Dynamic Cellular Manufacturing Research Articles

An archived multi-objective simulated annealing for a dynamic cellular manufacturing system

To design a group layout of a cellular manu- facturing system (CMS) in a dynamic environment, a multi-objective mixed-integer non-linear programming model is developed. The model integrates cell formation, group layout and production planning (PP) as three inter- related decisions involved in the design of a CMS. This paper provides an extensive coverage of important manu- facturing features used in the design of CMSs and enhances the flexibility of an existing model in handling the fluctu- ations of part demands more economically by adding machine depot and PP decisions. Two conflicting objec- tives to be minimized are the total costs and the imbalance of workload among cells. As the considered objectives in this model are in conflict with each other, an archived multi-objective simulated annealing (AMOSA) algorithm is designed to find Pareto-optimal solutions. Matrix-based solution representation, a heuristic procedure generating an initial and feasible solution and efficient mutation operators are the advantages of the designed AMOSA. To demon- strate the efficiency of the proposed algorithm, the per- formance of AMOSA is compared with an exact algorithm (i.e., (-constraint method) solved by the GAMS software and a well-known evolutionary algorithm, namely NSGA- II for some randomly generated problems based on some comparison metrics. The obtained results show that the designed AMOSA can obtain satisfactory solutions for the multi-objective model.

A Robust Model for a Dynamic Cellular Manufacturing System with Production Planning

In this paper, a robust optimization approach is proposed to design a dynamic cellular manufacturing system (DCMS) under uncertainty of processing time of products. In addition, a mathematical model considering cell formation, inter-cell design and production planning under a dynamic environment (i.e., product mix and demand are changed in each period) is presented. Therefore, reconfiguration becomes necessary for one period in relative with previous periods. The aim of this model is to minimize inter and intra-cell material handling movements, inventory holding, back order and reconfiguration costs. Furthermore, alternative routing for each part is considered, in which one of its routing with the lowest cost is chosen among its other routings for each part. Finally, a numerical example is solved to demonstrate the behavior of the presented model and verify the performance of the developed approach.

New bi-objective robust design-based utilisation towards dynamic cell formation problem with fuzzy random demands

Generally, one of the most important issues which cause a production system not to be an efficient one is unemployed capacities of both machines and workers. On the other hand, adaptive design’s most exploiting facilities are not practical for any cases (huge machines). Hence, sometimes manufacturing firms are in need of robust design. In this paper, a robust design is considered to configure cells in a dynamic cellular manufacturing system to overcome the onerous machine relocation costs. Most of researches on robust design are trying to reduce the number of facilities while, here, this idea is adopted accompanying with practical concepts of facility utilisation with new mathematical formulae. Then, a multi-objective mathematical model containing the minimisation of the inter-cell movements besides maximising the machine and worker utilisation is developed. Some other supplements are the following: (1) considering a cubic space representation for calculation of inter-cell movements and (2) fuzzy random nature of part demands. Above all, a new goal programming method named ‘percentage multi-choice goal programming’ (PMCGP) is innovated to solve the proposed multi-objective model. Finally, a comprehensive numerical example is generated randomly to verify the proposed model.

Design and reconfiguration models for dynamic cellular manufacturing to handle market changes

In today’s competitive environment, customer demand has become increasingly diversified and customised. In order to stay competitive, the manufacturing companies need to adopt models that are able to rapidly adapt to market changes. The reconfigurable manufacturing system emerges from the scientific literature as a relevant manufacturing paradigm to meet changes and uncertainties in a dynamic manufacturing environment. This research proposes a cellular manufacturing system (CMS) with reconfigurable machines to handle the turbulent market conditions. The approach proposed concerns three mathematical models to support the design, reconfiguration and scheduling of the manufacturing system. The three linked models are developed in LINGO® package. The main optimisation model concerns the design of the CMS, considering product demand not deterministic but described by several discrete probability scenarios. Then, an optimisation sub-model has been developed to manage the reconfiguration at fixed periods. An allocation model is used to allocate the jobs to the manufacturing cells. The proposed approach is compared to a model without reconfiguration activities. A further configuration of the models is the possibility of using remainder cells. The simulation results show that the proposed approach is a promising methodology to support the dynamic CMSs with reconfigurable machines.

