Cell Production System Design: A Literature Review

Purpose In a cell production system, a number of machines that differ in function are housed in the same cell. The task of these cells is to complete operations on similar parts that are in the same group. Determining the family of machine parts and cells is one of the major design problems of production cells. Cell production system design methods include clustering, graph theory, artificial intelligence, meta-heuristic, simulation, mathematical programming. This article discusses the operation of methods and research in the field of cell production system design. Methodology: To examine these methods, from 187 articles published in this field by authoritative scientific sources, based on the year of publication and the number of restrictions considered and close to reality, which are searched using the keywords of these restrictions and among them articles Various aspects of production and design problems, such as considering machine costs and cell size and process routing, have been selected simultaneously. Findings: Finally, the distribution diagram of the use of these methods and the limitations considered by their researchers, shows the use and efficiency of each of these methods. By examining them, more efficient and efficient design fields of this type of production system can be identified. Originality/Value: In this article, the literature on cell production system from 1972 to 2021 has been reviewed

Virtual Cellular Multi-period Formation under the Dynamic Environment

AbstractVirtual cellular manufacturing is an innovative way of production organization which both in the production of flexibility and efficient to meet today's rapid development of science and technology and replacement of products. The key process of the design of virtual cellular manufacturing system—cell formation is the focus of research. In order to meet the characteristics of small batch and dynamically changing market demand, this paper studies the problems of virtual cellular multi-period dynamic reconfiguration. A reconfigurable system programming model is developed. The model incorporates parameters of the problems of product dynamic demand, machine capacity, operation sequence, balanced workload, alternative routings and batch setting. The objective of mixed integer programming model is to minimize the total costs of operation, moving raw materials, inventory holding and process routes setup. Though a case study, demonstrates the feasibility and validity of the model in reality

The evolution of cell formation problem methodologies based on recent studies (1997-2008): review and directions for future research

This paper presents a literature review of the cell formation (CF) problem concentrating on formulations proposed in the last decade. It refers to a number of solution approaches that have been employed for CF such as mathematical programming, heuristic and metaheuristic methodologies and artificial intelligence strategies. A comparison and evaluation of all methodologies is attempted and some shortcomings are highlighted. Finally, suggestions for future research are proposed useful for CF researchers

PRABHA - A New Heuristic Approach For Machine Cell Formation Under Dynamic Production Environments

Over the past three decades, Cellular Manufacturing Systems (CMS) have attracted a lot of attention from manufacturers because of its positive impacts on analysis of batch-type production and also a wide range of potential application areas. Machine cell formation and part family creation are two important tasks of cellular manufacturing systems. Most of the current CMS design methods have been developed for a static production environment. This paper addresses the problem of machine cell formation and part family formation for a dynamic production requirement with the objective of minimizing the material handling cost, penalty for cell load variation and the machine relocation cost. The parameters considered include demand of parts in different period, routing sequences, processing time and machine capacities. In this work a new heuristic approach named PRABHA is proposed for machine cell formation and the part family formation. The computational results of the proposed heuristics approach were obtained and compared with the Genetic Algorithm approach and it was found that the proposed heuristics PRABHA outperforms the Genetic Algorithm

Dynamic Facility Layout for Cellular and Reconfigurable Manufacturing using Dynamic Programming and Multi-Objective Metaheuristics

