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CHAPTER 2

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CHAPTER 2

LITERATURE SURVEY

 

2.1 INTRODUCTION

Manufacturing Cell Formation (MCF) is an approach that helps in producing a variety of products with minimum possible waste. A manufacturing cell is a group of workstations, machine tools and types of equipment arranged for processing of part families from one workstation to another without waiting for a batch to be completed or requiring additional handling between operations. The cellular manufacturing groups together the needed machinery and a team of staff, so that processing on a product can be accomplished in the same cell eliminating unnecessary resources. The main goal of the manufacturing cell formation system is to bring together the advantages of both the flow shop and the job shop production and to simplify the production control. This chapter gives an account of the brief history of earlier works with a detailed description of the core algorithms, highlighting the significant contributions of the different algorithms put forth by various authors. This research work aims at giving an overview of the previous researches done in this field, evaluating the current status of the job done and envisioning gaps in the current knowledge. A comprehensive survey of the most powerful algorithms of literature has been explained and compared with them in the horizontal and vertical data layouts.

The Manufacturing Cell Formation widely applied in metal fabrication, computer chip manufacturing, and assembly work. It involves a product-oriented layout, which allocates different machines into cells to work on the product having similar processing requirements. With this arrangement, the total machine set up time required (to process-related parts) reduced. This provides the opportunity for simplification of processes and reduction in lot size, which leads to a reduction in work in process, inventory, and shorter manufacturing lead time. Other benefits of cellular manufacturing include a high level of job satisfaction and better management control, since responsibilities are product based, reflecting the team performance. In this chapter, the various existing methods with their corresponding methodologies explained briefly.

The Manufacturing Cellular Formation creates coordination needs that cannot obtained through the traditional production planning system. The system needs concern (a) determining families of parts and (b) handling components in the same cell and dependent cells. Several CMS design approaches ranging from simple to sophisticated techniques have suggested for the formation of manufacturing cells and part family. A few of them include:

  • Classification and Coding
  • Array-Based Clustering
  • Graph Partitioning
  • Similarity Coefficient Approach
  • Mathematical Programming
  • Metaheuristic

The simple CMS design technique usually manipulates part-machine matrices. The sophisticated ones can handle many constraints, such as maximum cell size, demand size for different products, number of cells and set-up cost. Most of the approaches assume the part demand to stay constant over long periods. The conventional Manufacturing Cell Formation systems (MCF) consider the product mix and part demand to be stable for the entire planning span and do not respond to the changes in part operation sequence while redesigning a part, and the variation in product mix and the demand size over some time. Thus, the CMS configuration designed for one period may not be efficient for the successive period.

The robust manufacturing cell formation design for dynamic production considered with several manufacturing attributes such as machine procurement cost, machine operating cost, production cost, and material handling cost, and subcontracting cost, simultaneously. The cell formation offers flexibility in production planning such as production and outsourcing, which can achieve by producing product mixes at each period segment of planning horizon within the limiting production capacity without affecting the manufacturing cell formation configuration.

 

2.2 SURVEY ON MANUFACTURING CELL FORMATION

            The Manufacturing Cell Formation Problem (MCFP) is an application of group technology to organize cells containing a set of machines to process a family of parts. The MCFP involves the creation of an optimal design of production plants, in which the main objective is to minimize the movement and exchange of material between these cells, thus generating greater productivity and reducing production costs. The problem of cell formation has followed two complementary lines, which can be organized into two groups: approximate methods and exact methods. Approximate methods are mostly focused on finding an optimal solution in a limited time; however, they do not guarantee a global optimum. Exact methods, on the contrary, aim to fully analyze the search space to ensure a global optimum; however, these algorithms are quite time-consuming and can only solve cases of very limited size. For this reason, many research efforts have focused on the development of heuristics, which find near-optimal solutions within a reasonable period of time. The idea is to represent the processing requirements of machine parts through an incidence matrix called machine part. The goal of MCFP is to identify a cell formation in a way that minimizes the transport of different parts between cells, in order to reduce production costs and increase productivity.

            Kesavan et al. 2020 author proposed the cellular manufacturing systems (CMSs) have emerged to cope with such production requirements and have been implemented with favorable performance. Designing and implementing effective CMS involves many problems such as cell formation, machine layout, alternative process routes and inventory lot sizing. To review different heuristic, meta-heuristic, hybrid and exact solution algorithms developed to solve NP-hard problems associated with medium and large sized CMS problems. Hence, a robust meta-heuristic algorithm with hybridization is essential to address multiple CMS problems simultaneously.

Yang et al. 2020 proposed the state-of-the-art meta-heuristic algorithms and related variants applied on PEMFC parameter estimation, which has greatly enhanced the diversity of algorithms. These algorithms are classified into four categories, e.g., biology-based, physics-based, sociology-based and mathematics-based, upon which readers can systematically utilize these approaches to deal with PEMFC parameter estimation. The various evaluation criteria, specific experimental performance, and other estimation techniques are also thoroughly discussed.

Bouaziz, Berghida & Lemouari 2020 proposed the cell formation problem, which is the Generalized Cubic Cell Formation Problem (GCCFP). In this study, a mathematical model is developed for this variant of the problem. Besides the multiple objectives considered in most research works, the quality index of the produced parts is also considered in this study. To solve the problem, a Discrete Flower Pollination Algorithm (DFPA) is developed. To validate the model and the DFPA, a set of randomly generated instances were solved using B&B under LINGO software, DFPA and Simulated Annealing (SA) algorithm. The performance of DFPA, from the standpoint of the considered objectives and the time of calculation.

Rahimi, Arkat & Farughi 2020 proposed the three problems of cell formation (CF), cellular scheduling, and cellular layout are closely interrelated in the design of cellular manufacturing systems (CMSs) and should, therefore, be considered in an integrated structure. In the presented model, decisions including machine grouping, processing route selection, operation sequencing, and cell assignment into candidate locations are taken such that the total completion time is minimized. Since the problem in hand belongs to the NP-hard class, a vibration damping optimization (VDO) algorithm is proposed to solve large-sized problems. In order to verify the efficiency of the proposed algorithm comparing to the CPLEX solver of the GAMS software and two other meta-heuristic algorithms, namely a genetic algorithm (GA) and an ant lion optimizer (ALO) algorithm, several sample problems with different sizes and settings are implemented.