Solving a multi-floor layout design model of a dynamic cellular manufacturing system by an efficient genetic algorithm

This paper presents a mixed-integer programming model for a multi-floor layout design of cellular manufacturing systems (CMSs) in a dynamic environment. A novel aspect of this model is to concurrently determine the cell formation (CF) and group layout (GL) as the interrelated decisions involved in the design of a CMS in order to achieve an optimal (or near-optimal) design solution for a multi-floor factory in a multi-period planning horizon. Other design aspects are to design a multi-floor layout to form cells in different floors, a multi-rows layout of equal area facilities in each cell, flexible reconfigurations of cells during successive periods, distance-based material handling cost, and machine depot keeping idle machines. This model incorporates with an extensive coverage of important manufacturing features used in the design of CMSs. The objective is to minimize the total costs of intra-cell, inter-cell, and inter-floor material handling, purchasing machines, machine processing, machine overhead, and machine relocation. Two numerical examples are solved by the CPLEX software to verify the performance of the presented model and illustrate the model features. Since this model belongs to NP-hard class, an efficient genetic algorithm (GA) with a matrix-based chromosome structure is proposed to derive near-optimal solutions. To verify its computational efficiency in comparison to the CPLEX software, several test problems with different sizes and settings are implemented. The efficiency of the proposed GA in terms of the objective function value and computational time is proved by the obtained results.

A hybrid genetic and imperialist competitive algorithm approach to dynamic cellular manufacturing system

Cellular manufacturing system is an important application of group technology, which deals with grouping the parts and machines into cells, based on their similarity in design or production process. Since a purpose of this article is designing the cells under indefinite demand values up to the end of previous period, a dynamic multi-objective mathematical model based on this condition is proposed, and a heuristic approach has been developed in order to solve the problem. During this procedure, since the proposed model is a nondeterministic polynomial-time-hard problem, a new hybrid meta-heuristic approach called genetic imperialist competitive algorithm is implemented to solve it. Also, an experimental design has been used for tuning essential parameters. The proposed model has been verified by some hypothetical numerical examples with different input parameters and also by a real-world case study taken from a glass mold company. The comparison of results of the proposed genetic imperialist competitive algorithm and the genetic algorithm verifies the efficiency of the proposed solution approach. Also, the results show that the accuracy of the proposed algorithm is comparable with the exact solution methods such as branch and bound algorithm in both solution quality and computational time aspects.

Dynamic Cell Formation based on Multi-objective Optimization Model

In this paper, a multi-objective model is proposed to address the dynamic cellular manufacturing (DCM) formation problem. This model considers four conflicting objectives: relocation cost, machine utilization, material handling cost and maintenance cost. The model also considers the situation that some machines could be shared by more than one cell at the same period. A genetic algorithm is applied to get the solution of this mathematical model. Three numerical examples are simulated to evaluate the validity of this model.

A SIMULATED ANNEALING FOR INTRA-CELL LAYOUT DESIGN OF DYNAMIC CELLULAR MANUFACTURING SYSTEMS WITH ROUTE SELECTION, PURCHASING MACHINES AND CELL RECONFIGURATION

