GA approach to optimise material flow and makespan in an AGV-based flexible jobshop manufacturing system

Flexible jobshop scheduling problem (FJSP) is an extended traditional jobshop scheduling problem, which more approximates to practical scheduling problems. This paper presents a genetic algorithm–based (GA) algorithm to solve the multi objective FJSP. Flexible jobshop manufacturing system (FJMS) is a complex network of processing, inspecting, and buffering nodes connected by system of transportation mechanisms. For an FJMS, it is desirable to be capable to increase or decrease the output with the rise and fall of demand. Such specifications show the complexity of decision making in the field of FJMSs and the need for concise and accurate modelling methods. Therefore, in this paper, an AGV–based flexible jobshop automated manufacturing system is considered to optimise the material flow and makespan. The flexibility is on the multishops of the same type and also multiple products that can be produced. An automated guided vehicle is applied for material handling. The objective is to optimise the material flow regarding the demand fluctuations and machine specifications and the makespan. An illustrative example is adopted from the literature to test the validity of the proposed algorithm.