Yang et al. 2020 proposed the Accurate parameter identification is crucial for a precise PV cell modelling and analysis of characteristics of PV systems, while high nonlinearity of output I-V curve makes this problem extremely thorny. Due to the rapid advancement of computer technology and swarm intelligence, various promising meta-heuristic algorithms have been proposed to further accelerate this trend. This paper aims to undertake a comprehensive review on meta-heuristic algorithms and related variants which have been applied on PV cell parameter identification. Particularly, these algorithms are classified into four categories, e.g., biology-based algorithms, physics-based algorithms, sociology-based algorithms and mathematics-based algorithms.

Forghani, Ghomi & Kia 2020 proposed to integrating the cell formation, group layout of rectangle-shaped machines and routing selection problems. The problem is formulated as a mixed-integer program with the objective of minimizing the handling costs. Due to the computational complexity of the problem, a hybrid simulated annealing (SA) is employed to solve the problem. The sequence-pair representation, originally proposed for block placement, is utilized for solution encoding. Two placement algorithms are developed to evaluate the objective function value of an encoded solution in the SA. The computational results demonstrated the high performance of both designed hybrid SA algorithms.

Müller 2020 proposed the large-scale industrial production, making PV cost competitive with other means of electricity generation to be achieved. In many regions worldwide, PV achieves the lowest levelized cost of electricity. Several different factors made this tremendous achievement possible-namely economy of scale, a lean and efficient production process, and high conversion efficiencies. In this work, some of the key concepts and methods are described based on Hanwha Q CELLS’ experience. The methods and approaches for the fast transfer of cell technologies from laboratory to production and for accelerated progress in cell efficiency, quality, and reliability of the cell and module product are described. Over the last decade, the cell conversion efficiency increased by 0.5%abs per year. Currently, average cell conversion efficiencies exceeding 20% using boron-doped p-type multicrystalline (mc-Si) and 22%, using Czochralski-grown silicon (Cz-Si) substrates, are achieved on a multi-GW scale.

Bortolini et al. 2020 proposed the adoption of reconfigurable systems represents a primary strategy to improving flexibility, elasticity and efficiency in both manufacturing and assembly. Global markets, the increasing need for customization, high-quality standards, dynamic batches and short life cycles are the key factors driving the transition from traditional to reconfigurable manufacturing systems (RMSs). Despite their automation level, such systems still require actions by human operators, e.g. material handling, WIP load/unload, tool setup, etc. These operations rise safety issues because of the human–machine interaction and cooperation. Particularly, RMSs require changes of auxiliary modules and tools, based on the manual intervention, to achieve effective system configurations enlarging the produced mix for the industrial perspective and applicability.

Knauer, Stiehl & Marciniak-Czochra 2020 proposed the occurrence of a super-critical Hopf bifurcation in a model of white blood cell formation structured by three maturation stages. We provide an explicit analytical expression for the bifurcation point depending on model parameters. The Hopf bifurcation is a unique feature of the multi-compartment structure as it does not exist in the corresponding two-compartment model. It appears for a parameter set different from the parameters identified for healthy hematopoiesis and requires changes in at least two cell properties. Model analysis allows identifying a range of biologically plausible parameter sets that can explain persistent oscillations of white blood cell counts observed in some hematopoietic diseases. Relating the identified parameter sets to recent experimental and clinical findings provides insights into the pathological mechanisms leading to oscillating blood cell counts.

Qais, Hasanien & Alghuwainem 2020 proposed the meta-heuristic whale optimization algorithm (WOA) for maximum power point tracking (MPPT) of variable-speed wind generators. First of all, twenty-three benchmark functions tested the enhanced whale optimization algorithm (EWOA). Then the statistical results of EWOA compared with the results of other algorithms (WOA, salp swarm algorithm (SSA), enhanced SSA (ESSA), grey wolf optimizer (GWO), augmented GWO (AGWO), and particle swarm optimization (PSO). Also, the non-parametric statistical test and convergence curves proved the superiority and the speed of the EWOA. After that, the EWOA and WOA are implemented to design optimal Takagi–Sugeno fuzzy logic controllers (FLCs) to enhance the MPPT control of variable-speed wind generators. Moreover, real wind speed data has confirmed the robustness of optimal EWOA-MPPT.

Kant, Pattanaik & Pandey 2020 proposed the manufacturing model for integrating reconfigurable machine cells as feeders of components to a multi-product assembly line exhibiting a prominent lean characteristic; synchronization with Takt time. Two mathematical formulations in the form of combinatorial optimization problems are developed to identify the best reconfiguration for the machines by optimum selection of the modules in the cells to minimize the total error with Takt time and then the optimum sequence of assembling the products to minimize the total reconfiguration time and effort. An elitist genetic algorithm (GA) meta-heuristic is employed twice in succession to search the optimal solutions for both the minimization problems. The best machine configuration found from the first search is utilized further to identify the optimal assembly sequence. A hypothetical data set representing the features and parameters of the model in conformance with any typical mixed-model assembly is presented for illustration of computational procedure. Synchronization among machine cells and tardiness are considered as performance evaluation measures to validate the effectiveness of the proposed model.

Zhang, Xiao & Razmjooy 2020 proposed the optimal modeling and simulating a proton exchange membrane fuel cell (PEMFC) system to assure dependable modeling. The main idea is to utilize a newly developed meta-heuristic, called Chaos Owl Search Algorithm (COSA) to optimal selecting of the model parameters of the PEMFC stacks by minimizing the Sum of Squared Error (SSE) between the estimated and the measured output voltage for two different case studies. By applying 50 independent runs with the algorithm, it is analyzed and compared with some literature meta-heuristics including Bat Algorithm (BA) Firefly algorithm (FFA), and Multi-verse optimizer (MVO) in terms of convergence speed and minimum SSE that produced the best convergence speed.

Ghasemi-Marzbali  2020 proposed the nature-inspired meta-heuristic algorithm for optimization which is called as bear smell search algorithm (BSSA) that takes into account the powerful global and local search operators. The proposed algorithm imitates both dynamic behaviors of bear based on sense of smell mechanism and the way bear moves in the search of food in thousand miles farther. Among all animals, bears have inconceivable sense of smell due to their huge olfactory bulbs that manage the sense of different odors. Since the olfactory bulb is a neural model of the vertebrate forebrain, it can make a strong exploration and exploitation for optimization. According to the odors value, bear moves the next location. Therefore, this paper mathematically models these structures. To demonstrate and evaluate the BSSA ability, numerous types of benchmark functions and four engineering problems are employed to compare the obtained results of BSSA with other available optimization methods with several analyzed indices such as pair-wise test, Wilcoxon rank and statistical analysis.

