emphasizes the “zero” concept: zero machine changeovers (“set-ups”), zero defects in the finished products, zero inventories, zero production stops, zero bureaucracy, zero misalignments. This result may be reached through a continuous improvement activity, which takes cue from Deming’s
Just-In-Time is the TPS solution to reduce inventory and waiting times. Its name, according to [4], was coined by Toyota managers to indicate a method aimed to ensure “the right products, in the right quantities,
one-piece flow;
mixed-model production;
demand-pull production;
Indeed, generally speaking, processing a 10 product-batch requires one tenth of the time needed for a 100 product-batch. Thus, reducing the batch value (up to “one piece”) would generate benefits in reducing either time-to-market or inventory level. This rule must come along with mixed-model production, which is the ability of manufacture different products alternating very small batches on shared resources. Demand-pull production indicates that the system is activated only after an order receipt; thus, no semi-finished product is processed if no downstream workstation asks for it. On top of this, in order to smooth out the material flow, the process operations should be organized to let each workstation complete different jobs in similar cycle times. The base reference is, thus, the
A
Since it was conceived as an easy and cheap way to control inventory levels, many different implementations of kanban systems have been experimented in manufacturing companies all over the world. In the following paragraphs, the most commonly used “one/two cards” kanban systems are described.
The “one-card” is the simplest implementation of kanban systems. This approach is used when the upstream and downstream workstations (respectively, the preceding and succeeding processes) are physically close to each other, so they can share the same stock buffer. The card is called “Production Order Kanban” (POK) [11, 12]. The stock buffer acts either as the outbound buffer for the first (A) workstation or as the inbound buffer for the second (B) workstation. A schematic diagram of a one-card system is shown in Figure 1.
A one-card kanban system
Here, each container (the JIT unit load) has a POK attached, indicating the quantity of a certain material contained, along with eventual complementary information. The POK also represents a production order for the Workstation A, indicating to replenish the container with the same quantity. When a B operator withdraws a container from the buffer, he removes the POK from the container and posts it on a board. Hence, A operator knows that one container with a specific part-number must be replenished in the stock buffer.
In the two-card system, each workstation has separate inbound and outbound buffers [13, 14]. Two different types of cards are used: Production Order Kanbans (POK) and Withdrawal Kanbans (WK). A WK contains information on how much material (raw materials / semi-finished materials) the succeeding process should withdraw. A schematic diagram of a two-card system is shown in Figure 2.
Each work-in-progress (WIP) container in the inbound buffer has a WK attached, as well as each WIP in the outbound buffer has a POK. WK and POK are paired, i.e. each given part number is always reported both in
A two-card kanban system
From the previously described procedure, it is clear that the each workstation bases its production sequence on kanban cards posted on the POK board. In literature, few traditional ways to manage the board are reported: each of them is quite easy to implement and does not require significant investments in technology or other expensive assets.
The most commonly used policy [3] requires having a board for each station, and this should be managed as a single First-In-First-Out (FIFO) queue. The board is usually structured as one vector (one column, multiple rows): POK are posted on the board in the last row. Rows are grouped in three zones (red/yellow/green) which indicate three levels of urgency (respectively, high/medium/low). Kanban are progressively moved from the green to the red zone and the workstation operator will process the topmost kanban. If a kanban reaches the red rows, it means that the correspondent material is likely to be requested soon, by the succeeding process. Thus, it should be urgently replenished in the outbound buffer, in order to avoid stock-outs.
Although this policy does not rely on any optimized procedure, it may ensure a leveled production rate in each workstation, given the fact that other TPS pillars are implemented, e.g. setup time reduction and mixed model scheduling. Indeed, if the final downstream demand is leveled, the production plan of the workstations will be leveled as well. Clearly, this policy is vulnerable to high setup times and differences among workstations cycle times: in this latter case, indeed, the ideal jobs sequence for a workstation may be far from optimal for the preceding. It is noticeable that the colored zones on the board only provide a visual support for the operators and do not influence the jobs processing order.
A
During the last decades, Just-In-Time has been criticized from different authors [17]. Indeed, certain specific conditions – which, though, are not uncommon in manufacturing companies – can put in evidence some well-known weak points of the Japanese approach. Specifically, un-steady demand in multi-product environments where differences in processing lead times are not negligible represent a scenario where JIT would miserably fail, despite the commitment of the operations managers.
First, we have to keep in mind that one pillar of Lean Production is the “one-piece-flow” diktat. A one-piece batch would comply with the Economic Production Quantity theory [18] only when order cost (i.e. setup time) is zero. Having non-negligible setup times hampers JIT implementation and makes the production leveling problem even more complicated. It is peculiar that, originally, operations researchers concentrated on finding the best jobs sequence considering negligible setups time. This bound was introduced into the mixed model kanban scheduling problem only since 2000. Setups are inevitable in the Lean Production philosophy, but are considered already optimized as well. Given that setup times are
Given the fact that JIT is based on stock replenishment, constant production and withdrawal rates should be ensured in order to avoid either stock outs or stock proliferation. Mixed-model production requires a leveled Master Production Schedule (MPS) [19], but this is not sufficient to smooth the production rate in a short time period. While it is easy to obtain a leveled production in a medium or even medium-short period, it is difficult to do it in each hour, for each workstation and each material.
Indeed, demand is typically unstable under two points of view: random frequency, which is the chance that production orders are irregularly received, and random quantities, which is related to product mix changes. Indeed, since TPS assume minimal stock levels, the only chance to cope with demand peak is to recur to extra production capacity. However, available production capacity should be higher than required as the average (as TPS requires), but for sure cannot be limitless. Thus, the JIT management system should anyway be able to consider the opportunity of varying the maintenance plan as well as the setup scheduling, in case of need. On the other hand, if the production site faces a leveled production, changes in product mix should not represent a problem; however, they increase sequencing problem complexity. Most of the operational research solutions for JIT scheduling are designed for a fixed product mix, thus its changes can greatly affect the optimality of solutions, up to make them useless.
On the contrary, kanban board mechanism is not influenced by demand randomness: as long as demand variations are contained into a certain (small) interval, kanban-managed workstations will handle their production almost without any problem. Therefore, in case of unstable demand, in order to prevent stock-outs, inventory managers can only increase the kanban number for each product: the greater are the variations, the greater is the need of kanban cards and, thus, the higher is the stock level. In order to prevent stock level raise, some authors [20, 21] proposed to adopt a frozen schedule to implement JIT production in real companies, where demand may clearly be unstable. Anyway, this solution goes in the opposite direction compared to JIT foundations.