This paper develops a novel mixed integer nonlinear programming model for the intra-cell layout design of dynamic cellular manufacturing systems. In dynamic environment, the product mix and part demand are varying during a multi-period planning horizon. As a result, the cell configuration for one period may not be efficient for successive periods and thus necessitates reconfigurations. The proposed model incorporates several design features including intra-cell layout, operation sequence, operation time, alternative process routings, duplicate machines, purchase machine, machine capacity, route selection, production volume of parts, part movements in batch and cell reconfiguration. By considering intra-cell layout and operation sequence, the material handling volume and related cost is calculated more exactly. The objective is to minimize the total costs of inter-cell material handling, forward and backward intra-cell material handling, setting up route, machine relocation, purchasing new machines, machine overhead and machine processing. The main constraints are route selection among flexible routings, machine availability, cell size, machine time-capacity and machine location. The proposed model cannot be solved for large-sized problems optimally within a reasonable amount of computational time. Therefore, an efficient simulated annealing algorithm is developed to overcome NP-hardness of the proposed model.

A multi-objective model for designing a group layout of a dynamic cellular manufacturing system

This paper presents a multi-objective mixed-integer nonlinear programming model to design a group layout of a cellular manufacturing system in a dynamic environment, in which the number of cells to be formed is variable. Cell formation (CF) and group layout (GL) are concurrently made in a dynamic environment by the integrated model, which incorporates with an extensive coverage of important manufacturing features used in the design of CMSs. Additionally, there are some features that make the presented model different from the previous studies. These features include the following: (1) the variable number of cells, (2) the integrated CF and GL decisions in a dynamic environment by a multi-objective mathematical model, and (3) two conflicting objectives that minimize the total costs (i.e., costs of intra and inter-cell material handling, machine relocation, purchasing new machines, machine overhead, machine processing, and forming cells) and minimize the imbalance of workload among cells. Furthermore, the presented model considers some limitations, such as machine capability, machine capacity, part demands satisfaction, cell size, material flow conservation, and location assignment. Four numerical examples are solved by the GAMS software to illustrate the promising results obtained by the incorporated features.

Designing a new integrated model for dynamic cellular manufacturing systems with production planning and intra-cell layout

This paper investigates the problem of designing cellular manufacturing systems incorporating several design features including multi-period production planning, sequence of operations, alternate process routings, intra-cell layout, dynamic system reconfiguration, duplicate machines, machine capacity, lot splitting, and material flow between machines in a dynamic environment. The problem is formulated through a comprehensive integer linear programming model. The objective is to minimise the total costs of inter-cell material handling, forward and backward intra-cell material handling, machine operating, machine maintenance and overhead, cell reconfiguration, outsourcing and inventory holding. The main constraints are demand satisfaction, cell size, machine availability, machine time-capacity, material flow conservation and machine location. Computational results are presented by solving some numerical examples to verify the model performance and illustrate its advantageous aspects.

A robust optimisation model for manufacturing cell design problem under uncertainty

The concern about significant changes in the manufacturing environment (such as operating/part costs and part demands) has spurred an interest in designing robust dynamic cellular manufacturing systems (RDCMS). Here, first a deterministic mixed-integer linear programming (MILP) model for cell formation is proposed. Cost parameters of model and demand fluctuations are subject to uncertainty. The objective function calculates machine costs, operating cost, internal part production cost, inter-cellular material handling cost, backorder cost, inventory holding cost and subcontracting. Then the robust counterpart of the proposed mixed integer linear programming model is presented by using the recent extensions in robust optimisation theory. Finally, to assess the robustness of the solutions obtained by the novel robust optimisation model, they are compared to those generated by the deterministic mixed-integer linear programming model in a number of realisations under different test problems. Numerical tests show the power of the proposed robust model in handling uncertainty in parameters and generating robust optimal solutions.

Utilizing the Modified Self-Adaptive Differential Evolution Algorithm in Dynamic Cellular Manufacturing System

Today, Cellular Manufacturing Systems (CMS) have been introduced as a mixture of work-shop manufacturing and line-production systems for keeping efficiency and flexibility synchronously. One of the difficult steps of designing CMS is the Cell Formation (CF) problem in which parts with similar processes are made in one cell. Solving a dynamic integer model of CF with three sub-objective functions is considered using evolutionary algorithms. Due to the fact that CF is a NP-hard problem, solving the model using classical optimization methods needs long computational time. In this paper, a nonlinear integer model of CF is presented and then solved by proposed Modified Self-adaptive Differential Evolution (MSDE) and Modified Genetic Algorithm (MGA) using a set of 25 test problems. The results are compared with the optimal solution, and the efficiency of MSDE algorithm is discussed.

