Cellular Manufacturing Systems Research Articles

A Framework for Adaptive Scheduling in Cellular Manufacturing Systems

The Fourth Industrial Revolution has made efficient and adaptive manufacturing a prominent research topic. The necessity for personalized products leads to complex production lines. Due to the increased complexity, planning and scheduling of manufacturing processes seeks to identify near-optimum solutions to ensure fast and precise decision making. This research paper aims to contribute to the field of adaptive scheduling by proposing an algorithm that enables near real-time cooperation among machines, workforce and the production manager. A framework for self-adaptive scheduling in a real-world manufacturing case deriving from an SME that produces solar panels will be presented.

Assessment of Hamming Distance and Self Organization Map in Solving Cell Formation Problem

The cell formation (CF) problem is considered the most essential issue in cellular manufacturing systems (CMS). CF deals with the arrangement of similar parts into groups known as part families (PFs) and organizes machines also into groups, called machine cells (MCs). In the literature, numerous methods, models and algorithms have been proposed and developed to handle CF problems. However, very few studies have dealt with the assessment and comparison of these methods, to identify the most effective. This has provided strong motivation for the study presented here. The present paper focuses on two methods that are used infrequently to form MCs and PFs, and applies them in three strategies: the first is based on the use of a hamming distance only, while the second uses only a self-organization map (SOM). However, the third method applies a hybrid approach based on SOM and hamming distance. The outputs of the selected methods were compared, to select the best one. A set of five benchmark datasets and three performance measures was used for comparison and evaluation. These performance measures are: percent of the exceptional elements (PE), grouping efficiency (GE), and machine utilization (MU). The results refer to the outperforms of the hamming distance in terms of PE, GE and MU for most of the selected benchmark problems.

A review on the multi-objective cell formation problem in cellular manufacturing systems

With the advent of cellular manufacturing systems and consequent evolution of configuring methodologies, researchers proposed several multi-objective techniques, besides others, to handle different types of cell formation problems. This paper presents a systematic review on types of cell formation by using multi-objective techniques. It also delineates the type of objectives, production factors and datasets used for testing the efficacy of methodologies, considered by researchers along with the scope and direction of future studies.

A review on the multi-objective cell formation problem in cellular manufacturing systems

With the advent of cellular manufacturing systems and consequent evolution of configuring methodologies, researchers proposed several multi-objective techniques, besides others, to handle different types of cell formation problems. This paper presents a systematic review on types of cell formation by using multi-objective techniques. It also delineates the type of objectives, production factors and datasets used for testing the efficacy of methodologies, considered by researchers along with the scope and direction of future studies.

A fuzzy multi-objective model for a cellular manufacturing system with layout designing in a dynamic condition

Cellular manufacturing system (CMS) plays a remarkably significant role in modern production systems. This paper presents a novel and practical fuzzy multi-objective mathematical model for a CMS in a dynamic condition considering the flexibility in allocating machines. The proposed model seeks to determine the best layout design in each production period. Two conflicting objectives are considered including minimising the cost of manufacturing system due to the amount of production loss caused by waste, as well as minimising the variance of fuzzy costs. Due to the complexity of the problem, non-dominate sorting genetic algorithm-II and multi-objective particle swarm optimisation algorithm are designed to solve the model and to obtain the effective solutions. In order to demonstrate the efficiency of the algorithms and to choose the premier algorithm, both algorithms are evaluated in several A fuzzy multi-objective model for a cellular manufacturing system 515 random problems and then compared based on the existing measurement indicators. Then, the algorithms are tuned to solve the problem, based on which their performances are analysed statistically. The applicability of the proposed approach and the solution methodologies are demonstrated as well.

An Integrated Mathematical Programming Model for a Dynamic Cellular Manufacturing System with Limited Resources

This paper proposes an integrated integer nonlinear programming model for a concurrent cell formation and production planning problem in a dynamic cell-manufacturing system (DCMS) with limited resources to setup cells and to procure machines. The proposed model seeks to minimise the total costs associated with the production planning and the cell construction and formation under a dynamic system. To validate the model, it is first converted to a linear programming. Then, a numerical example is presented based on which the branch and bound method is used to solve it employing the Lingo 8 software. Besides, due to NP-hardness of the problem, two meta-heuristic algorithms namely a GA and a PSO are utilised to solve large-scale problems. In addition, the Taguchi method is used to calibrate the parameters of the meta-heuristics in order to find better quality solutions. Computational results confirm the efficiency and the effectiveness of the proposed solution algorithms.