The facility layout problem is one of the most classical yet influential problems in the planning of production systems. A well-designed layout minimizes the material handling costs (MHC), personnel flow distances, work in process, and improves the performance of these systems in terms of operating costs and time. Because of this importance, facility layout has a rich literature in industrial engineering and operations research. Facility layout problems (FLPs) are generally concerned with positioning a set of facilities to satisfy some criteria or objectives under certain constraints. Traditional FLPs try to put facilities with the high material flow as close as possible to minimize the MHC. In static facility layout problems (SFLP), the product demands and mixes are considered deterministic parameters with constant values. The material flow between facilities is fixed over the planning horizon. However, in today’s market, manufacturing systems are constantly facing changes in product demands and mixes. These changes make it necessary to change the layout from one period to the other to be adapted to the changes. Consequently, there is a need for dynamic approaches of FLP that aim to generate layouts with high adaptation concerning changes in product demand and mix. This thesis focuses on studying the layout problems, with an emphasis on the changing environment of manufacturing systems. Despite the fact that designing layouts within the dynamic environment context is more realistic, the SFLP is observed to have been remained worthy to be analyzed. Hence, a math-heuristic approach is developed to solve an SFLP. To this aim, first, the facilities are grouped into many possible vertical clusters, second, the best combination of the generated clusters to be in the final layout are selected by solving a linear programming model, and finally, the selected clusters are sequenced within the shop floor. Although the presented math-heuristic approach is effective in solving SFLP, applying approaches to cope with the changing manufacturing environment is required. One of the most well-known approaches to deal with the changing manufacturing environment is the dynamic facility layout problem (DFLP). DFLP suits reconfigurable manufacturing systems since their machinery and material handling devices are reconfigurable to encounter the new necessities for the variations of product mix and demand. In DFLP, the planning horizon is divided into some periods. The goal is to find a layout for each period to minimize the total MHC for all periods and the total rearrangement costs between the periods. Dynamic programming (DP) has been known as one of the effective methods to optimize DFLP. In the DP method, all the possible layouts for every single period are generated and given to DP as its state-space. However, by increasing the number of facilities, it is impossible to give all the possible layouts to DP and only a restricted number of layouts should be fed to DP. This leads to ignoring some layouts and losing the optimality; to deal with this difficulty, an improved DP approach is proposed. It uses a hybrid metaheuristic algorithm to select the initial layouts for DP that lead to the best solution of DP for DFLP. The proposed approach includes two phases. In the first phase, a large set of layouts are generated through a heuristic method. In the second phase, a genetic algorithm (GA) is applied to search for the best subset of layouts to be given to DP. DP, improved by starting with the most promising initial layouts, is applied to find the multi-period layout. Finally, a tabu search algorithm is utilized for further improvement of the solution obtained by improved DP. Computational experiments show that improved DP provides more efficient solutions than DP approaches in the literature. The improved DP can efficiently solve DFLP and find the best layout for each period considering both material handling and layout rearrangement costs. However, rearrangement costs may include some unpredictable costs concerning interruption in production or moving of facilities. Therefore, in some cases, managerial decisions tend to avoid any rearrangements. To this aim, a semi-robust approach is developed to optimize an FLP in a cellular manufacturing system (CMS). In this approach, the pick-up/drop-off (P/D) points of the cells are changed to adapt the layout with changes in product demand and mix. This approach suits more a cellular flexible manufacturing system or a conventional system. A multi-objective nonlinear mixed-integer programming model is proposed to simultaneously search for the optimum number of cells, optimum allocation of facilities to cells, optimum intra- and inter-cellular layout design, and the optimum locations of the P/D points of the cells in each period. A modified non-dominated sorting genetic algorithm (MNSGA-II) enhanced by an improved non-dominated sorting strategy and a modified dynamic crowding distance procedure is used to find Pareto-optimal solutions. The computational experiments are carried out to show the effectiveness of the proposed MNSGA-II against other popular metaheuristic algorithms

An integrated framework for design, management and operation of reconfigurable assembly systems

Abstract Manufacturing has to cope with the continuously increasing variety of products, change of volumes and shortening product life cycles. These trends also affect the automotive sector: the frequent introduction of new models, materials and assembly technologies put the suppliers of make-to-order parts under pressure. In this context, the design of assembly systems and their management are of paramount importance for the companies’ competitiveness. In this paper, we propose an approach for the design and reconfiguration of modular assembly systems through the integration of different computational tools addressing the design of the system, the optimization of the layout, the planning of reconfiguration actions as well as production planning. Integrating these computational tools and iterating through the resulting workflow and feedback allow to consider the outcomes and dependencies of alternative decision sequences holistically with the objective of an effective and efficient approach to production system design and management. The viability of the approach is demonstrated through the application to an automotive case study

Conception de systèmes manufacturiers cellulaires dans un environnement de chaînes d'approvisionnement

Classical cellular manufacturing system (CMS) design are performed on a single plant and are no more appropriate in an environment where material procurement and Customer delivery processes issues with the selection of multi-plant production system locations have to be considered simultaneously. Integration of these issues is a crucial challenge to supply chain managers. This thesis deals with the multi-plant CMS design in a supply chain environment. This context is established with considering the supply or the customer delivery processes, and with linked manufacturing plants. Three problems are examined. The first problem considers linked multi-plant CMS design integrated to material supply process. The solution approach is based on a proposed linearised model where the total system design cost to minimise combines the cost of the cellular configuration set on existing plants and the costs linked to supplier process selection. Experimentation demonstrates the potential benefits gained through increasing routing flexibility over plants on investment costs and the effect of integrating the supply process in a multi-plant cellular manufacturing configuration. The second problem combines multi-plant CMS design to manufacturing plant selection and customer demand allocation decisions. We propose a mathematical model and develop a simulated annealing based approach which aims to select the manufacturing plants to open and define the machine cells locations and structures and assign customer’s demand. We demonstrate the efficiency of the solution approach and show the cost savings due to integrated multi-stage multi-plant CMS with customer allocation decisions, compared to a sequential decision process. The third problem examines a context of multi-period customer demand where multi-plant production planning decisions, part transfer between plants, system reconfiguration and dynamic customer allocation decisions are coupled with dynamic multi-plant CMS configuration. It is shown that considering these issues in an integrated model reduces total design cost and enhances manufacturing and delivery flexibility