Moreover, one-piece-flow conflicts with demand variability: the batch size should be chosen as its processing time exceeds the inter-arrival time of materials requests. Thus, the leveling algorithm must find the proper sequencing policy that, at the same time, reduces the batch size and minimize the inter-arrival time of each material request. This sequence clearly depends on the total demand of each material in the planning horizon. However, JIT does not use forecasting, except during system design; thus, scheduling may be refreshed daily. From a computational point of view, this is a non-linear integer optimization problem (defined
moving suppliers in the areas around the production sites, in order to minimize the supply lead time;
collaborating with the suppliers and helping them to introduce JIT in their factories;
always relying on two alternative suppliers for the same material, not to be put in a critical situation.
In the end, it should be noted that, considering that at each stage of the production process at least one unit of each material must be in stock, in case of a great product variety the total stock amount could be huge in JIT. This problem was known also by Toyota [1], who addressed it limiting the product customization opportunities and bundling optional combinations.
Many alternatives to JIT have been proposed since TPS appeared in Western countries. One of the most famous JIT-derivative approaches is CONWIP (CONstant Work-In-Process). This methodology, firstly proposed in the 90’s [23], tries to mix push and pull approaches: it schedules tasks for each station – with a push approach – while production is triggered by inventory events, which is a pull rule. Thus, CONWIP is card-based, as kanban systems, but cards do not trigger the production of a single component in the closest upward workstation; conversely, cards are used to start the whole production line, from beginning downwards. Then, from the first workstation up to the last one, the process is push-driven; materials are processed as they get to an inbound buffer, notwithstanding the stock levels. Only the last workstation has a predetermined stock level, similar to the JIT outbound buffer. All queues are managed through a FIFO policy. In order to have a leveled production rate and to avoid production spikes or idle times, the system is calibrated on the slowest workstation, the
Another alternative technique mixing push and pull system is the POLCA (Paired-Cell Overlapping Loops of Cards with Authorization), which stands at the base of the Quick Response Manufacturing (QRM) approach, proposed in 1998 [25]. QRM aims to minimize lead times rather than addressing waste reduction, as TPS does. A series of tools, such as manufacturing critical-path time, cellular organization, batch optimization and high level MRP, are used to minimize stock levels: the lesser is the lead time, the lesser is the on-hand inventory. Likewise CONWIP, POLCA handles WIP proliferation originating from multiple products, since it does not require each station to have a base stock of each component. At first, an MRP-like algorithm (called HL/MRP) creates some “Release Authorization Times”. That means that the HL/MRP system defines when each cell may start each job, as MRP defines the “Start Dates”. However, differently from a standard push system - where a workstation should process the job as soon as possible - POLCA simply authorizes the possibility to start the job. Analogously to CONWIP and Kanban, POLCA uses production control cards in order to control material flows. These cards are only used between, and not within, work cells. Inside each work cell, material flows resemble the CONWIP approach. On top of this, the POLCA cards, instead of being specifically assigned to a product as in a Kanban system, are assigned to pairs of cells. Moreover, whereas a POK card is an inventory replenishment signal, a POLCA card is a capacity signal. If a card returns from a downstream cell, it signals that there is enough capacity to process a job. Thus, the preceding cell will proceed only if the succeeding cell has available production capacity. According to some authors [20] a POLCA system may overcome the drawbacks of both standard MRPs and kanban systems, helping in managing both short-term fluctuation in capacity (slowdowns, failures, setups, quality issues) and reducing unnecessary stocks, which is always present in any unlevelled replenishment system – i.e. where heijunka condition is not met.
The Just in Sequence approach is an evolution of JIT, which embeds the CONWIP idea of mixing push/requirement and pull/replenishment production management systems. The overall goal of JIS is to synchronize the material flow within the supply chain and to reduce both safety stocks and material handling. Once the optimal production sequence is decided, it is adopted all along the process line and up to the supply chain. Thus, the suppliers are asked to comply not only to quantity requirements but also to the product sequence and mix, for a certain period of time. In this case the demand must be stable, or a frozen period should be defined (i.e. a time interval, prior to production, in which the demand cannot be changed) [26]. Clearly, when the demand mix significantly changes, the sequence must be re-computed, similarly to what happens in MRP. This makes the JIS system less flexible compared to JIT. Research results [27] proved that, applying some techniques to reduce unsteadiness – such as flexible order assignment or mixed bank buffers – the sequence can be preserved with a low stock level. Thanks to
Leveraging on the common idea that “a chain is no stronger than its weakest link”, the Israeli physicist E.M. Goldratt firstly introduced the Theory of Constraints (TOC) in his most famous business novel “the Goal” [28]. Looking to a production flow-shop as a chain, the weakest link is represented by the line bottleneck. Compared to the TPS approach of reducing wastes, this approach is focused on improving bottleneck operations, trying to maximize the
Its implementation is based on a loop of five steps:
constraint identification;
constraint optimization;
alignment of the other operations to the constraint optimization;
elevation of the constraint (improving throughput);
if the constraint after the previous 4 steps has moved, restart the process.
Again, Deming’s concept of “improvement cycle” is recalled. However, improvements are only focused on the bottleneck, the Critical Constraint Resource (CCR), whereas in the Lean Production’s Kaizen approach bottom-up, an improvement may arise wherever wastes are identified; moreover, improvements only aim to increase throughput. It is though noticeable that the author includes, as a possible throughput constraint, not only machinery problem but also people (lack of proper skills) and policies (bad working). To this extent, Goldratt coined the “Drum-Buffer-Rope” (DBR) expression: the bottleneck workstation will define the production takt-time, giving the beat as with a drum. The remaining upstream and downstream workstations will follow this beat. This requires the drum to have an optimized schedule, which is imposed to all the production line. Thus, takt-time is not defined from the final demand anymore, but is set equal to the CCR minimal cycle time, given that the bottleneck capacity cannot be overcome. A “buffer” stock is only placed before the CCR, assuring that no upward issue could affect the process pace, reducing line throughput. This helps in reducing the inventory level in comparison to replenishment approaches, where buffers are placed among all the workstations. Eventually, other stock buffers may be placed in few synchronization points in the processes, besides the final product warehouse, which prevents stock-outs due to oscillating demand. The “rope” represents the job release authorization mechanism: a CONWIP approach is used between the CCR and the first phase of the process. Thus, the advanced entrance of a job in the system is proportional to the buffer size, measured in time. Failing to comply with this rule is likely to generate too high work-in-process, slowing down the entire system, or to generate a starvation condition on the CCR, with the risk of reducing the throughput. Several authors [29, 30, 31] analyzed the DBR rule in comparison to planning with mathematical linear programming techniques. Results on the most effective approach are controversial.