Ayough, Zandieh & Farhadi 2020 proposed the Performance of a manufacturing cell is dependent on an efficient layout design, and optimal work schedules. However, the operator-dependent factors such as learning, forgetting, motivation, and boredom, can considerably impact the output of the system. The heterogeneous operators with dynamic performance metrics and integrate the job assignment, and job rotation scheduling problems, with the balancing and production sequencing in a U-shaped lean manufacturing cell. The novel multi-period nonlinear mixed-integer model to minimize the deviations from takt time, and the number of operators, in a finite planning horizon. An efficient meta-heuristic approach is developed to solve the problem and the results are compared to a static case where no human factor is included. Our computational results demonstrate that including the operator-dependent metrics can improve the performance of the cell design. The sensitivity analysis of the scheduling parameters including, rotation frequencies, takt time, cell size, and task types, and derive that the obtained solutions with the static settings, are not sufficient for an efficient lean cell design in the presence of dynamic human factors.

Yang et al. 2020 proposed the Precise and reliable modeling of solid oxide fuel cells (SOFC) is critical for simulation analysis and optimal control of SOFC systems, which typically relies on an accurate identification of its unknown parameters. However, such problem is characterized by high non-linearity, multi-variable, and strong non convexity, thus conventional strategies cannot always achieve satisfactory performance. The combination of various effective methods is crucial for novel parameter identification techniques development, upon which more reliable and efficient approaches can be devised for better simulation analysis and optimal control of SOFC systems. Besides, normalized models on such problem are also needed to be established for more accurate performance evaluation and prediction.

            Long & Zhao 2020 proposed the Movie scenes scheduling problem (MSSP) is the NP-hard. It refers to the process of film shooting through the reasonable sequence of film scenes to minimize the total cost of film shooting. an ILP(integer linear programming) model is established and a TABU search based method (TSBM), a particle swarm optimization based method (PSOBM) and an ant colony based method (ACOBM) are used to study the movie scenes scheduling problem. The objective is to compare and analyze relation performances of the three methods in the problem. By compared the experiment, the results show that TSBM, PSOBM and ACOBM can effectively reduce the total cost of film shooting. Comparison with the experiments show that the optimization result of ACOBM is better, the running time of TSBM is shorter, and the optimization result of TSBM is better than that of PSOBM.

Shin et al. 2020 proposed the an integrated batching and scheduling problem for a single-machine flexible machining cell in which each pallet can load multiple parts, i.e. multi-fixturing pallets, and part processing times can be changed with different processing costs, i.e. controllable processing times. The batching sub-problem is to select the set of parts to be produced in each period of a planning horizon and the resulting scheduling sub-problem is to determine the set of parts to be loaded on each multi-fixturing pallet, the part processing times and the pallet input/processing sequences for the parts selected in each period.  A solution approach is proposed that consists of three phases from the first to the last period: (a) generating the whole schedule over the planning horizon; (b) selecting the parts to be produced during the current period using the scheduling information; and (c) determining the final schedule for the selected parts.

Xue & Offodile 2020 proposed a non-linear mixed integer programming (MIP) model that integrates dynamic cell formation (DCF) and hierarchical production planning (HPP). In the model, the DCF problem optimizes reconfiguration of machine cells, with varying production quantities in different periods determined by the HPP model. The HPP problem, formulated as an integrated model, determines the optimal production plans that meet forecast demands in the planning horizon, with capacity limitations of the machine cells formed through the DCF model. Compared with prior studies that integrate DCF and production planning (PP) problems, which provides the most comprehensive options needed to meet demands in dynamic cellular manufacturing systems (DCMS). With the introduction of HPP, the model could incorporate more decision variables such as inventory, internal production, subcontracting and backlogging costs, inter- and intra-cell material handling costs, yet with less solution complexity through the branch-and-bound method, and its complexity to be analyzed.

Ajagekar, Humble & You 2020 proposed the hybrid models and methods that effectively leverage the complementary strengths of deterministic algorithms and Quantum computing (QC) techniques to overcome combinatorial complexity for solving large-scale mixed-integer programming problems and the applications such as molecular conformation problem, job-shop scheduling problem, manufacturing cell formation problem, and the vehicle routing problem, are specifically addressed. Large-scale instances of these application problems across multiple scales ranging from molecular design to logistics optimization are computationally challenging for deterministic optimization algorithms on classical computers. To address the computational challenges, hybrid QC-based algorithms are proposed and extensive computational experimental results are presented to demonstrate their applicability and efficiency. The proposed QC-based solution strategies enjoy high computational efficiency in terms of solution quality and computation time, by utilizing the unique features of both classical and quantum computers.

Bortolini et al. 2020 proposed the Global markets, the increasing need for customization, high-quality standards, dynamic batches and short life cycles are the key factors driving the transition from traditional to reconfigurable manufacturing systems (RMSs). Despite their automation level, such systems still require actions by human operators, e.g. material handling, WIP load/unload, tool setup, etc. These operations rise safety issues because of the human–machine interaction and cooperation. Particularly, RMSs require changes of auxiliary modules and tools, based on the manual intervention, to achieve effective system configurations enlarging the produced mix. In this field, embracing the emerging Industry 4.0 technology, a lack of procedures and reference approaches exists to supporting companies and practitioners in analyzing the impact on safety and ergonomics coming from the switch from standard to RMSs.

Sadeghi et al. 2020 proposed the manufacturing system design based on layered Cellular Manufacturing System (CMS) for which a mixed-integer linear programming approach is proposed in order to minimize the required number of cells. The novelty of the proposed model is in incorporating shifts into layered CMS design. In the second step of the design phase inventory parameters in different levels of the supply chain are estimated based on the continuous review, fixed order quantity (Q,r) model. Control phase is triggered by releasing customer order for multiple types of products and includes replenishments in the three echelons of the supply chain and scheduling in the layered CMS. In order to evaluate the system cost, the capacitated supply chain system is simulated in Simio software package. The simulation based on OptQuest feature is performed to improve total cost and estimate the inventory parameters and expected cell utilization. Comparing system cost and the parameters from the design phase to the results of the OptQuest approach evaluates the manufacturing system design and the replenishment policy proposed in the design phase.