Machine cell formation and part family identification by combined algorithm

Two major problems in the cell manufacturing process are machine cell formation and component family identification. A combined algorithm is proposed in this paper to improve the cellular production system. The aim of this algorithm is to maximise the effectiveness of grouping and efficacy of grouping to reduce the part's travel time and increase production. Grouping efficiency and grouping efficacy are the two most widely utilised measures of quality for the proposed cellular manufacturing systems. The proposed method integrates appropriate production data to shape the device cell and classify the component unit, compared to the well-known approaches. The combined algorithm is increased the grouping efficiency at maximum of 30.85% and it is maximising the grouping efficacy at the maximum of 5%. It will reduce the total travelling time of parts and increase the machine utilisation.

An integrated mathematical programming model for a dynamic cellular manufacturing system with limited resources

An integrated mathematical programming model for a dynamic cellular manufacturing system with limited resources

A Hybrid GRASP Algorithm for an Integrated Production Planning and a Group Layout Design in a Dynamic Cellular Manufacturing System

Demand fluctuations influence the configuration of manufacturing workshops. Integration of optimal production planning via the replenishment organization, can significantly reduce the excessive++reconfigurations number in each period and, thereafter, the global managing costs. In this article, we discuss the joint machines Group layout design (GLD) and lot-sizing problem (LSP) in a dynamic cellular manufacturing system (DCMS). We propose a novel multi-period model to determine the best cell formation, necessary configurations over each period, and optimal production and inventory policy that minimizes intra and inter-cell material handling, holding costs, and multitasks machines relocation. We propose a novel mixed-integer programming (MIP) associated model which is then solved by using the commercial software Optimizer CPLEX. Additionally, we present a hybrid greedy randomized adaptive search procedure (GRASP) enhanced with a path relinking procedure (PR) to solve the problem. Computational results on several benchmarks and randomly generated instances show the effectiveness and the relevance of the proposed approach and highlight the integration value.

Improved similarity coefficient and clustering algorithm for cell formation in cellular manufacturing systems

Cell formation plays an important role in the design of cellular manufacturing systems. Among many methods utilized to solve the cell formation problem, the similarity coefficient method is the most widely used owing to its high flexibility and low computational requirement. However, generalized similarity coefficients ignore material flow, which is closely related to operation sequence and repeated operations. Moreover, most similarity coefficient-based clustering algorithms focus on the number of inter-cell movements but disregard distinction of the movement effort. To overcome these limitations, this study improves the generalized similarity coefficient method to form part families. In addition, a new clustering algorithm is presented to assign machines to cells with minimum intensity of material inter-cell movement, which depends on the frequency, production volume and difficulty level of inter-cell movement. Experimental results demonstrate that the proposed method has superior sensitivity and effectiveness for solving the cell formation problem.

Bi-objective robust optimization model for configuring cellular manufacturing system with variable machine reliability and parts demand: A real case study

In this paper, a bi-objective mixed-integer mathematical model is presented for configuration of a dynamic cellular manufacturing system. In this model, dynamic changes and uncertainty in parts demand and machines reliability are considered. The first objective function minimizes total costs and the second one maximizes the machines reliability through minimizing machines failure. In addition, some routes are considered to produce each part based on operational requirements. An appropriate route is selected respect to the costs and operational time. Some parameters are considered under uncertainty in two categories. The first category such as demand is dependent on market condition and the uncontrolled competitive environment. The second one includes some parameters for production system and machines that are directly related to plans organized by production management. A robust optimization approach is used to deal with parameters uncertainty to produce feasible and optimal solutions. Furthermore, for validation and implementation of results in real world, a case study is investigated. Computational results show that the robust model reports better values for objective functions compared to the scenario-based model. In fact, Pareto-front which are resulted by robust model are dominated by scenario-based models’ Pareto front. Sensitivity analyses on main parameters of the problem are performed to drive some managerial insights that help corresponding decision makers to provide suitable and homogenous decisions in a production system.