The leveling problem in JIT operations research literature was formalized in 1983 as the “mixed-model just-in-time scheduling (or sequencing) problem” (MMJIT) [32], along with its first solution approach, the “Goal Chasing Method” (GCM I) heuristic.
Some assumptions are usually made to approach this problem [33]. The most common are:
no variability; the problem is defined in a deterministic scenario;
no details on the process phases: the process is considered as a black box, which transforms raw materials in finished products;
zero setup times (or setup times are negligible);
demand is constant and known;
production lead time is the same for each product.
Unfortunately, the problem with these assumptions virtually never occurs in industry. However, the problem is of mathematical interest because of its high complexity (in a theoretical mathematical sense). Because researchers drew their inspiration from the literature and not from industry, on MMJIT far more was published than practiced.
The objective of a MMJIT is to obtain a leveled production. This aim is formalized in the Output Rate Variation (ORV) objective function (OF) [34, 35]. Consider a M set of
Each product type
Given a set of binary variables
subject to
The first and second group of constraints indicate that for each time
A simplified version of this problem, labeled “Product Rate Variation Problem” (PRV) was studied by several authors [36, 37, 38], although it was found it is not sufficient to cope with the variety of production models of modern assembly lines [33]. Other adaptations of this problem were proposed along the years; after 2000, when some effective solving algorithms were proposed [39], the literature interest moved on to the MMJIT scheduling problem
In this equation,
an initial setup is required regardless of sequence; this is the reason for the initial “1” and the
the setup time is standard and it is not dependent from the product type;
the setup number and setup time are directly proportional each other.
The following sets of bounds must be added in order to shift
Being a multi-objective problem, the MMJIT with setups has been approached in different ways, but it seems that no one succeeded in solving the problem using a standard mathematical approach. A simulation approach was used in [42]. Most of the existing studies in the literature use mathematical representations, Markov chains or simulation approaches. Some authors [10, 40] reported that the following parameters may vary within the research carried out in the recent years, as shown in Table 1 below.
The MMJIT problem, showing nonlinear OF and binary variables, has no polynomial solutions as far as we know. However, a heuristic solution approach can be effective. To get to a good solution, one among dynamic programming, integer programming, linear programming, mixed integer programming or nonlinear integer programming (NLP) techniques can be used. However, those methodologies usually require a long time to find a solution, so are infrequently used in real production systems [44]. Just a few studies used other methods such as statistical analysis or the Toyota formula [45]. The most renowned heuristics are the Miltenburg’s [36] and the cited Goal Chasing Method (GCM I) developed in Toyota by Y. Monden. Given the products quantities to be processed and the associated processing times, GCM I computes an “average consumption rate” for the workstation. Then, the processing sequence is defined choosing each successive product according to its processing time, so that the cumulated consumption rate “chases” its average value. A detailed description of the algorithm can be found in [32]. GCM I was subsequently refined by its own author, resulting in the GCM II and the Goal Coordination Method heuristics [46].
The most known meta-heuristics to solve the MMJIT [44, 47, 48] are:
Simulated Annealing;
Tabu Search;
Genetic Algorithms;
Scalar methods;
Interactive methods;
Fuzzy methods;
Decision aids methods;
Dynamic Programming.
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t||||||||||
\n\t\t\t\t | \n\t\t\tMathematical programming | \n\t\t\tSimulation | \n\t\t\tMarkov Chains | \n\t\t\tOther | \n\t\t|||||||
\n\t\t\t\t | \n\t\t\tKanban number | \n\t\t\tOrder interval | \n\t\t\tSafety Stock level | \n\t\t\tOther | \n\t\t|||||||
\n\t\t\t\t | \n\t\t\tKanban number | \n\t\t\tUtilization ratio | \n\t\t\tLeveling effectiveness | \n\t\t||||||||
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\tSetup cost | \n\t\t\tInventory holding cost | \n\t\t\tOperating cost | \n\t\t\tStock-out cost | \n\t\t||||||
\n\t\t\t\t | \n\t\t|||||||||||
\n\t\t\t\t | \n\t\t|||||||||||
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\tFlow-shop | \n\t\t\tJob-shop | \n\t\t\tAssembly tree | \n\t\t|||||||
\n\t\t\t\t | \n\t\t\tMulti-period | \n\t\t\tSingle-period | \n\t\t|||||||||
\n\t\t\t\t | \n\t\t\tMulti-item | \n\t\t\tSingle-item | \n\t\t|||||||||
\n\t\t\t\t | \n\t\t\tMulti-stage | \n\t\t\tSingle-stage | \n\t\t|||||||||
\n\t\t\t\t | \n\t\t\tMultiple machines | \n\t\t\tSingle machine | \n\t\t|||||||||
\n\t\t\t\t | \n\t\t\tCapacitated | \n\t\t\tNon-capacitated | \n\t\t|||||||||
\n\t\t\t\t | \n\t\t\tOne-card | \n\t\t\tTwo-card | \n\t\t|||||||||
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\tDefined | \n\t\t\tIgnored (container size equals one item) | \n\t\t||||||||
\n\t\t\t\t | \n\t\t\tRandom set-up times | \n\t\t\tRandom demand | \n\t\t\tRandom lead times | \n\t\t\tRandom processing times | \n\t\t\tDeterminism | \n\t\t||||||
\n\t\t\t\t | \n\t\t\tManufacturing system | \n\t\t\tContinuous production | \n\t\t|||||||||
\n\t\t\t\t | \n\t\t\tZero withdrawal times | \n\t\t\tNon-zero withdrawal times | \n\t\t|||||||||
\n\t\t\t\t | \n\t\t\tIgnored | \n\t\t\tComputed as lost sales [43] | \n\t\t|||||||||
\n\t\t\t\t | \n\t\t\tDynamic demand | \n\t\t\tBreakdowns possibility | \n\t\t\tImbalance between stages | \n\t\t\tReworks | \n\t\t\tScraps | \n\t\t
Alternative configurations of most common MMJIT models
In some experiments [44] Tabu Search and Simulated Annealing resulted to be more effective than GCM; however, the computational complexity of these meta-heuristics – and the consequent slowness of execution – makes them quite useless in practical cases, as the same authors admitted.
Another meta-heuristic based on an optimization approach with Pareto-efficiency frontier – the “multi objective particle swarm” (MOPS) – to solve the MMJIT with setups was proposed through a test case of 20 different products production on 40 time buckets [47].