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.

Production planning and worker assignment in a dynamic virtual cellular manufacturing system

Most traditional cell formation problem (CFP) procedures disregard any changes in demand and part mix variation. However, in today’s business environment, the life cycle of goods is short, and demand volumes and product mix can frequently vary. In this article, a mathematical model for production planning in a dynamic virtual environment is proposed which includes an extensive coverage of the important manufacturing features, including a consideration of multi-period planning horizons with demand and part mix variation, machine capacity, the main constraints are demand satisfaction in all over period, machine availability, machine time-capacity, and availability of workers. VCMS combines the robustness and flexibility of process orientation with the advantages of product orientation, which is expected to appreciate throughput time performance. To validate and verify the proposed model, computational results are presented by solving a number of numerical examples, with a linearized formulation.

Designing an intra-cell layout model in dynamic cellular manufacturing systems with unequal-area facilities

This paper presents a novel mixed-integer non-linear programming model for an intra-cell layout (ICL) design for a dynamic cellular manufacturing system (DCMS). In a dynamic environment, the product mix and part demands vary during a multi-period planning horizon. As a result, the best cell configuration and part routings for one period may not be efficient for successive periods, and necessitating reconfiguration. A novel aspect of this model is the concurrent making of the cell formations and ICL decisions in a dynamic environment. Another aspect of this model is the utilization of a multi-row machine layout for machines of unequal-area. The DCMS model presented incorporates several design features including alternative process routings, operation sequencing, processing time, parts production volume, duplicate machines, machine capacity, ICL, multi-row layout of unequal-area facilities, continuous-area cells and machine relocation. The objective of the integrated model is to minimize the total costs of intra and inter-cell material handling, machine relocation, machine overheads and machine processing. The main constraints are part processing requirements, machine availability, maximal and minimal cell size, machine time-capacity and the non-overlapping of facilities. A comprehensive example is solved using Lingo software to verify the performance of the proposed model. Also, the computational results show that to some extent the proposed model overcomes common disadvantages in the existing dynamic cell formation models that have not yet considered layout problems of unequal-area facilities.

Dynamic cellular manufacturing design and its sensitivity analysis

Numbers of research papers have been dedicated for the design of cell formation which is NP-complete even under fairly restrictive conditions (Ballakur, 1985). In most of these design formulations, the demand for product-mix is considered to be independent of time. In practice, the demand is always varying with time. It is need of time that the manufacturing cell should be so configured that it can accommodate the fluctuation of demand of products with time. The effectiveness of a cellular manufacturing system is prone to fluctuations in the demand for products and the product-mix. This paper presents a solution approach that incorporates the dynamic nature of the production environment by considering a multi-period forecast of product-mix in the formation of part families and machine cells. An approach of solution methodology for formation of manufacturing cell and the effect of critical production parameters has been described in an uncertain product mix environment.

A multi-objective integrated cellular manufacturing systems design with production planning, worker assignment and dynamic system reconfiguration

This paper offers a new mathematical model for the design of cell formation in dynamic cellular manufacturing system (DCMS) based on production planning (PP) and worker assignment. Here, workers will assign on machines by their skills. In dynamic environment, the best-designed cells for one period may not be efficient for subsequent periods and reconfiguration of cells is required. Reconfiguration may involve adding, removing or relocating machines; it may also involve a change in processing rout of part types from a period to another. Here, the aim of the model is to develop a multi-objective DCMS model that integrates multi-period PP, dynamic system reconfiguration and available time of workers, worker assignment and several other attributes. Consequently, a multi-objective dynamic cell formation problem is presented, where the total cells load variation (the second objective function) and sum of the miscellaneous costs (the first objective function) are to be minimised simultaneously. For solving this type of problems, it is reasonable to investigate a set of solutions, so I use fast and elitist multi-objective genetic algorithm for solving the mathematical model to find Pareto-optimal solutions and numerical results show effectiveness of the proposed model.