Xu et al. 2020 proposed the pulse wire arc additive manufacturing (PWAAM) method to fabricate large-size pyramidal lattice cells. This method greatly improved the material utilization rate and achieved unsupported manufacturing compared with other additive manufacturing methods. The overall experimental process and the forming principle are analyzed. The experiment aims to fabricate multi-angle pyramid lattice cells. The manufacturing process of inclined rods in each direction is studied, and the manufacturing process of the whole cell analyzed as well. The molding mechanism and droplet transfer modes in the forming process are discussed. The optimum forming angle is between 45° and 90°. Finally, the PWAAM method was successfully used to fabricate the pyramid lattice structure.

Pattanaik 2020 proposed the reconfigurable manufacturing system (RMS) is designed at the outset with the capability of rapid adjustment of production capacity and functionality in response to fluctuations in product demand. This paper is presenting a model of RMS containing reconfigurable/modular machines assembled from sets of basic and auxiliary modules to exhibit two key characteristics: a defined range of functionality and scalable capacity. The products with the cell formation based alternative process plans and two discrete levels (low and high) of capacity requirements are considered for the modular machines. The objective of the work is to identify the best production sequence and respective process plans in order to minimize the total number of module changes while fulfilling the capacity constraint. Self-organizing migrating algorithm (SOMA) an evolutionary migration algorithm-based search is applied to find the near-optimal solution for the NP-hard combinatorial optimization problem. The approach is illustrated through a numerical problem along with computational results as applied to a hypothetical RMS model.

Saez-Mas et al.  2020 proposed the cell assignment problem in an assembly facility. These cells receive parts from external suppliers, and sort and sequence these parts to feed the final assembly line. Therefore, to each cell are associated important inbound and outbound flows generating hundreds of material handling equipment movements along the facility, impacting the traffic density and causing eventually safety issues in the plant. A hybrid approach encompassing mathematical optimization and discrete event simulation (DES) is proposed that allows us to reduce complexity by decomposing the design. The problem of generating cell’s assignment alternatives by using a heuristic method to find good quality solutions. Then, DES software is used to dynamically evaluate the performance of the solutions with respect to operational features such as traffic congestion and intensity. This second phase provides interesting managerial insights on the manufacturing system from both quantitative and qualitative aspects related to in-plant safety and traffic.

Rai, & Bajpai 2020 proposed the  Particle swarm optimization and Genetic algorithm to optimize the various kind of manufacturing system with an objective to overcome the limitations of traditional optimization techniques and to enhance the optimality of objective function. Besides that, customary methodology is to utilize an ordinary least squares relapse investigation for building up the machinability models. In recent decade, the utilization of evolutionary calculation techniques, or additionally called the Genetic strategies, in light of impersonation of Darwinian characteristic choice has turned out to be across the board. This is because of truth that numerous frameworks are excessively complex, making it impossible to be effectively enhanced by the utilization of traditional deterministic calculations. Despite what might be expected, the evolutionary algorithms (EA) include probabilistic tasks. The current chapter also presents brief details about stepwise procedure of implementation of genetic algorithm and particle swarm optimization to solve various problems associate with manufacturing cell formation systems.

Najid, Castagna & Kouiss 2020 proposed the critical review of the existing manufacturing paradigms, which are the dedicated manufacturing lines (DMLs) and the flexible manufacturing systems (FMSs), reveals that these systems are not capable of fulfilling the requirements imposed by the current market; these requirements are mainly resumed in cost, quality and reactivity. Reconfigurable manufacturing system or RMS is this new paradigm; it is supposed to be reactive enough to cope with the sudden changes in the market while keeping the products’ quality high at a low cost. The main challenge in RMS is their design. Most of the suggested methods in the literature do not address the RMS design issue as a whole; they treat just a part of the problem. Hence, a generic RMS design based cell manufacturing methodology based on systems engineering (SE).

Mahmoodian et al. 2019 proposed the formation of manufacturing cells forms the backbone of designing a cellular manufacturing system. In this paper, we present a novel intelligent particle swarm optimization algorithm for the cell formation problem. The proposed solution method benefits from the advantages of particle swarm optimization algorithm (PSO) and self-organization map neural networks by combining artificial individual intelligence and swarm intelligence. Numerical examples demonstrate that the proposed intelligent particle swarm optimization algorithm significantly outperforms PSO and yields better solutions than the best solutions existed in the literature of cell formation. The application of the proposed approach is examined in a case problem where real data is utilized for cell reconfiguration of an actual company involved in agricultural manufacturing field.

Ulutas 2019 proposed the Cellular Manufacturing Systems (CMS) can be considered as to ease flexibility, to reduce setup time, throughput time, work-in-process inventories, and material handling costs. Cell formation problem (CFP) that is one of the critical CMS design problems is the assignment of parts and machines to specific cells based on their similarity. The Clonal Selection Algorithm (CSA) with a novel encoding structure that is efficient to solve real-sized problems. Unlike the methods in literature that define the number of cells as a constant number, this algorithm is significant because it can obtain the optimum number of cell to generate best efficacy value. Proposed CSA is tested by using 67 test problems. CSA obtains the same 63 best-known optimal solutions, provides solutions for the 3 of the well-known test problem and a new solution for the largest test problem that was not possible to be solved by the mixed integer linear programming model due to the high computational complexity.

Nalluri et al. 2019 proposed the cellular manufacturing technology, an application of group technology in manufacturing, has been a widely studied combinatorial optimization problem where the entire production system is divided into many cells and part families. In this paper, a novel clonal selection algorithm (CSA) that uses a new affinity function and part assignment heuristic for solving a multi-objective cell formation problem is studied. The proposed CSA has been hybridized with genetic algorithm for generating feasible cell sequences that fulfill both mutual exhaustivity and exclusion properties of machine cells prior to the initial population generation. Additionally, a new part assignment heuristic function that maps parts to machine cells and a novel basic affinity function have been built into the proposed CSA so that it can act as the utility function to solve the multi-objective cell formation problem. This hybrid CSA (HCSA) provides the Extensive statistical and convergence tests.

Feng et al. 2019 proposed the concurrent design of cell formation and scheduling is an effective method for better implementing cellular manufacturing. To address the integrated cell formation and scheduling problem, a nonlinear mixed integer programming mathematical model is developed in this paper. This newly proposed model features the simultaneous consideration of many design attributes, such as duplicate machines, alternative process routings, reentrant parts and variable cell number. Several linearization techniques are proposed to transform it into a mixed integer linear programming formulation. An improved genetic algorithm (IGA) is developed to solve large-scale problems efficiently. To remove redundancy between two chromosomes, a cell renumbering procedure is applied in IGA. The integration of cell formation and scheduling can remarkably reduce the flow time of cellular manufacturing systems to be solved. A set of thirteen test problems with various scale is used to further evaluate the performance of IGA.