In [48], the authors compared a Bounded Dynamic Programming (BPD) procedure with GCM and with an Ant Colony (AC) approach, using as OF the minimization of the total inventory cost. They found that BDP is effective (1,03% as the average relative deviation from optimum) but not efficient, requiring roughly the triple of the time needed by the AC approach. Meanwhile, GCM was able to find the optimum (13% as the average relative deviation from optimum) on less than one third of the scenarios in which the AC was successful.
A broad literature survey on MMJIT with setups can be found in [49] while a comprehensive review of the different approaches to determine both kanban number and the optimal sequence to smooth production rates is present in [10].
Assumed that time wastes are a clear example of MUDA in Lean Production [3], complex mathematical approaches which require several minutes to compute one optimal sequence for MMJIT [44] should be discarded, given that the time spent calculating new scheduling solutions does not add any value to products. On the other side, it is notable that MRP computation requires a lot of time, especially when it is run for a low-capacity process (in which CRP-MRP or capacitated MRPs are required). However, being MRP a look-ahead system which considers the demand forecasts, its planning is updated only at the end of a predefined “refresh period”, not as frequently as it may be required in a non-leveled JIT context. MRP was conceived with the idea that, merging the Bill-Of-Materials information with inventory levels and requirements, the production manager could define a short-term work plan. In most cases, MRP is updated no more than every week; thus, an MRP run may also take one day to be computed and evaluated, without any consequences for the production plan. On the contrary, the situation in JIT environment evolves every time a product is required from downstream. While MRP assumes the Master Production Schedule forecasts as an input, in JIT nobody may know what is behind the curtain, minute by minute.
Indeed, while a perfect JIT system does not need any planning update – simply because in a steady environment (e.g. heijunka) the optimal sequence should always be almost the same, at least in the medium term – real-world short-term variations can deeply affect the optimality of a fixed schedule production. For instance, a one-day strike of transport operators in a certain geographical area can entirely stop the production of a subset of models, and the lack of a raw material for one hour can turn the best scheduling solution into the worst. On top of this, while MRP relies on its “frozen period”, JIT is exposed to variability because is supposed to effectively react to small changes in the production sequence. However, some authors noticed that the JIT sequences [10, 48, 50] are not so resistant to demand changes, so a single variation in the initial plan can completely alter the best solution. This is particularly true when the required production capacity gets near to the available. Thus, developing algorithm for solving the MMJIT problem under the hypothesis of constant demand or constant product mix seems useless.
JIT was developed for manual or semi-automated assembly line systems, not for completely automated manufacturing systems. The flexibility of the JIT approach requires a flexible production environment (i.e. the process bottleneck should not be saturated) and this is not an easy condition to be reached in real industries. Consequently, despite the competence of its operations managers, even a big multinational manufacturer may encounter several problems in implementing JIT if a significant part of its supplier is made of small or medium-size enterprises (SMEs), which are naturally more exposed to variability issues. On top of this, differently from MRP – where the algorithm lies within a software and is transparent for users – in JIT the product sequencing is performed by the workforce and managed through the use of simple techniques, such as the heijunka box, the kanban board or other visual management tools, e.g.
As it has been said, finding good solutions for the MMJIT problem with setups using an algorithmic approach may take too long and, on top of this, the solution can be vulnerable to product mix changes. Indeed, Kanban technique and GCM I methods are the most used approaches to manage JIT production thanks to their simplicity [44]. Some companies, where SMED techniques [51] failed to reduce setup times, use a modified version of the kanban FIFO board, in order to prevent setups proliferation. Thus, a simple batching process is introduced: when more than one kanban is posted on the board, the workstation operator shall not start the job on the first row but, on the contrary, chooses the job which allows the workstation to skip the setup phase. As an example, given the original job sequence A-B-A-C-A-B for a workstation, if the operator is allowed to look two positions ahead, he would process A-A-B-C-A-B, saving one setup time. In such situations, where setup times cannot be reduced under a certain value, rather than giving up the idea of adopting the Lean Production approach, heuristics can be developed and tested in order to obtain a leveled production even if coping with long setup times or demand variability.
The most common method to analyze and validate heuristics is through simulation. Several authors agree that simulation is one of the best ways to analyze the dynamic and stochastic behavior of manufacturing system, predicting its operational performance [52, 53, 54]. Simulating, a user can dynamically reproduce how a system works and how the subsystems interact between each other; on top of this, a simulation tool can be used as a decision support system tool since it natively embeds the
In spite of these potentialities, there seem to be few manufacturing simulation software really intended for industrial use, which go beyond a simple representation of the plant layout and modeling of the manufacturing flow. On top of some customized simulators – developed and built in a high-level programming language from some academic or research group in order to solve specific cases with drastic simplifying hypotheses – the major part of commercial software implements a graphical model-building approach, where experienced users can model almost any type of process using basic function blocks and evaluate the whole system behavior through some user-defined statistical functions [61]. The latters, being multi-purpose simulation software, require great efforts in translating real industrial processes logic into the modeling scheme, and it is thus difficult to “put down the simulation in the manufacturing process” [55]. Indeed, the lack of manufacturing archetypes to model building seems one of the most remarkable weakness for most simulator tools, since their presence could simplify the model development process for who speak the "language of business" [62]. Moreover, commercial simulators show several limitations if used to test custom heuristics, for example to level a JIT production or to solve a line-balancing problem: some authors report typical weaknesses in presenting the simulation output [63] or limited functionalities in terms of statistical analysis [64], on top of the lack of
In order to widespread simulation software usage among the manufacturing industry, some authors underline the need of a standard architecture to model production and logistics processes [65, 66, 67]. Literature suggested to focus on a new reference framework for manufacturing simulation systems, that implement both a structure and a logic closer to real production systems and that may support industrial processes optimization [68, 69].
Moreover, given hardware increased performances, computational workload of a simulation tool is not a problem anymore [70] and it seems possible to develop simulators able to run in less than one minute even complex instances. The complexity of a manufacturing model is linked both to size and system stochasticity. A careful analysis of time series can provide useful information to be included in the simulator, in order to model stochastic variables linked to machine failures or scrap production. This allows a more truthful assessment of key performance indicators (KPI) for a range of solutions under test.