An integrated model of dynamic cellular manufacturing and supply chain system design

In a global dynamic environment, there is a need to develop organizations and facilities significantly more flexible and responsive. This work proposes an integrated model of dynamic cellular manufacturing and supply chain design with consideration of various issues such as multi-plant locations, multiple markets, multi-time periods, reconfiguration, etc. The model objective was to minimize the sum of various costs such as facility/plant to market transportation cost, part holding cost at a facility/plant, part outsourcing cost, machine procurement cost, machine maintenance overhead cost, machine repair cost, production loss cost due to machine breakdown, machine operation cost, setup cost, tool consumption cost, inter-cell travel cost, intra-cell travel cost, and system reconfiguration cost for the entire planning time horizon. To study the model, three procedures—LINGO, artificial immune system, and hybrid artificial immune system—are used to perform computational experiment on some problems from existing literature. The best result generally is found by the hybrid artificial immune system algorithm.

Multi-objective cell formation and production planning in dynamic virtual cellular manufacturing systems

This article presents a fuzzy goal programming-based approach for solving a multi-objective mathematical model of cell formation problem and production planning in a dynamic virtual cellular manufacturing system. In a dynamic environment, the product mix and part demand change over a planning horizon decomposed into several time periods. Thus, the cell formation done for one period may be no longer efficient for subsequent periods and hence reconfiguration of cells is required. Due to the variation of demand and necessity of reconfiguration of cells, the virtual cellular manufacturing (VCM) concept has been proposed by researchers to utilise the benefits of cellular manufacturing without reconfiguration charges. In a VCM system, machines, parts and workers are temporarily grouped for one period during which machines and workers of a group dedicatedly serve the parts of that group. The only difference of VCM with a real CM is that machines of the same group are not necessarily brought to a physical proximity in VCM. The virtual cells are created periodically depending on changes in demand volumes and mix, as new parts accumulate during a planning horizon. The major advantage of the proposed model is the consideration of demand and part mix variation over a multi-period planning horizon with worker flexibility. The aim is to minimise holding cost, backorder cost and exceptional elements in a cubic space of machine–part–worker incidence matrix. To illustrate the applicability of the proposed model, an example has been solved and computational results are presented.

A fuzzy linear programming approach to layout design of dynamic cellular manufacturing systems with route selection and cell reconfiguration

This paper presents a novel integer non-linear programming model for the layout design of dynamic cellular manufacturing systems (DCMS) under an uncertain environment. A novel aspect of this model is concurrently making the interrelated cell formation and intracell layout decisions in a dynamic and uncertain environment. Other compromising aspects are: considering single-row layout of equal area facilities to locate machines in each cell, machine relocation based on locations assigned to a machine during two successive periods, calculation of material handling cost based on the distance between the locations assigned to machines, and presenting the existence of uncertainty in the model parameters by fuzzy numbers. Such an integrated model with an extensive coverage of important manufacturing features has not been proposed before and incorporates several design features including intracell layout, operation sequence, operation time, alternative process routing, duplicate machines, machine capacity, route selection, production volume of parts and cell reconfiguration. The uncertainty stems from part demand fluctuation and machine capacity. Linearization procedures are used to transform the proposed non-linear programming model into a linearized formulation. Finally, a new fuzzy linear programming approach which can consider the fuzziness of whole parameters in a mathematical model is developed to solve the linearized model. A comprehensive example is solved by the Lingo software to verify the performance of the proposed model and developed fuzzy approach. 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 and fuzzy issues.