Almonacid & Soto 2019 proposed the population based optimization algorithm called Andean Condor Algorithm (ACA) for solving cell formation problems. The ACA metaheuristic is inspired by the movement pattern of the Andean Condor when it searches for food. This pattern of movement corresponds to the flight distance traveled by the Andean Condor from its nest to the place where food is found. This distance varies depending on the seasons of the year. The ACA metaheuristic presents a balance of its population through a performance indicator based on the average quality of the population’s fitness. This balance determines the number of Andean Condors that will perform an exploration or intensification movements. ACA metaheuristics have a flexible design. It allows to easily integrating specific heuristics according to the optimization problem to be solved. Two types of computational experiments have been performed. To determine that ACA is an algorithm with an outstanding RPD% in relation to the algorithms BAT, MBO and PSO, robust and with a convergence which tends not to be trapped in the local optimums obtained. Besides, according to the non-parametric multiple comparisons, results have been obtained in which the ACA metaheuristic has significant differences in relation to the BAT, MBO and PSO algorithms.

Wang, Liu & Li 2019 proposed the linear mathematical model for integrated cell formation and task scheduling in the cellular manufacturing system (CMS). It is suitable for the dual-resource constrained setting, such as garment process, component assembly, and electronics manufacturing. The model can handle the manufacturing project composing of some tasks with precedence constraints. It provides a method to assign the multi-skilled workers to appropriate machines. The workers are allowed to move among the machines such that the processing time of tasks might be reduced. A hybrid simulated annealing (HSA) is proposed to minimize the makespan of manufacturing project in the CMS. The approach combines the priority rule based heuristic algorithm (PRBHA) and revised forward recursion algorithm (RFRA) with conventional simulated annealing (SA). The result of extensive numerical experiments shows that the proposed HSA outperforms the conventional SA accurately and efficiently.

Rabbani, Farrokhi-Asl & Ravanbakhsh 2019 proposed a new multi-objective mathematical model for dynamic cellular manufacturing system (DCMS) is provided with consideration of machine reliability and alternative process routes. In this dynamic model, we attempt to resolve the problem of integrated family (part/machine cell) formation as well as the operators’ assignment to the cells. The first objective minimizes the costs associated with the DCMS. The second objective optimizes the labor utilization and, finally, a minimum value of the variance of workload between different cells is obtained by the third objective function. Due to the NP-hard nature of the cellular manufacturing problem, the problem is initially validated by the GAMS software in small-sized problems, and then the model is solved by two well-known meta-heuristic methods including non-dominated sorting genetic algorithm and multi-objective particle swarm optimization in large-scaled problems.

Zhou et al. 2019 proposed the new generation information technologies, such as big data analytics, internet of things (IoT), edge computing and artificial intelligence, have nowadays driven traditional manufacturing all the way to intelligent manufacturing. Intelligent manufacturing is characterized by autonomy and self-optimization, which proposes new demands such as learning and cognitive capacities for manufacturing cell, known as the minimum implementation unit for intelligent manufacturing. The proposed general framework for knowledge-driven digital twin manufacturing cell (KDTMC) towards intelligent manufacturing, which could support autonomous manufacturing by an intelligent perceiving, simulating, understanding, predicting, optimizing and controlling strategy. Three key enabling technologies including digital twin model, dynamic knowledge bases and knowledge-based intelligent skills for supporting the above strategy are analyzed, which equip KDTMC with the capacities of self-thinking, self-decision-making, self-execution and self-improving. The implementing methods of KDTMC are also introduced by a thus constructed test bed. Three application examples about intelligent process planning, intelligent production scheduling and production process analysis and dynamic regulation demonstrate the feasibility of KDTMC, which provides a practical insight into the intelligent manufacturing paradigm.

Méndez-Vázquez & Nembhard 2019 proposed the worker-cell assignment of heterogeneous workers in various cellular manufacturing structures while considering between-cell heterogeneity, cell size, and system size as organizational factors. Workforce heterogeneity is considered based on individual learning characteristics, which include individual learning by doing and learning by knowledge transfer. However, research related to the worker-cell assignment considering workforce heterogeneity and knowledge transfer is scarce. The different organizational factors are investigated to evaluate their effects on system performance and their relevance for the worker-cell assignment problem currently. This work contributes to the development of managerial insights to assist organizational managers in workforce management decisions in scenarios where more complex mathematical optimization methods are impractical.

Souier, Dahane & Maliki 2019 proposed the routing flexibility is one of the most common types of flexibilities of manufacturing systems. It allows the system to continue producing given part types despite uncertainties. Its main purpose is to maintain a high level of performance so that the system can deal with disturbances. However, due to resource and alternative routing limitations, the scheduling problems in such systems can become very complex. To present the scheduling problem in a flexible manufacturing system (FMS) with routing flexibility under uncertainties related to the random arrival of parts orders and machines failures, by considering reliability and maintenance constraints. The real-time decisions for part routing selection are made using a non-dominated sorting genetic algorithm (NSGA-II), by considering the workload, utilization level, and reliability of machines in a workstation, in order to minimize the deadlocks and maximize the overall system reliability. The simulation results obtained showed that, for an overloaded system, the proposed NSGA-II algorithm induces the best performance in terms of total profit, system productivity, and machines utilization.

Huang & Yan 2019 proposed the Reconfigurable manufacturing system (RMS) is designed around part family providing exact production function and capacity in cost-effective way when needed. Besides the grouping accuracy of part family impacting the responsiveness of RMS, the efficiency problem of RMS resulting from the difference of process time and capacity demand should be solved. Therefore, a similarity coefficient method for RMS part family grouping considering process time and capacity demand is proposed. First, the longest common subsequence (LCS) among different part process routes is extracted and the shortest composite super sequence (SCS) of parts is constructed. Idle machine (IM) and bypass move (BPM) are analyzed based on SCS. The characteristic value sequences of process route, LCS, SCS, IM and BPM are gained, that is, TDP, TDLCS, TDSCS, TDIM and TDBPM respectively. Based on the similarity matrix, the netting clustering algorithm is used for clustering to complete the part family grouping. Finally, a case study is presented to implement the proposed method and validate the effectiveness.