The effective application of JIT cannot be independent from other key components of a lean manufacturing system or it can “end up with the opposite of the desired result” [71]. Specifically, leveled production (heijunka) is a critical factor. The leveling problem in JIT, a mixed-model scheduling problem, was formalized in 1983 and named MMJIT. Several numbers of solving approaches for MMJIT have been developed during the last decades. Most of them assume constant demand and product mix. Zero setup-times hypothesis has been removed only since 2000, and few approaches still cope with stochasticity. On top of this, these algorithms, although heuristic based, usually spend too much time in finding a good solution. Simplification hypotheses, operations research competences requirements and slow execution prevented these approaches to widespread in industry. Indeed, the heijunka box or the standard FIFO kanban approach with the simple Goal-Chasing-Method heuristic are still the most used tools to manage production in JIT environment. This is acknowledged also by the proponents of alternatives, and GCM is always used as a benchmark for every new MMJIT solution. However, these traditional approaches are not so effective in case of long setups and demand variations, given the fact that they have been conceived in pure JIT environments. In high stochastic scenarios, in order to prevent stock-outs, kanban number is raised along with the inventory levels. There are several cases of companies, operating in unstable contexts and where setup times cannot be reduced over a certain extent, that are interested in applying JIT techniques to reduce inventory carrying costs and manage the production flow in an effective and simple way. The development of kanban board / heijunka-box variations, in order to cope with the specific requirements of these companies, seems to offer better potentialities if compared to the development of difficult operations research algorithmic approaches. In order to solve industrial problems, researchers may concentrate in finding new policies that could really be helpful for production systems wishing to benefit from a JIT implementation but lacking in some lean production requirements, rather than studying new algorithm for MMJIT problem.
For instance, kanban board / heijunka-box variations can effectively focus on job preemption opportunities in order to reduce setups abundance, or on new rules to manage priorities in case of breakdowns or variable quality rates. The parameters fine-tuning can be performed through simulation. In this sense, given the limitations of most commercial software, the development of a simulation conceptual model – along with its requisites – of a model representation (objects and structures) and some communication rules between the subsystems (communication protocols) are the main issues that need to be addressed from academics and developers.
As a result of this pandemic, we can note the disappearance of questions and conversations related to climate change [1]. It seems that the lack of progress towards the objectives of the Paris agreement has disappeared into the background. What would once have been the center of attention in most forums and agendas does not even reach the inside pages of magazines or newscasts. Our global consciousness has shifted towards survival and transcendence, as a species, which are immediate threats to our lives [2].
\nThis desire for transcendence allows us to analyze what happens when we are closer to the top of the pyramid and all our basic concepts are covered as in Maslow’s pyramid [3]. With the pandemic around the world, we have all been taken down on some levels and we are now looking to protect ourselves and our loved ones.
\nOn the surface, it seems that all efforts, all activities and all initiatives have shifted away from the greatest threat to our planet, climate change, towards the immediate and more tangible threat to our lives of COVID-19 [4].
\nThe desire for transcendence can also be expressed towards transhumanism and environmental sustainability. Transhumanism is a social movement predicated on the belief that we can and should leave behind our biological condition by merging with technology [5]. Environmental sustainability is responsibly interacting with the planet to maintain natural resources and give the ability of future generations to meet their needs. It can be interpreted as an steady state economy [6]. This status depends on legal, thermodynamic, and economic aspects of ecosystem services [7, 8].
\nThis chapter proposes a new regard of the circular economy following analysis of the most prominent issues related with COVID-19, government responses to the crisis and sustainability.
\nThe significance of this article is that it addresses the criticisms of the circular economy paradigms by considering three dimensions of analysis related to the COVID-19 outbreak [9]. The increased demand for sustainable production processes post COVID-19 justifies the need for more effective policies with a focus on human health [1, 10].
\nThe methodology used in this paper takes sustainability, health care, economic growth and dimensions to provide an analysis related to COVID-19. Figure 1 presents the analysis of how these three dimensions might be reinterpreted using the framework proposed by Doussoulin [9]. As shown in Figure 1, when π = 100%, z = 0 and w = 0, all attention dedicated by the government are apportioned to economic growth. Alternatively, if π = 0, z = 0 and w = 100%, then all attention is given to health care. It is also possible the attention to the earth as a natural resource, this can be represented by the point where π =0, z = 100% and w = 0, which corresponds to a sustainable future [11, 12, 13].
\nThree dimensions: Economic growth, health care and sustainability. Source: [
This section outlines a set of matters involving the COVID-19 crisis through the exploration of three dimensions: sustainability, health care and economic growth, as follow:
\nThe circular economy has rapidly gathered momentum within sustainable development initiatives due to its potential for decoupling economic development and growth from resource consumption [14, 15]. The industrial circular economy corresponds to π =0, z = 100% and w = 0 in Figure 1. Manufacturing companies play a critical role in the implementation of the circular economy at the industrial level. Success with this role requires a rethinking of the overall value generation logic and the redefinition of business success, by reconfiguring existing business models or creating new business models for the circular economy [16, 17].
\nThe transition of manufacturing companies’ business models to the circular economy is still limited, with low market penetration across sectors. Among several external and internal barriers, manufacturing companies face shortcomings in capabilities and skills and require knowledge and scientific-based guidance for business modeling within the context of the circular economy [18, 19].
\nAcademic literature is unable to support companies adequately, due to a core gap related to the lack of systematized practices to provide guidance and advice to manufacturing companies during the design of business models for the circular economy [20]. Based on this gap, a design research methodology was applied in this research, with the objective of developing a theoretical framework to provide guidance and advice for policy-makers and stakeholders [21].
\nThe workload of the MSW (management solid waste) industry has increased due to significant rises in household consumption in Japan [22] and the construction sector in France [23].
\nThe Center for Disease Control (CDC) is saying the same as the World Health Organization and also what Ocean is saying, that solid waste is not an effective vector for the transmission of corona virus from one person to another by being within six feet of that person. Something people need to be aware of when they are at work, whether it is collecting trash, recycling material or a disposal facility. The CDC just late last week, issued a new waste specific fact sheet [24].
\nThat indicated that corona virus can live on cardboard for up to 24 hours, which causes some concerns in our industry. People are also urged to delay their spring cleaning. Individuals will admit that this has not always been succesfully communicated [25]. As people at home with a lot spare time, they are using it to clean out their attics, garages and basements, and this is generating a significant amount of material that has stressed the system to some extent. Authors are also communicating the importance of not reusing gloves and masks that people are now wearing and that they should be thrown in the trash after use [26].
