Inter-cell Layout Research Articles

Benders decomposition approach to solve a Bi-objective cell formation in dynamic conditions

Cellular Manufacturing System (CMS) is an alternative to production systems based on process and product layout that combines the advantage of high throughput rate of flow shop system with the flexibility of job shop layout. CMS design has four steps: 1) Cell formation: grouping the parts with similar geometric design features and processing requirements in part families to use similarities to build, manufacture and assemble machines in machine lines and cells, 2) group layout: placing machines in each cell that includes intra-cell and inter-cell layout, 3) group scheduling: part family scheduling, and 4) source allocation: allocation of tools and primary resources and human force. Dynamic CMS (DCMS) can be divided into smaller periods, each of which is different from the previous one as these periods have various demands and compositions. The study presented a mixed-integer programming model for DCMS. The model considered some significant real-world conditions. In this model, the first objective function tries to minimize the costs related to the machines purchase cost, machines variable cost, the cost of movement workers, the cost of cell formation, the cost of inter-cell and intra-cell part movement, the cost of overtime, and the cost of reconfiguration.

パレート解分析に基づくセル生産システムのレイアウト設計

In this paper, we propose a support method for multi-objective layout design of cellular manufacturing systems. The proposed method is composed of a global search technique for layout designs and a data mining technique applied to the obtained layout solutions. First, intercell layout design candidates (possible locations of each manufacturing cell or department within a floor area) are encoded as sequence variables, using a sequence-pair scheme, and a multi-objective genetic algorithm using NSGA-II is then used to search the set of Pareto optimal solutions for the intercell layout design problem. Then, to analyze the solution set and simplify the subsequent decision process, we develop a data mining technique, EM clustering, which clusters the obtained layout solutions considering correlations of cells or departments among the layout solutions. To demonstrate the effectiveness of the proposed method, we present a numerical example of a two-objective intercell layout design problem that aims to minimize the material handling cost and maximize the closeness rating.

Integrated dynamic cell formation with operator assignment and inter-cell layout problems: A mathematical model

Designing a cellular manufacturing system involves four major decisions: cell formation, cellular layout, operator assignment and cellular scheduling which should be considered, simultaneously. This article presents a new mathematical model to solve the cell formation, operator assignment and inter-cell layout problems, concurrently. The objectives of proposed model are minimization of inter–intra cell part movements, machine relocation cost and operator-related issues including hiring, firing, training and salary costs. Two numerical examples in both small and large sizes are optimally solved by the Lingo software to verify and validate the proposed mathematical model. Also, a sensitivity analysis is performed to analyze the behavior of operators in different production periods.

Applying soft-computing techniques in solving dynamic multi-objective layout problems in cellular manufacturing system

The formation of machine cells to process subsequent part families is not the only goal of the designing of an efficient cellular manufacturing system (CMS). A competent layout of the newly acquired cells is also essential to restrict the total inter-cell material handling cost which is primarily significant with large production volume. Furthermore, in realistic industrial scenario, the uncertainty of product demand can influence the layout configuration to be altered from period to period. Albeit there are numerous articles exist in the domain of CMS research considering cell formation problems, layout issues have not been addressed significantly. Therefore, the aim of our paper is to portray a reformed mathematical model of the inter-cell layout design problem in dynamic production situation considering material handling cost and a modified proximity relationship of manufacturing cells. The proposed Quadratic Assignment Programming (QAP) model is combinatorial in nature and is difficult to solve using traditional exact solution methods. The state-of-the-art soft-computing techniques are extremely advantageous for such QAP paradigms. Thus, we developed an improved genetic algorithm (IGA) and a simulated annealing heuristic (SAH) to sort out the abovementioned problem. Due to the inadequacy of datasets, we formed small to large size test problems (6 × 6 × 2 to 24 × 24 × 10) in logical way to cater the purpose. The proposed algorithms are successfully employed to attain near-optimal solutions to the test problems. Computational results demonstrate the proficiency of the IGA over SAH for all the test problems in terms of solution quality and computational time. In addition, we conducted a statistical data analysis to validate the test results.

