The design of a cellular manufacturing system requires that a part population, at least minimally described by its use of process technology (part/machine incidence matrix), be partitioned into part families and that the associated plant equipment be partitioned into machine cells. At the highest level, the objective is to form a set of completely autonomous units such that inter-cell movement of parts is minimized. We present an integer program that is solved using a genetic algorithm (GA) to assist in the design of cellular manufacturing systems. The formulation uses a unique representation scheme for individuals (part/machine partitions) that reduces the size of the cell formation problem and increases the scale of problems that can be solved. This approach offers improved design flexibility by allowing a variety of evaluation functions to be employed and by incorporating design constraints during cell formation. The effectiveness of the GA approach is demonstrated on several problems from the literature.

A comparison of heuristic methods for

This paper presents a new evolutionary approach to the machine-part cell formation (MPCF) problem, generally considered in manufacturing cell design, where a zero-one machine-part matrix must have its rows and columns moved to form machines and parts clusters. The Constructive Genetic Algorithm (CGA) was proposed recently to solve clustering problems, and is applied here to the MPCF. The MPCF is modeled as a bi-objective problem that guides the construction of feasible assignments of machines and parts to specify clusters, and provides evaluation of schemata and structures in a common basis. A particularly derived structure and schema representation considers Jaccard distances for binary strings. A variable size population is formed only by schemata, considered as building blocks for feasible solutions construction along the generations. Recombination gives population diversification, and local search mutation is applied to structures that represent feasible solutions. Experimental res...

this paper is to provide a review and comparison of the approaches to multi-criteria decision-making (MCDM) in the design of manu - facturing cells. A brief description of existing classi cations is provided, together with an overview on the MCDM. Selected papers are reviewed and a structured scheme is outlined which allows comparison of inputs, criteria, solution approaches and outputs across selected models. Finally the models are discussed and directions for new avenues of work are identi ed

Abstract—In recent years, the process of cellular manufacturing and group technology has received much attention and popularity in many developed countries. By applying Group Technology (GT), many benefits of flow-line production can be attained in a batch production system. GT can improve material handling, significantly reduce material flow time and distance, and setup times. In this paper, a two step approach is proposed to solve the GT problem using Genetic Algorithms (GA). The first step assigns parts to the best available machines according to their required specifications. The second step forms manufacturing cells and part families. The proposed GA model has the flexibility of choosing the number of cells required, which is very useful in examining different manufacturing cell configurations; or in case that the workshop or factory prefers a certain number of cells. For example if the workshop or factory doesn't have the workspace required for more than four cells. To compare the performance of the proposed GA model, five part-machine matrices are obtained from the literature are solved using different techniques and their results are compared to the results achieved by the proposed GA model. The GA model results were found satisfactory and superior to other techniques in some cases.

Abstract — Group technology (GT) is a useful way to increase productivity with high quality in cellular manufacturing systems (CMSs), in which cell formation (CF) is a key step in the GT philosophy. When boundaries between groups are fuzzy, fuzzy clustering has been successfully adapted to solve the CF problem; however, it may result uneven distribution of parts/machines where the problem becomes larger. In this case, particle swarm optimization (PSO) can be used to tackle such a hard problem. This paper proposes a hybrid algorithm based on the fuzzy clustering and particle swarm optimization (FPSO) to solve the given CF problem. We experiment a number of examples to show the efficiency of the proposed algorithm and find that our proposed FPSO algorithm is able to obtain good results at reasonable time.

Abstract — Researchers and practitioners frequently spend more time fine-tuning algorithms than designing and implementing them. This is particularly true when developing heuristics and metaheuristics, where the “right ” choice of values for search parameters has a considerable effect on the performance of the procedure. When testing metaheuristics, performance typically is measured considering both the quality of the solutions obtained and the time needed to find them. In this paper, we describe the development of CALIBRA, a procedure that attempts to find the best values for up to five search parameters associated with a procedure under study. Since CALIBRA uses Taguchi’s fractional factorial experimental designs coupled with a local search procedure, the best values found are not guaranteed to be optimal. We test CALIBRA on six existing heuristic-based procedures. These experiments show that CALIBRA is able to find parameter values that either match or improve the performance of the procedures resulting from using the parameter values suggested by their developers. The latest version of CALIBRA can be downloaded for free from