Cheng et al. 2019 proposed the Task Allocation (TA) approach can give an optimized arrangement of existing resources, enable manufacturing system’s flexibility, and thus improve both economic performance and social benefits. However, there is still no uniform analysis on TA to date, while it has been paid more attention from the view of manufacturing resource allocation. With the application of advanced information and manufacturing technologies, the TA process improved with intelligence or even smartness could respond to demand changes rapidly and maintain a good balance for supply-demand matching issues. In this paper, TA and its intelligent improvements are picked and investigated. The general workflow of TA is divided into six stages: task description and modelling, analysis and modelling of TA process, algorithm design and selection for TA, decision-making of TA, simulation, and task execution. Each stage is separately analyzed at first. In particular, the decision-making process of TA consists of two approaches: the traditional way of system-oriented process (SoP), and the task-oriented process (ToP).

Kumar & Singh 2019 proposed the globalization, product demand and product mix disparate frequently. In addition, entry of new product and deletion of existing product due to changing market scenarios make the manufacturing environment uncertain. In the uncertain manufacturing environment, a facility layout design must be capable to handle all these changes while keeping minimal material handling cost (MHC) and re-configuration cost. The proposed  novel of modified simulated annealing (modified SA) approach to solving bi-objective robust stochastic cellular facility layout problem (RSCFLP). The RSCFLP minimizes material handling distance (MHD) and thus MHC and maximizes similarity score for multi-periods and provides a robust layout design considering stochastic demand for multi-periods. The proposed robust layout design suits for all time periods and avoids re-configuration cost. The proposed modified SA is tested using twenty-five data sets with a varying number of machines, products, cells, time periods, and product demand.

Zandieh 2019 proposed the Virtual cellular manufacturing system (VCMS) is one of the modern strategies in the production facilities layout, which has attracted considerable attention in recent years. In this system, machines are located in different positions on the shop floor and virtual cells are a logical grouping of machines, jobs, and workers from the viewpoint of the production control system. These features not only enhance the system’s agility but also allow a dynamic reassignment of cells as demand changes. The VCMS scheduling problems where the jobs have different orders on machines and the objective is to simultaneously minimize the weighted sum of the makespan and total traveling distance in order to create a balance between criteria. The research methodology firstly consists of a mathematical programming model with regard to the production constraints in order to describe the characteristics of the VCMS. Secondly, a basic genetic algorithm (GA), a biogeography-based optimization (BBO) algorithm, an algorithm based on hybridization of BBO and GA, and the BBO algorithm accompanied by restart phase are developed to solve the VCMS scheduling problems.

Zeng, Tang & Fan 2019 proposed the cell part scheduling (CPS) problem with transportation capacity constraint. In the problem, parts may need to visit different cells; they have to be transferred by automated guided vehicle (AGV). The objective is to minimize the over-all process make-span. An integer nonlinear programming (INLP) model is formulated to allocate the machines, AGVs and schedule all parts. A reasonable transportation mode is presented, and an auction-based heuristic approach is proposed to solve the problem, which focuses on dealing with cooperation between machines and AGVs during processing and transferring parts. The auction consists of two aspects: auction for AGV and auction for machine. In both auctions, AGVs and machines act as auctioneers respectively, and parts act as bidders. A new improved disjunctive graph model is developed to optimise the feasible solutions obtained by auction-based approach. Numerical experiments were conducted to test the auction-based approach and improved disjunctive graph model. The results demonstrate the effectiveness of proposed auction-based approach and improved disjunctive graph model, also indicate influence of the capacity of AGV on scheduling parts.

Chen & Tiong 2019 proposed an automated guided vehicle-based flow production system is used for manufacturing prefabricated bathroom units. One unit can occupy a space of more than 10 m2. Due to large time deviations in sequential processes, queues are formed and greater plant space is needed. Reducing work-in-progress helps to save plant space but renders manufacture less efficient. The research explores better workstation arrangements. An open queuing network (OQN) model was used to approximate the flow production system. Since the problem of workstation arrangement is a combinatorial optimization problem, simulated annealing (SA) was applied to search for a good solution. The combination of an OQN model and SA provides a powerful tool to solve the facility layout problem for a stochastic flow production system. The experimental results show that the proposed approach has the potential to guide industrial layout design and layout practice.

Deliktas, Torkul & Ustun, 2019 proposed the  single‐ and bi-objective functions that deal with a flexible job shop scheduling problem in cellular manufacturing environment by taking into consideration exceptional parts, intercellular moves, intercellular transportation times, sequence‐dependent family setup times, and recirculation. The problem has been known as NP‐hard. The proposed Lingo 11.0 with minimization of makespan for the problems involving about 4 cells, 4 part families, 15 parts, and 12 machines. The most suitable model among the proposed single‐objective models is determined using the test results. Then, another objective function as total tardiness is added to this model. The obtained bi-objective model is solved using the scalarization methods, the weighted sum method, ɛ‐constraint method, and conic scalarization method (CSM), in order to convert the mathematical model’s objectives into a single‐objective function. By utilizing these scalarization methods, the Pareto effective solutions are generated for a specific test problem. The advantages of the CSM are demonstrated by considering the Pareto effective solutions.

Benderbal & Benyoucef 2019 proposed the Reconfigurable Manufacturing Systems (RMSs) are designed to manufacture a specific product, incorporating the scalability to other products in the same family. This ability is based primarily on the reconfiguration capabilities offered by reconfigurable machines tools (RMTs). One of the most important aspects related to the reactivity of RMSs. More specifically, it considers the relations, which link the conceived system with two important environments: its logical environment, i.e., the product family (products that share similarities) in which the RMS can evolve, and its physical environment, i.e., the physical workshop that implements this RMS. The machine layout problem by considering the product family evolution where two sub-problems are addressed. The first sub-problem concerns the evolution of the product, in the same family, towards new products to meet the evolutions and the requirements of the customers. The second sub-problem deals with the machine layout problem based on the results of the first sub-problem. For this, our two-phase-based approach combines the well-known metaheuristic, archived multi-objective simulated annealing (AMOSA), with an exhaustive search–based heuristic to determine the best machine layout for all the selected machines of the product family. The proposed machine layout constraints imposed by the generated process plans and those depicting the available location in the shop floor where machines can be placed.