A New Mathematical Model for the Integrated Solution of Cell Formation and Part Scheduling Problem

In a cellular manufacturing system, three important decisions are to form cells, design the layout of cells, and schedule of parts in the cells. Most of the studies in this area have discussed these decisions separately and independently. However, for general system performance, it is important to consider these decisions in relation to each other, and integrated solutions are needed. But few studies include that two or more decisions are handled together. In this paper, a new mathematical model considers decisions both cell formation and part-scheduling in cells together is proposed. The objective function is designed in integrated manner and includes two objectives to minimize together. These objectives are to minimize the exceptional elements and makespan of the jobs. Numerical examples are provided in the paper to show that the model is valid and it can be applicable as practically. The test data are derived from the related literature and solved by GAMS software CPLEX solver. The results show that the performance of the cellular manufacturing systems in terms of formation of cells and scheduling of parts can be significantly improved by the proposed multi objective mathematical model.

Robust Design of Dynamic Cell Formation Problem Considering the Workers Interest

To enhance agility and quick responding to customers' demand, manufacturing processes are rearrenged according to different systems. The efficient execution of a manufacturing system depends on various factors. Among them, cell design and human issue are the pivotal ones. The proposed model designs cellular manufacturing systems using three objective functions from three different perspectives, to reflect a more realistic picture of the cell formation problem. This paper presents a model with the goals of maximizing the total value of grouping efficacy and minimizing the total costs and total non-interest workers in cells in a dynamic environment for several consecutive periods. The main idea of the proposed model is enhancing cell efficiency through an assigning the group of workers who have a mutual interest in working with each other. For solving the current model, the revised multi-choice goal programming method has been employed. Finally, computational results and sensitivity analysis are discussed.

Integrated optimization of dynamic cell formation and hierarchical production planning problems

The paper presents 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, this paper 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. The model is solved with branch-and-bound method, and its complexity and solution results are analyzed using empirical data from a mold manufacturing plant, with the results of the model compared with those in the literature. Our analyses show that the proposed model is simpler and easier to solve, and that the total cost is insensitive to demand volatility and decreases inversely with the number of cells.

Strategic Outcome Using Fuzzy-AHP-Based Decision Approach for Sustainable Manufacturing

The manifestation of new part designs and continuously changing market demands as well as the requirements of new functions and technologies often results in higher material cost, lesser machine utilization, and extensive wastage of energy. As a consequence, companies across the world are striving for sustainable manufacturing, which can ensure flexibility as well as adaptability, with higher productivity and lesser wastage of resources. The dynamic and competitive nature of the world market emphasizes the importance of economically sustainable setups, such as reconfigurable cellular manufacturing systems (RCMSs). Indeed, among several cutting-edge strategies, the RCMS is the most prominent owing to its versatility, rationality, and resilience. However, one of the limitations associated with RCMS is the evaluation and selection of the best configuration that can meet abrupt changes and achieve manufacturing sustainability. Therefore, organizations intending to reconfigure have to address the issue of evaluating all possible alternatives and selecting the best one using a well-defined methodology. This paper focuses on evaluating and finding the best configuration using multi-criteria decision-making (MCDM) approaches depending on PROMETHEE and VIKOR. The fuzzy analytic hierarchy process (FAHP) is utilized to compute the weights of various criteria because it also considers any uncertainty and vagueness existing in the problem. The assessing attributes in this study are selected by keeping in mind the objective of sustainable manufacturing. The two MCDM methods are utilized for ranking different configurations to ascertain the results obtained from the other. The study accomplished in this paper is related to manufacturing setups that need to be reconfigured. The number of manufacturing configurations is determined, and simulation models are established for each configuration. The simulation outcomes are examined using the FAHP-PROMETHEE and FAHP-VIKOR to assess the appropriateness of each configuration depending on the identified performance measures. The results of the experiments show the importance of employing MCDM in RCMS to achieve sustainable manufacturing and determining the most effective setup.

An Optimization Approach for Simulation Operator Allocation and Job Dispatching Rule in a Cellular Manufacturing System

The purpose of this article is to present an approach based on simulation optimisation for improving the performance of cellular manufacturing systems through optimising operator allocation and job...