\nWe can agree that generally individuals do not want to recycle. The material, even though it is mostly made of plastic, is not recycled on the curbside and unfortunately, what we have seen a great deal is many people throwing away gloves and masks on the street, in parking lots and in parks which is bad for public health in the environment [27]. The fact that some facilities are closing due to small amounts of coronavirus on things that are being handled, at a time when the commodity pricing is actually going up, is ironic. There have been a number of facilities that perform recycling that have actually closed. They closed because they were unable to provide sufficient social distancing between workers. However most facilities have been either installing Plexiglas separators between the sorters or pickers on the line or they have been separating the pickers [28, 29].
\nThe commercial sector in the United States shut down a major source of recovered paper. A major source of clean, recovered paper was lost, and so it is our hope that, as the United States returns to something like the new normal, we will see that those paper sources in the commercial sector will be available to recycling facilities [30].
\nThere is an interaction at the weighing facilities and to reduce the potential of exposure to the attendants who work there, the use of cash and the signing of documents is being eliminated. Other steps are being taken to ensure there is as little interaction as possible with customers at drop-off centers, where people go to drop off various types of materials such as glass, books or batteries. Those facilities have seen a significant increase in the number of customers that they are receiving so there is a need to keep the customers six feet apart from each other and also from the staff. Disposal facilities also pose a number of hazards and challenges when a waste, hauler or a private citizen goes through a small facility, whether it is by dumper truck or to bring some boxes from the household, hazardous waste or other material [31].
\nThese facilities are urging people to wear masks. Some people are not happy being asked to wear them at some of the facilities. Some facilities have higher safety measures to maintain distance and enforce their rules. Curbside recycling collection has been suspended in several communities [31]. For the use of vehicles, at recycling plants and composting plants, a number of issues in handling the materials and during maintenance were coming up. In the United States, the drop-off centers are adding to the burden. People are very slowly beginning to go back to work and it is our expectation that this is going to continue and some authors will argue a decrease in the amount of residential waste and an increase in the amount of commercial waste being generated [32].
\nThings were coming to a point where waste management was at risk of stopping because of the worries of the workers and some of their employees, basically there was a very short time frame to sort this out. If it is a commercial collection, maybe it could be stopped, if it is a household collection, it probably could not be, and it cannot be run by a single person because of health and safety or other issues. People could then think about transporting individuals separately through the collection point, and some local authorities in the UK have been taking that approach where they have individually taken collections, from the curbside where the transfer has been done. Other solutions might be screens in the cabs, therefore there is a guidance on screens [33].
\nThere are health and safety issues in assessing the safety of the screen and the material. Whether or not the driver can see through it, whether they can get out of a vehicle safely if there is an accident, and if it is effective in case somebody sneezes and if it will really stop an aerosol from circulating in a cab. There has been quite a lot in the media about viral load and long term exposure to viral load [34].
\nTo what extent can some of the lessons learned in UK composting sites and reducing work exposure to aerosols be translated to the virus or are the particles too small? Authors argued that the reason that has happened is because people in their homes are seeing all this waste being generated and being putting out on the curb which is not usual and seeing it taken away by trucks [35]. Normally, when the garbage truck comes to the house, people are not at home, they are at work, and so the fact that there are these people out there every single day performing this essential service when citizens are told to stay at home has been a way to reinforce the importance of the industry. Authors argue that in the long term, this will benefit the industry in a number of things, but in the short-term, we will continue to have challenges related to reducing exposure because, unfortunately, the virus is very contagious compared to other diseases. It spreads easily, and at least some component of the virus can live on cardboard for a while [36].
\nThere is limited research on the viability of this virus in the recycling stream. As pointed out earlier, we need to make sure that those workers who are working with recycling are not touching the recycled material and being infected by COVID-19 [37].
\nIt is a terrible pandemic, so in terms of waste we have to make sure that people are managing it safely. It also highlights deficiencies in the system in the past which individuals are now having to correct to ensure the health and safety of the workers properly. Maybe now people need to address how we protect our workers in general. People need to make the point and highlight the fact that the message must be quite clear to the citizens and to the overall community through different channels that separate collection and recycling services are a part of the overall waste management system and part of those essential services that must keep running [38]. It would be a bad thing if the message that was sent out was that these kinds of services can be interrupted or can be stopped. The support of the people is needed and would be difficult to get back if lost, as all international researchers assert [39].
\nIt would be interesting to analyze where facilities dedicated to hazardous waste treatment are lacking and countries where sufficient capacities exist. The import of hazardous waste from outside the EU is worrying not because of the lack of business, but because it means that potentially stockpiles of hazardous waste will grow and grow in the coming months, with all associated risks for health and the environment. Authors argue that increase is not really the right word to best define the situation. It would be better to say shift: there is a shift in terms of the nature of hazardous waste, a shift from non-dedicated to dedicated hazardous waste treatment facilities and a shift in the internal movement of waste [40].
\nIt is possible to improve recycling in the time of COVID-19. Particularly, People have to think about the fact that if houses are separating their recyclables or households, they cannot contaminate themselves. There is no reason that any city should tell its citizens not to continue recycling. The point is that only recyclables from one household, which only people in that household have touched, are put out for collection for recycling or put into a bin where somebody else will empty. It does require that cities who find recycling to be important at the city level should think about extra storage and extra capacity.
\nWe are living in unstable and uncertain times due to current health, economic and social instabilities [41]. The significant challenges to the waste industry are putting authorities and waste workers under significant pressure. That is where it is our duty to ensure that our members and the waste management industry have knowledge and information to keep our towns and cities clean and healthy. Proper waste management is an essential public service that cannot be overlooked in this time of crisis [42].
\nPandemics prove to the public worldwide that for crucial issues, scientific support of political decisions and data-driven decision-making is absolutely necessary for a proper response so a new relationship between science and policy is needed. This is something that has been lacking in waste management the last ten or fifteen years [43].
\nThis period marks a return of single-use plastics after three years of efforts to ban them under a specific framework. Thus, single-use plastics are coming back today and this time also with gloves, with masks and with personal protective equipment that might be infectious [27].
\nThis difficult period is not a reason to change our policies and circular economics for plastics and single-use plastics. Authors argue that the plastic industry is trying to use the pandemics as an excuse to delay any new regulation or secretly use plastics, both at a European or national level [44, 45].
\nWe have a range of wastes being generated at the moment and initially it comes down to segregation and while people may be seeing the volumes of personal protective equipment (PPE) and other medical wastes increasing, we have to look at the relative risk of those wastes. Waste from people who treat a COVID-19 patient, will be particularly risky. [46].