Moghaddam et al. 2019 proposed the demand fluctuations handling of the products throughout their lifecycles with minimum cost, RMS configurations for cellular formation must change as well. Two different approaches are developed for addressing the system configuration design in different periods. Both approaches make use of modular reconfigurable machine tools (RMTs), and adjust the production capacity of the system, with minimum cost, by adding/removing modules to/from specific RMTs. In the first approach, each production period is designed separately, while in the second approach, future information of products demands in all production periods are  available in the beginning of system configuration design. Two new mixed integer linear programming (MILP) and integer linear programming (ILP) formulations are presented in the first and the second approaches respectively. The performance of these approaches are compared with respect to many different aspects, such as total system design costs, unused capacity, and total number of reconfigurations. Analyses of the results show the superiority of both approaches in terms of exploitation and reconfiguration cost.

Majumder, Laha & Suganthan 2019 proposed the discrete bacterial foraging algorithm to determine the optimal sequence of parts and robot moves in order to minimize the cycle time for the 2-machine robotic cell scheduling problem with sequence-dependent setup times. We present a method to convert the solutions from continuous to discrete form. In addition, two neighborhood search techniques are employed to updating the positions of each bacterium during chemotaxis and elimination–dispersal operations in order to accelerate the search procedure and to improve the solution. Moreover, a multi-objective optimization algorithm based on NSGA-II combined with the response surface methodology and the desirability technique is applied to tune the parameters as well as to enhance the convergence speed of the proposed algorithm. Finally, a design of experiment based on central composite design is used to determine the optimal settings of the operating parameters of the proposed algorithm. The results of the computational experimentation with a large number of randomly generated test problems demonstrate that the proposed method is relatively more effective and efficient than the state-of-the-art algorithms in minimizing the cycle time in the robotic cell scheduling.

Saez-Mas et al. 2020 proposed the cell assignment problem in an assembly facility. These cells receive parts from external suppliers, and sort and sequence these parts to feed the final assembly line. Therefore, to each cell are associated important inbound and outbound flows generating hundreds of material handling equipment movements along the facility, impacting the traffic density and causing eventually safety issues in the plant. Following an important plant redesign, cells have been relocated, and the plant managers need to decide how to manage the new logistic flows. A hybrid approach encompassing mathematical optimization and discrete event simulation (DES) is proposed. This approach allows us to reduce complexity by decomposing the design into two phases. The first phase deals with the problem of generating cell’s assignment alternatives by using a heuristic method to find good quality solutions. Then, a DES software is used to dynamically evaluate the performance of the solutions with respect to operational features such as traffic congestion and intensity. This second phase provides interesting managerial insights on the manufacturing system from both quantitative and qualitative aspects related to in-plant safety and traffic.

Garshasbi et al. 2019 proposed the optimal learning groups based on their multiple characteristics is a determining effort in enhancing the effectiveness of collaborative learning. The developing and implementing appropriate computational tools to handle classification processes in expert and intelligent systems, the effectiveness and accuracy of optimal cell formation algorithms are still worth improving. The majority of cell formation processes in collaborative learning environments is orchestrated through single-objective optimization algorithms, which need to be revisited due to some intrinsic limitations. The proposed novel algorithm capable of properly addressing a variety of optimization problems in optimal learning group formation processes. To this end, a multi-objective version of Genetic Algorithms, i.e. Non-dominated Sorting Genetic Algorithm, NSGA-II, was successfully implemented and applied to improve the performance and accuracy of optimally formed learning groups.

Danilovic & Ilic 2019 proposed the cell formation problem is a crucial component of a cell production design in a manufacturing system. Problems related to the cell formation problem are complex NP-hard problems. The aim to design the algorithm for the cell formation problem that is more efficient then the best-known algorithms for the same problem. The strategy of the new approach is to use the specificities of the input instances to narrow down the feasible set, and thus increase the efficiency of the optimization process. In the dynamic production environment, efficacy is one of the most significant characteristics of the applied expert system. The result is, extensible hybrid algorithm that can be used to solve complex, multi-criteria optimization cell formation problems. The new algorithm produces solutions that are as good as, or better than, the best results previously reported in literature on all commonly used test instances. The time efficiency of the proposed algorithm is at least an order of magnitude better than the efficiency of the most efficient reported algorithms.

Gliatis & Minis 2019 proposed the appropriateness of cellular work structures for information-intensive services under a wide range of conditions. An important and unique service characteristic, such as the relationship of operator performance and workload or overwork. The approach uses experimental design and discrete event simulation. The cellular structure in service environments in which (a) significant reductions in key operational parameters may be achieved by introducing the work cells and its results, such as in setup time, in task duplication, and in the number of administrative layers; (b) no severe bottlenecks emerge; (c) operators are cross-skilled; and (d) high information dependencies exist amongst tasks. An important managerial implication that service managers and consultants need to consider before embarking into the establishment of cells.

Lian et al. 2019 proposed the Additive Manufacturing (AM) processes produce unique microstructures compared with other manufacturing processes because of the large thermal gradient, high solidification rate and other local temperature variations caused by the repeated heating and melting. The effect of these thermal profiles on the microstructure is not thoroughly understood. In this work, a 3D cellular automaton method is coupled to a finite volume method to predict the grain structure of an alloy. The heat convection due to thermo-capillary flow inside the melt pool is resolved by the finite volume method for a real and accurate temperature field, while an enriched grain nucleation scheme is implemented to capture epitaxial grain growth following the mechanism identified from experiments. The 3D cellular automaton finite volume method results establish our approach as a powerful technique to model grain evolution for AM and to address the process-structure-property relationship through the thermal characteristics and the grain structure are identified.

Amiri, Shirazi & Tajdin 2019 proposed the metamodel using simulation optimization approach based on multi-objective efficiency. The proposed metamodel includes different general techniques and swarm intelligent technique to reach the optimum solution of uncertain resource assignment and job sequences in an Automated Flexible job shop (AFJS). In order to show the efficiency and productivity of the proposed approach, various experimental scenarios are considered. The makespan, number of late jobs, total flow time and total weighted flow time minimization have been resulted in an automated flexible job shop too.

Madani & Carranza 2020 proposed the Identification of geochemical anomalies is of particular importance for tracing the footprints of anomalies. This can be implemented by advanced techniques of exploratory data analysis, such as fractal/multi-fractal approaches based on priori or posteriori distribution of geochemical elements. The latter workflow involves analysis of 2D/3D produced maps, which can be mostly obtained by geo-statistical algorithms. There are two challenging issues for such an analysis. At first, to handling the cross-correlation structures among the data, and the second one relates to the compositional nature of data. In this context, an innovative algorithm, namely co-simulated size number (CoSS-N), is introduced for this purpose. The compositional nature of data is addressed by additive log-ratio transformation of original data while the Gaussian co-simulation handles the reproduction of cross-correlation among the components and the accuracy as satisfied and improved.