Cellular layout design using Tabu search, a case study

This paper presents a facility layout design for a cellular manufacturing system (CMS), with the automated guided vehicles (AGVs) as the transportation device, to minimize the total material handling costs. A new concept of assigning the workstations to the non-overlapping closed zones, by which independent AGVs are allocated for internal transfer of materials/parts in each zone, is proposed. To handle this problem, a mathematical programming model is developed. The problem has been shown to be NP-hard. This computational difficulty has led us to consider suboptimal solutions generated by a Tabu search with three algorithmic variants. To evaluate the efficiencies of these algorithms, an ANOVA statistical test is performed and the best algorithm is designated. The solution of the new algorithms is then compared with the solution obtained by the CPLEX software. The result indicates that the designated algorithm can provide an average solution with a small deviation of only 0.01% from the optimal solution. This algorithm is also applied to a real-world case problem. The use of this algorithm for re-designing of the layout for the real-world case problem shows considerable cost saving comparing to its existing layout design.

Designing a manufacturing cell system by assigning workforce

Purpose: In this paper, we have proposed a new model for designing a Cellular Manufacturing System (CMS) for minimizing the costs regarding a limited number of cells to be formed by assigning workforce. Design/methodology/approach : Pursuing mathematical approach and because the problem is NP-Hard, two meta-heuristic methods of Simulated Annealing (SA) and Particle Swarm Optimization (PSO) algorithms have been used. A small randomly generated test problem with real-world dimensions has been solved using simulated annealing and particle swarm algorithms. Findings: The quality of the two algorithms has been compared. The results showed that PSO algorithm provides more satisfactory solutions than SA algorithm in designing a CMS under uncertainty demands regarding the workforce allocation. Originality/value: In the most of the previous research, cell production has been considered under certainty production or demand conditions, while in practice production and demand are in a dynamic situations and in the real settings, cell production problems require variables and active constraints for each different time periods to achieve better design, so modeling such a problem in dynamic structure leads to more complexity while getting more applicability. The contribution of this paper is providing a new model by considering dynamic production times and uncertainty demands in designing cells.

Process planning and scheduling for loop layout robotic workcell using virtual reality technology

The adoption of virtual reality in manufacturing system simulation had proved its effectiveness in bridging up to the gap between different areas of expertise, especially in product design and manufacturing. Virtual reality had enclosed human–machine interface by enabling the user to be immersed into the virtual environment and experience real-time interaction with the virtual objects. In this article, an implementation of virtual reality in cellular manufacturing system simulation is presented. By utilizing the features of visualization and real-time interaction of virtual reality technology, the manufacturing process of a product had been visualized while the real-time control on the product traveling path based on the user’s input was performed and the corresponding activities that related to the change of traveling path had been predicted in the virtual environment. Through the study, simulation of the manufacturing system in virtual reality showed its potential as a powerful decision support system in process planning and scheduling. Various process planning and schedules can be planned through the virtual environment, while the product traveling distance can be obtained from the developed system.

A genetic algorithm to integrate a comprehensive dynamic cellular manufacturing system with aggregate planning decisions

ABSTRACTIn this paper, to design a Cellular Manufacturing System (CMS) under a dynamic situation and make Aggregate Planning (AP) decisions simultaneously, a mixed-integer nonlinear programming (MINLP) model is designed. The distinctive features of the comprehensive Dynamic CMS (DCMS) model under consideration are: i) an extensive coverage of significant manufacturing characteristics in designing a DCMS in addition to the main strategies of AP, ii) integration of cost elements addressing structural, operational and planning issues in the design of DCMS, and iii) capable of developing better DCMS design decisions by incorporating more detailed and realistic parameters when compared to the literature. An illustrative numerical example is solved by CPLEX 12.6 to demonstrate the achievements obtained by the integrated model. Since the proposed model belongs to NP-hard category, a Genetic Algorithm (GA) improved by an elaborately designed matrix-based chromosome representation to represent all decision variables, as well as a sequential procedure generating initial solutions is developed. Several test problems either generated randomly or taken from the literature with various sizes are solved and the results are compared with the solutions gained using CPLEX solver. The comparisons results show that the designed GA is capable of evolving optimal or near-optimal solutions with relative gap less than 1% in a computationally satisfactory manner.Abbreviation: DCMS: Dynamic CMS; AP: Aggregate Planning; GA: Genetic Algorithm; MINLP: Mixed-integer nonlinear programming