\nA lot of medical waste will be produced. There will also be some pharmaceutical waste. There will be ventilated type waste; there will be collection mechanisms for contaminated waste, and proper, genuine medical waste. These wastes need to be collected and treated appropriately, not necessarily by incineration where alternative treatments are in place. They should be genuinely treated as infectious medical wastes. There will be a lot more medical waste in our general waste. It has been argued that if we start to call them social distancing wastes rather than medical wastes, even though they look like medical wastes, some distinctions may be able to be made [47, 48].
\nAuthors claim that many of the patients who have COVID-19 who are very sick and also have other conditions as well. In addition to dealing with the pandemic, people also need to consider that those wastes may also have hepatitis or HIV, as well as those other viruses that we would normally manage as healthcare waste [40, 49]. Individuals have to be aware of the fact that, while this is a very serious pandemic, there are other people that are sick with other diseases, and we have to ensure that these other conditions are managed.
\nAs well as waste, there are other contaminants that could be present. People should be treating anything that is known to be infectious or reliably suspected, and this is where it is not necessary that PPE is classified as medical waste. While authors are seeing pressure from an increasing volume and a decrease in weight into the system, we have to still make sure that we are taking care of health. In effect, COVID-19 is like a little ball of genetic material with a fatty inside with a little protein sticking out of it. People need to ensure that people can use heat when necessary, but not necessarily high temperance in washing. It would be useful to have facilities for washing clothes and washing linen. These wastes should be kept out of the waste stream. People need to ensure that the threat against waste workers is mitigated and that may be done by social distancing and PPE. Welfare facilities that ensure that people can wash their hands should also be given because it is not a good idea that more people get sick [50].
\nIt would also be useful to have facilities to ensure that people can maintain not only their personal hygiene, but also environmental hygiene. As authors argue, as there is an increase in the volume of waste arriving to waste facilities, all need to be operating effectively in this situation. It depends on existing infrastructures and the capacities for the treatment of hazardous waste and it also depends on the strictness of lockdown and how it impacts the industrial activities. It is important to say that the other waste treatment facilities never stop during the crisis. Part of the healthcare waste is treated in hazardous waste incinerators and, during this period, where the quantity and mostly the volume of this care waste rises by twenty to forty percent, all permitted capacities are used [31, 51].
\nA change in the distribution of the different categories of hazardous waste has been observed. Some industrial activities work intensively, for example, the pharmaceutical sector, and others slow down or have even stopped completely, such as the automotive industry and all the suppliers, hazardous waste from households and hazardous waste in small quantities, drums or buckets from refuse collectors. Another important aspect is those non dedicated facilities which treat hazardous waste as a side activity, have slowed down or drastically reduced or even shut down, such as part of the cement industry due to the stopping of activity in the construction sector. Companies have dedicated operators for the treatment of hazardous waste, and they have been able to take over. If we take the case of Europe, we face an increase of transboundary shipments from within the EU to countries where treatment capacities are lacking [38].
\nWaste management has been classed as an essential public service in most countries, which means waste management professionals must navigate the health and safety risks related to coronavirus, although, information about the virus is still evolving [52]. It is essential that countries ensure that waste workers, especially frontline collection and processing employees, are afforded maximum protection and remain safe and can support communities in mitigating the spread of the virus [44]. The medical issue corresponds to π =0, z = 0 and w = 100% in Figure 1.
\nSome governments have been talking somewhat about the role of the public in protecting solid waste workers [53]. One of the byproducts of the corona virus epidemic has been a very significant increase in the amount of residential recycling and that includes making waste materials safe. The government recommends that people do not use loose tissues or wipes to clean down something that could impact the workers [10].
\nFrench and Spanish citizens claim that the first challenge at the beginning of the crisis was to state which of their activities was essential, ensuring that all employees who have to be on-site to operate the facilities can circulate freely during lockdown and be able to secure supplies opportunely. Also, subcontractors facilitate the help of authorities in case of problems. The second action concerned, behaviors and work practices. New safety measures should be applied in order to protect workers by adapting the management of operation teams and their way of working and deploying teleworking. For all others, and last but not least, all necessary personal protective equipment should be provided to everybody, which is probably the trickiest point during this crisis [54, 55].
\nWe are in the middle of the storm, which does not allow us to see clearly what is coming. But at present the most important thing is to take care of people’s health and then employment, which means that people have to understand that we are experiencing a crisis.
\nA recurring question relates to the time frame in which we will emerge from this crisis. In this sense, the recovery curve represents the impact of coronavirus on the economy. This can have a V- shape if the stoppage is abrupt and the reactivation is rapid, which would not be the effect of COVID-19 for many countries. If economic indicators take longer to reach the levels they were before the recession, a U- shape may appear. Finally, if the crisis implies a recession in the economy and a very slow recovery, we would be describing the L-shape [56].
\nThere is widespread uncertainty about the economic effects of the crisis [57]. Some authors argue that the recovery will be similar to that which occurred in China between 2005 and 2013, following the tendency of L-shaped growth [58]. This economic effect may be very different in the other countries. In this case, what counts is the adaptation of the population to this “new normality” [59].
\nThis means that people have to adapt quickly, and be flexible in how they proceed. People have to understand that spirits are exacerbated, and act accordingly, both for employees and employers [60]. The second thing is to know that we are in a situation that demands priorities, and here it is the health issue. It would be interesting to measure this problem and understand that very complex weeks are coming, and take measures to try to stop or mitigate the impacts on the environment through the application of waste management policies [44].
\nIn the first part of this chapter we described three dimensions of analysis, now we will briefly delve into the economic dimension. The economic growth and labor issues corresponds to π = 100%, z = 0 and w = 0 in Figure 1. It is useful to recognize waste management workers by definition, formal and informal, in daily contact with waste, who are more vulnerable than other categories of the population to potential health problems.
\nIn this way, one of the key issues that is arising all over the world is the need to protect waste workers, formal or informal, as a special asset that provides an essential service. Not only the International Labour Organization, but also many unions and NGOs are advocating putting the protection of waste workers in the center of our attention in this period. For countries outside of the EU and North America, outside of the high-income countries, most recycling is done by independent or informal recyclists who collect materials simply to feed their families [41].
\nSome countries which have informal recyclers are countries such as South Africa, but also some countries in Europe such as Serbia. There are many people who cannot eat because they are in lockdown. They would normally be picking up recyclables and now they are not allowed to go out. Also people who are in formalized recycling situations. For those in Colombia or Brazil, the government often tells people to stop recycling. At the same time, we even see in the Netherlands a tremendous amount of littering, and so the environment is changing quickly and nobody is really thinking about what these informal workers actually need [61].
