Developing a Fuzzy Mathematical Programming Model for the Allocation and Scheduling of Components in a Flexible Production System

Due to the ever-increasing changes in the surrounding world, flexibility is a solution for pioneering in the field of business. The expansion of the consumer market along with the variety of consumer demands caused these two groups not to function as efficiently as before. Thus, the optimal response to consumer needs became possible only through strategic flexibility. In this regard, one of the basic dimensions is related to a flexible manufacturing system (FMS). The aim of this study is to present a fuzzy mathematical planning model for assigning and scheduling tasks in an FMS. The main objective is to minimize the maximum time to complete the last order (max), the maximum workload of the machines (Wmax), and the total workload (W), i.e., the total workload of all machines. Since the problem of scheduling flexible workshop manufacturing is an NP-hard problem and obtaining optimal response for medium- and large-size problems is not possible in a reasonable time, innovative or metaheuristic methods should be used. Thus, NSGAII and MOGWO algorithms are used to solve this problem. While the cost of manufacturing and maintenance increased by 3-3.5%, transfer cost increased by 1.8%, which is considered to be unfavorable in terms of cost-effectiveness. Also, the average performance of MOGWO was better than NSGAII.

Flexible Manufacturing, Grey Wolf Optimizer, Genetic Algorithm, Optimization