A comparison of heuristic methods for solving a cellular manufacturing model in a dynamic environment

A cellular manufacturing system (CMS) belongs to a family of modern production methods, which many industrial sectors have used beneficially in recent years. In fact, this system is an application of group technology (GT) determining cell formation (i.e. clustering part families and machine grouping) and layout design (i.e., inter-cell and intra-cell layouts). In the last two decades, a number of researchers have carried out scientific studies on static production and deterministic demand states. However, in the real world a CM model often consists of a large number of variables and constraints. To extract a solution from such problems requires a large amount of computer time, memory, and processing power by current optimization software packages. In this paper, a mathematical model of a nonlinear mixed-integer programming type is presented for designing CMS in a dynamic environment (DE). It considers the machine and routing flexibility in dynamic production states. This CMS model belongs to a class of NP-Complete problems that cannot be solved by traditional optimization methods. Thus, three well-known meta-heuristic methods, i.e. simulated annealing (SA), genetic algorithms (GAs), and tabu search (TS) are proposed to solve the above problem. These methods belong to a class of stochastic search algorithms. After discussing the methods and presenting the models, the associated computational results obtained by these three methods are compared with the Lingo6 software in order to validate the efficiency of the proposed algorithms.