An Immune Genetic algorithm for inter-cell layout problem in cellular manufacturing system

The objective function of inter-cell layout problem minimizes the total inter-cellular material handling cost. It is mostly significant with moderate production quantity in cellular manufacturing systems (CMS). This problem is classified as quadratic assignment problem (QAP) which is NP-Hard in nature. Heuristic techniques are extremely effective for such problems. In this paper we proposed a novel Immune Genetic algorithm (Immune-GA-RS) to obtain competent inter-cell layout in the vicinity of CMS. It exploits an elitist replacement strategy in order to improve the rate of convergence. The proposed method is successfully tested upon 8 datasets which are being widely used for inter-cell layout design problems. Proposed Immune-GA-RS is compared with two variants of the Genetic Algorithms, GA-RS and Alt-GA-RS. It is further compared with other published layout design techniques. Immune-GA-RS is shown to acquire 11.11 % improved solutions with 7.72 % reduced CPU time on an average. Further Immune-GA-RS is tested on 36 structured QAP instances available through QAPLIB and shown to outperform other two GA variants while attaining optimal solutions for 33 instances. It is also shown to outpace other published algorithms while attaining smaller solution gap for 11 test instances and obtains at least equal or better quality solutions for 24 instances. We conclude our work with a statistical data test to signify the results.

MIP model and elitist strategy hybrid GA–SA algorithm for layout design

It is most important for any manufacturing industry to have an efficient layout for their production environment to participate in global competition. One of the prime objectives of such an organisation is to decide an optimal arrangement of their facilities (machines or departments) in a two-dimensional planar region satisfying desired objectives, which is termed facility layout problem. To overcome the drawbacks of traditional layout design methodology, it is attempted to solve three important layout design problems such as inter-cell layout design, determination of optimum location for input/output stations and flow path layout design of material handling system simultaneously in an integrated manner. The quality of the final layout is evaluated by minimizing the total material handling cost, where the perimeter distance metric is used for the distance measurement. Sequence-pair, an elegant representation technique is used for layout encoding. The translation from sequence-pair to layout is efficiently done by longest common subsequence computation methodology. Due to the non-polynomial hard nature of the problem considered, an elitist strategy based hybrid genetic algorithm that uses simulated annealing as local search mechanism (ESHGA) is developed and tested with test problem instances available in the literature. The results indicate that proposed integrated methodology with developed mixed integer programming based mathematical model along with ESHGA could generate realistic layouts compared to reported result.

A robust optimization approach for an integrated dynamic cellular manufacturing system and production planning with unreliable machines

In this study, a robust optimization approach is developed for a new integrated mixed-integer linear programming (MILP) model to solve a dynamic cellular manufacturing system (DCMS) with unreliable machines and a production planning problem simultaneously. This model is incorporated with dynamic cell formation, inter-cell layout, machine reliability, operator assignment, alternative process routings and production planning concepts. To cope with the parts processing time uncertainty, a robust optimization approach immunized against even worst-case is adopted. In fact, this approach enables the system’s planner to assess different levels of uncertainty and conservation throughout planning horizon. This study minimizes the costs of machine breakdown and relocation, operator training and hiring, inter-intra cell part trip, and shortage and inventory. To verify the performance of the presented model and proposed approach, some numerical examples are solved in hypothetical limits using the CPLEX solver. The experimental results demonstrate the validity of the presented model and the performance of the developed approach in finding an optimal solution. Finally, the conclusion is presented.

Integrated cell formation and layout problem considering multi-row machine arrangement and continuous cell layout with aisle distance

Cellular manufacturing is an important application of group technology and the cell formation process is one of the important steps in designing cellular manufacturing system. In recent years, researchers have noticed potential benefits when the layout problem is considered within the cell formation process. Nevertheless, there are not sufficient studies about consideration of real-life features in the cell formation and layout design process. In this paper, a new approach is presented to integrate the cell formation and its layout design. The proposed approach has three important design features not found in other papers. These design features are multi-row intra-cell layout (layout of machines within the cells), continuous inter-cell layout (layout of rectangular shape cells on the planar area), and aisle distance. The objective of the proposed approach is to form machine cells, find the layout of machine cells, and obtain the arrangement of machines within the cells such that the total material handling cost is minimized. In order to have a more accurate layout design, the material handling cost is calculated in terms of the actual position of machines within the cell. Due to the computational complexity of the proposed problem, a heuristic method is proposed to solve medium- and large-scale problems in a reasonable computational time. Three lower bounds are developed for the proposed integrated problem in which the tightest of them is chosen for evaluating the solution of the heuristic method. Finally, numerical examples adopted from the literature are solved to verify the performance of the proposed heuristic method and illustrate the advantages of the proposed integrated approach. The results indicated that the heuristic method is both effective and efficient in solving real-sized problems. The results also demonstrated that the proposed layout approach gives better layout design in comparison with the existing approaches.