Kumar, Raju & Janardhana 2020 proposed the modern-day manufacturing process, flexible manufacturing system (FMS) is used for efficient production of parts. For manufacturing of specific parts, parts should be processed in a specified sequence of operations. It will be better to identify different possible sequence of operations on different machines and their cost implications in case of any machine failures. The three machines produce three different parts by doing different operations. Each machine can perform all the different operations to produce all the three parts. The combined objective function (COF) is formulated by considering the two objectives minimizing the total flow time and minimization of total tool cost with equal weightages. MATLAB Code is written for identifying all the possible sequences of operations, computed their total flow time and tool costs. Best sequences are identified when all machines are working; first machine fails, second machine fails and third machine fails based on COF values.

Kuo & Kusiak 2019 proposed the grouping with fuzzy features, production sequence data for cell formation and cell formation with the use of ordinal and ratio-level data. In these applications, the data was used to compute similarity measures for grouping decisions. Clustering was a widely applied solution approach in group technology. Another aspect of data research during this period was performance monitoring or control of processes. Data were deployed to construct control charts in most of these applications. The proposed optimization models to ensure closed-loop supply chains remained sustainable. The environmental impact databases and life cycle data were utilized and considered a closed-loop supply chain network design problem with uncertain interval data. They proposed an interval robust optimization approach to tackling the problem. A problem of cell formation in manufacturing with the consideration of sequence data, machine replications and alternative process routings. They proposed a hybrid algorithm of simulated annealing and local search heuristics to tackle the problem.

Baykasoğlu & Akpinar 2020 proposed the efficiency enhancement study of a recent meta-heuristic algorithm, WSA, by modifying one of its operators, superposition (target point) determination procedure. The original operator is based on the weighted vector summation and has some potential disadvantages with regard to domain of the decision variables such that determining a superposition out of the search space. Such potential disadvantages may cause WSA to behave as a random search and result in an unsatisfactory performance for some problems. To eliminate such potential disadvantages, the proposed new superposition determination procedure for the WSA algorithm. Thus, the mWSA algorithm will be able to behave more consistent during its search and its robustness will improve significantly in comparison to its original version. The mWSA algorithm is compared against the WSA algorithm and some other algorithms taken from the existing methods on both the constrained and unconstrained optimization problems.

Godec et al. 2020 proposed the microstructure details of 316L stainless steel produced by the additive-manufacturing selective-laser-melting technique under industrial conditions and correlated them with the mechanical properties. The employed micro- and nano-scale imaging electron microscopy techniques revealed the formation of a rigid hierarchical microstructure, which was driven by the rapidly changing solidification rates. The latter also affected the alloying atoms’ distribution in the melt-pool boundary area as well as in the dislocation-dense regions. The melt-pool boundaries in themselves did not produce structural irregularities, but were shown to have a slightly different chemical composition. The arrangement of the complex dislocation cells that developed in the whole material volume led to an increase in the yield strength. The calculated twenty-times-higher dislocation density compared to that of the forged material was linked to a very low strength hardening.

Chansoria & Shirwaiker 2020 proposed the 3D bioprinting continues to evolve as a promising alternative to engineer complex human tissues in-vitro, there is a need to augment bioprinting processes to achieve the requisite cellular and extracellular organizational characteristics found in the original tissues. While the cell distribution within bioinks is typically homogeneous, incorporating appropriate cellular patterning within the bioprinted constructs is an essential first step towards the eventual formation of anisotropically organized tissue matrix essential to its biomechanical form and function. This study describes a new bioprinting technique that uses ultrasonic standing bulk acoustic waves (SBAW) to organize cells into controllable anisotropic patterns within viscous bioinks while maintaining high cell viability. Pertinent cellular patterning and viability of at least 80 % were noted in the alginate and GelMA constructs across the experimental design space. Finally, we demonstrate the vat photo-polymerization-based bioprinting of a 3-layered GelMA construct with hASC strand lay pattern of 0-45-90° across the layers.

Shi et al. 2020 proposed the Additive manufacturing (AM) promises to revolutionize manufacturing by producing complex parts with tailored mechanical properties through local microstructure control. The main challenge is to control or prevent columnar (elongated) growth morphology which is prevalent in AM parts. We elucidate mechanisms of microstructure control that promote favorable equiaxed grains (aspect ratio close to 1) using a laser beam shaping strategy. This requires an accurate thermal profile that is only captured using advanced predictive simulation that couples full laser ray tracing, ultra-fast hydrodynamic melt flow and the cellular automata method for grain growth. We investigate columnar to equiaxed microstructure transition during single-track laser powder bed fusion processing of 316 L stainless steel using Gaussian and elliptical laser beam shapes to attained the local beam shaping for microstructural control would have implications on future complex beam shape designs as well as beam modulation.

Shashikumar et al. 2019 proposed the cellular manufacturing are cell formation, machine layout and cell layout problems. However, these problems are NP-hard optimization problems and cannot be solved using exact methods. A difficult part is to form the machine groups or cells, also called Cell Formation Problem and several techniques have been proposed to solve the same. In this paper, the Cell Formation Problem is solved using an integrated approach of heuristics along with Genetic Algorithm and Membership Index. Heuristics technique is used for domain selection which is used in Genetic Algorithm as the initial population. Genetic Algorithm is useful for optimizing the results of machine assignment to cells, and Membership Index is used to assign parts to the cells. The performance is analyzed using performance measures such as group technology efficiency and some exceptional elements also relevant from current research and industry trends.

 

 

 

 

 

 

 

 

 

2.3 SUMMARY

            In recent years, the Manufacturing Cell Formation (MCF) is a model for manufacturing system design which has been used beneficially by many industrial sectors. The goal of MCF is to have the flexibility to manufacture a wide variety of low demand products while maintaining the high productivity of mass production. MCF system achieves the product design and process design modularity. The properly designed and implemented MCF offers the flexibility of job-shops while retaining the efficiency of flow-shops, permitting batch production to gain economic advantages similar to those of mass production. In this chapter, the various existing manufacturing cell formation methods and the corresponding practical approaches discussed. The different formulation strategies with their inefficient abilities make the lag of the cell formation process.

 

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