\nSome authors claim that recycling is determined by the country’s income level. Low and middle income countries where most recycling happens by independent or informal recyclers, whether formalized as in Brazil and Colombia, or tolerated or semi formalized as in India should have these systems reestablished [62]. What is really important is to figure out a way for them to continue to provide the service that they provide for the city by managing its recyclables and giving the people and their families a way of making a living by thinking actively about how they can safely go about their work [40].
\nWe really hope that the post-crisis crisis will be the momentum for radical change in society worldwide. We also propose that people should be referring to expert guidance, not only from their national and regional governments, but also coming out of the World Health Organization and also for low-and middle-income countries.
\nThe COVID-19 crisis assumes that the effects are divided among economic growth, medical and sustainability. So how do we distribute the effect and the recovery efforts between these dimensions? How to allocate time and humans resources, not mention which trials and which patients?
\nIn this chapter we have not wanted to give specialized answers from an economic, or medical or environmental point of view, but rather to relate these answers to other topics of interest to society.
\nThere is an abundance of guidance to discuss about the economy, and to adapt it for the environment, and economically, an austere fiscal policy, which prioritizes employment and reactivation, through the optimization of resources and efforts.
\nWe need to reprioritize our priorities. What is important and what is not important is central, and this is why we also need to face changes in human behavior. We want to argue that one of the things that seem to be a very important thing is the idea of solidarity. It is just a matter of finding it with respect to health care waste because there is a great deal that has been written and it is just necessary that persons access that which it is suitable for the situation. We can build all this knowledge during this new situation, every one of us.
\nThe government response needs to act on three issues. First, to ensure that essential services are carried out without any interruption. Secondly, to ensure worker’s safety above any other measure, because we need these teams, we need these people, and we need these heroes on the streets and, thirdly, we need to ensure frequent and easy communication to the citizens to stop fake news and to avoid dissemination of misunderstanding.
\nFinally, a good way to face the crisis is to win small battles, in health, through the territorial management of contagions, in the environment avoiding very long-term objectives, and instead targeting more short- and medium-term goals.
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The applications of this research cover many related fields, such as biotechnology and medicine, where, for example, Bioinformatics contributes to faster drug design, DNA analysis in forensics, and DNA sequence analysis in the field of personalized medicine. Personalized medicine is a type of medical care in which treatment is customized individually for each patient. Personalized medicine enables more effective therapy, reduces the costs of therapy and clinical trials, and also minimizes the risk of side effects. Nevertheless, advances in personalized medicine would not have been possible without bioinformatics, which can analyze the human genome and other vast amounts of biomedical data, especially in genetics. The rapid growth of information technology enabled the development of new tools to decode human genomes, large-scale studies of genetic variations and medical informatics. The considerable development of technology, including the computing power of computers, is also conducive to the development of bioinformatics, including personalized medicine. In an era of rapidly growing data volumes and ever lower costs of generating, storing and computing data, personalized medicine holds great promises. Modern computational methods used as bioinformatics tools can integrate multi-scale, multi-modal and longitudinal patient data to create even more effective and safer therapy and disease prevention methods. Main aspects of the topic are: Applying bioinformatics in drug discovery and development; Bioinformatics in clinical diagnostics (genetic variants that act as markers for a condition or a disease); Blockchain and Artificial Intelligence/Machine Learning in personalized medicine; Customize disease-prevention strategies in personalized medicine; Big data analysis in personalized medicine; Translating stratification algorithms into clinical practice of personalized medicine.",annualVolume:11403,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/7.jpg",editor:{id:"351533",title:"Dr.",name:"Slawomir",middleName:null,surname:"Wilczynski",fullName:"Slawomir Wilczynski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035U1loQAC/Profile_Picture_1630074514792",institutionString:null,institution:{name:"Medical University of Silesia",institutionURL:null,country:{name:"Poland"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"5886",title:"Dr.",name:"Alexandros",middleName:"T.",surname:"Tzallas",fullName:"Alexandros Tzallas",profilePictureURL:"https://mts.intechopen.com/storage/users/5886/images/system/5886.png",institutionString:"University of Ioannina, Greece & Imperial College London",institution:{name:"University of Ioannina",institutionURL:null,country:{name:"Greece"}}},{id:"257388",title:"Distinguished Prof.",name:"Lulu",middleName:null,surname:"Wang",fullName:"Lulu Wang",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRX6kQAG/Profile_Picture_1630329584194",institutionString:"Shenzhen Technology University",institution:{name:"Shenzhen Technology University",institutionURL:null,country:{name:"China"}}},{id:"225387",title:"Prof.",name:"Reda R.",middleName:"R.",surname:"Gharieb",fullName:"Reda R. Gharieb",profilePictureURL:"https://mts.intechopen.com/storage/users/225387/images/system/225387.jpg",institutionString:"Assiut University",institution:{name:"Assiut University",institutionURL:null,country:{name:"Egypt"}}}]},{id:"8",title:"Bioinspired Technology and Biomechanics",keywords:"Bioinspired Systems, Biomechanics, Assistive Technology, Rehabilitation",scope:'Bioinspired technologies take advantage of understanding the actual biological system to provide solutions to problems in several areas. Recently, bioinspired systems have been successfully employing biomechanics to develop and improve assistive technology and rehabilitation devices. The research topic "Bioinspired Technology and Biomechanics" welcomes studies reporting recent advances in bioinspired technologies that contribute to individuals\' health, inclusion, and rehabilitation. Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',annualVolume:11404,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",institutionString:null,institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"49517",title:"Prof.",name:"Hitoshi",middleName:null,surname:"Tsunashima",fullName:"Hitoshi Tsunashima",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTP4QAO/Profile_Picture_1625819726528",institutionString:null,institution:{name:"Nihon University",institutionURL:null,country:{name:"Japan"}}},{id:"425354",title:"Dr.",name:"Marcus",middleName:"Fraga",surname:"Vieira",fullName:"Marcus Vieira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003BJSgIQAX/Profile_Picture_1627904687309",institutionString:null,institution:{name:"Universidade Federal de Goiás",institutionURL:null,country:{name:"Brazil"}}},{id:"196746",title:"Dr.",name:"Ramana",middleName:null,surname:"Vinjamuri",fullName:"Ramana Vinjamuri",profilePictureURL:"https://mts.intechopen.com/storage/users/196746/images/system/196746.jpeg",institutionString:"University of Maryland, Baltimore County",institution:{name:"University of Maryland, Baltimore County",institutionURL:null,country:{name:"United States of America"}}}]},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. 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