An elitist strategy genetic algorithm using simulated annealing algorithm as local search for facility layout design

A well-planned arrangement of manufacturing departments on a two-dimensional planar region considerably increases the efficiency of its production systems, which is termed facility layout problem (FLP). Conventional layout design approach often designs intercell layout (determining exact location of each department on shop floor area) and flow path layout design of material handling system (MHS) step by step in a sequential manner. This results in suboptimal solutions for FLP. In this paper, an integrated approach is adopted to design the intercell layout and the flow path layout of MHS simultaneously. The quality of the final layout is evaluated by minimizing total material handling cost. Sequence pair (SP) representation is used for layout encoding. The translation from SP to layout is efficiently made by longest common subsequence (LCS) methodology. An elitist strategy genetic algorithm using simulated annealing (E-GASAA) as a local search mechanism is developed and tested with four test problem instances available in the literature. Elitist strategy is incorporated to enhance convergence characteristic of the proposed algorithm. It is found that the proposed E-GASAA is able to produce best solutions consistently for the test problem instance of different sizes within acceptable computational effort. In addition to that, we tried to reduce the computational load with the help of adopted LCS computation methodology and achieved a good improvement.

Concurrent cell formation and layout design using scatter search

Two key decisions in designing cellular manufacturing systems are cell formation and layout design problems. In the cell formation problem, machine groups and part families are determined while in the facility layout problem the location of each machine in each cell (intra-cell layout) and the location of each cell (inter-cell layout) are decided. Owing to the fact that there are interactions between two problems, cell formation and layout design problem must be tackled concurrently to design a productive manufacturing system. In this research, two problems are investigated concurrently. Some important and realistic factors such as inter-cell layout, intra-cell layout, operations sequence, part demands, batch size, number of cells, cell size, and variable process routings are incorporated in the problem. The problem is formulated as a mathematical model. Three different methods are described to solve the problem: multi-objective scatter search (MOSS), non-dominated genetic algorithm (NSGA-II), and the e-constraint method. The methods are employed to solve nine problems generated and adopted from the literature. Sensitivity analysis is accomplished on the parameters of the problem to investigate the effects of them on objective function values. The results show that the proposed MOSS algorithm performs better than NSGA-II and produces better solutions in comparison to multi-stage approaches.

An integrated approach for the cell formation and layout design in cellular manufacturing systems

In this paper, a comprehensive model is presented for cell formation and layout design in cellular manufacturing systems (CMS). The proposed model incorporates an extensive coverage of important operational features and especially layout design aspects to determine optimal cell configuration and Intra and Inter-cell layout in CMS. Hence, proposed integrated approach attempts to design intra and inter-cell layout and material handling flow path structure simultaneously. We examine the great potential benefits of providing these features consist of routing flexibility, operation sequence, machine capacity, considering number of cells as a decision variable, un-equal dimension of machines, free machines and cells orientation, and considering pickup and drop off station for each cell. In order to show the effects and important of integrated design in the CMS, two approaches, sequentially and integrated, have been investigated and demonstrate the integrated approach improve the quality of obtained solution. The proposed model is a mixed integer non-linear programme. Linearisation procedures are proposed to transfer it into a linearised mixed integer programming formulation. Computational results are presented with the linearised formulation. We presented several enhancements in terms of valid inequalities and extensions to the proposed model in order to improve its computational performance. Finally, concluding remarks are provided.

A new mathematical model towards the integration of cell formation with operator assignment and inter-cell layout problems in a dynamic environment

This paper proposes a new mathematical model to solve the cell formation, operator assignment and inter-cell layout problems, simultaneously. The objectives of proposed model are minimization of inter–intra cell part trips, machine relocation cost and operator related issues. Since the objective function of the proposed model consists of none commensurable statements, the preferred solution is obtained by the LP-metric approach. In order to validate the proposed model, some numerical examples are generated randomly and solved by branch and bound technique. Moreover; a real case study is illustrated in order to verify its applicability in an automobile producer company. Moreover the sensitivity analysis of proposed model shows that considering the operator assignment problem has significant impact on the overall system efficiency.

A hybrid electromagnetism-like algorithm for dynamic inter/intra-cell layout problem

In this article, a new mathematical model is presented for the inter- and intra-cell layout problem under the dynamic environment. Inter/intra-cell layout is the second stage of designing a cellular manufacturing system, preceded by the cell formation stage. It involves laying out the cells in the shop floor (Inter-cell layout) and locating the machines in the cells (Intra-cell layout). The dynamic environment implies a multi-period planning horizon in which the best-designed cell layout for the current period may not be efficient for the subsequent periods and, therefore, rearrangement of the cell’s layout is required. The objective is to minimise the total costs of rearrangement and inter/intra-cell material flows. An electromagnetism-like (EM-like) algorithm and a genetic algorithm (GA) are implemented to solve the proposed model. Also, a hybrid algorithm based on the EM-like algorithm and GA is proposed. Statistical tests are carried out to evaluate the performance of the algorithms using a number of numerical examples separated in the small, medium and large-sized problems. The results show the superiority of the hybrid EM–GA algorithm in comparison with other algorithms. Finally, concluding remarks are provided.

Illustration and Model Verification for an Inter-Cell and Intra-Cell Facility Layout Problem in a Cellular Manufacturing System

In a Cellular Manufacturing System (CMS), parts based on their similarities are grouped into part families, and are assigned to one or more machine cells to be manufactured. In the design of a CMS, facility layout problem aims to arrange machine cells in the shop floor and facilities in the machine cells in order to have an efficient layout. In this study, a previously developed cellular layout model will be illustrated and validated for a well-known case from the literature. Lingo optimization software version 12.0 and an enumeration method will be used to solve, and validate the model. The results of this study show the validity of the proposed model.

Two-Stage Design for Cellular Manufacturing System

Cell formation（CF）and cellular layout design are the two main steps in designing a cellular manufacturing system (CMS). Efficient grouping is the prerequisite of a successful Cellular Manufacturing, and it is too hard to obtain a perfect consequence of CF and layout simultaneously. In this paper, a two-stage strategy is used to solve CF problem and layout sequentially. At the first stage, the cells are formed based on the part-machine clustering results obtained through the genetic algorithm, to get the initial inter-cell layout. At the second stage, the genetic algorithm is used to improve the solution obtained, to minimize the handling cost and maximize the utility of the machines.

A genetic algorithm for solving a multi-floor layout design model of a cellular manufacturing system with alternative process routings and flexible configuration

This paper presents a novel integer linear programming model for designing multi-floor layout of cellular manufacturing systems (CMS). Three major and interrelated decisions are involved in the design of a CMS; namely cell formation (CF), group layout (GL), and group scheduling (GS). A novel aspect of this model is concurrently making the CF and GL decisions to achieve an optimal design solution in a multi-floor factory. Other compromising aspects are: multi-floor layout to form cells in different floors is considered, multi-rows layout of equal area facilities in each cell is allowed, cells in flexible shapes are configured, and material handling cost based on the distance between the locations assigned to machines are calculated. Such an integrated CMS model with an extensive coverage of important manufacturing features has not been proposed before and this model incorporates several design features including alternative process routings, operation sequence, processing time, production volume of parts, duplicate machines, machine capacity, new machine purchasing, lot splitting, material flow between machines, intra-cell layout, inter-cell layout, multi-floor layout and flexible configuration. The objective is to minimize the total costs of intra-cell, inter-cell, and inter-floor material handling, new machines purchasing and machine processing. Two numerical examples are solved by the Lingo software to verify the performance of the proposed model and illustrate the model features. Sensitive analysis is also implemented on some model parameters. An improved genetic algorithm (GA) is proposed to derive near-optimal solutions for the integrated model because of its NP hardness. It is then tested using several problems with different sizes and settings to verify the computational efficiency of the developed algorithm in comparison to a classic simulated annealing algorithm and the Lingo software. The obtained results show the efficiency of proposed GA in terms of objective function value and computational time.

An elitist strategy genetic algorithm for integrated layout design

While designing the layout of any manufacturing organizations, the primary objective is to decide an optimal arrangement of their departments (machines or cells) in a two-dimensional shop floor (facility) satisfying desired objectives, which is termed facility layout problem (FLP). In traditional layout design philosophy, the inter-cell layout and flow path layout design of material handling system (MHS) was carried out step by step in a sequential manner. This results in sub-optimal solutions for FLP. In this paper, an integrated approach is adopted to design the inter-cell layout and the flow path layout of MHS simultaneously. The quality of the final layout is evaluated by minimizing the weighted sum of two distance-based cost objectives namely, (1) total material handling cost. (2) Distance-weighted cost of total closeness rating score. Sequence-pair (SP) representation is used for layout encoding. The translation from SP to layout is efficiently done by longest common subsequence methodology. Due to NP-hard nature of the proposed problem, an elitist strategy genetic algorithm (ESGA) is developed and tested with three test problem instances available in the literature. It is found that the proposed ESGA algorithm is able to produce the best solutions consistently, twice faster than the standard GA for the test problem instances.

Solving a group layout design model of a dynamic cellular manufacturing system with alternative process routings, lot splitting and flexible reconfiguration by simulated annealing

This paper presents a novel mixed-integer non-linear programming model for the layout design of a dynamic cellular manufacturing system (DCMS). In a dynamic environment, the product mix and part demands are varying during a multi-period planning horizon. As a result, the best cell configuration for one period may not be efficient for successive periods, and thus it necessitates reconfigurations. Three major and interrelated decisions are involved in the design of a CMS; namely cell formation (CF), group layout (GL) and group scheduling (GS). A novel aspect of this model is concurrently making the CF and GL decisions in a dynamic environment. The proposed model integrating the CF and GL decisions can be used by researchers and practitioners to design GL in practical and dynamic cell formation problems. Another compromising aspect of this model is the utilization of multi-rows layout to locate machines in the cells configured with flexible shapes. Such a DCMS model with an extensive coverage of important manufacturing features has not been proposed before and incorporates several design features including alternate process routings, operation sequence, processing time, production volume of parts, purchasing machine, duplicate machines, machine capacity, lot splitting, intra-cell layout, inter-cell layout, multi-rows layout of equal area facilities and flexible reconfiguration. The objective of the integrated model is to minimize the total costs of intra and inter-cell material handling, machine relocation, purchasing new machines, machine overhead and machine processing. Linearization procedures are used to transform the presented non-linear programming model into a linearized formulation. Two numerical examples taken from the literature are solved by the Lingo software using a branch-and-bound method to illustrate the performance of this model. An efficient simulated annealing (SA) algorithm with elaborately designed solution representation and neighborhood generation is extended to solve the proposed model because of its NP-hardness. It is then tested using several problems with different sizes and settings to verify the computational efficiency of the developed algorithm in comparison with the Lingo software. The obtained results show that the proposed SA is able to find the near-optimal solutions in computational time, approximately 100 times less than Lingo. Also, the computational results show that the proposed model to some extent overcomes common disadvantages in the existing dynamic cell formation models that have not yet considered layout problems.

Layout Design for Efficient Material Flow Path

Traditionally the design of physical layout of the manufacturing system (Inter-cell layout) and subsequently the design of Material Handling System (MHS) is being carried out in isolation. In this work an attempt is made to concurrently design Inter -Cell Layout and the MHS using a Genetic Algorithm based methodology for a Cellular Manufacturing System (CMS) environment under open field configuration. The proposed algorithm is employed to simultaneously optimize two contradicting objectives viz. 1. Total material handling cost (TMHC) 2.Total corner score (TCS). The algorithm is tested on four different bench mark layouts and with different initial problem data sets. It is found that the proposed algorithm is able to produce satisfactory solutions consistently within a reasonable computational limit.

An intelligent hybrid model for the integrated design of cellular manufacturing systems

The design of cellular manufacturing systems (CMS) mainly involves three interrelated phases: cell formation, intracell layout (machine layout), and intercell layout (cell layout). Most of the techniques for the design of CMS considered only the cell formation phase and only a few researchers for the design of CMS involved three interrelated phases separately and sequentially without evaluating the effect of each phase on the other phases. This sequential approach may be efficient to one particular phase but need not be efficient for the overall design of CMS. In this paper, we propose a mathematical model for integration of three interrelated phases and an intelligent hybrid algorithm that uses simulated annealing and genetic algorithms to solve the problem. The proposed model is validated using examples from literature.