A multi-objective evolutionary algorithm for solving integrated scheduling and layout planning problems in manufacturing systems

Due to industrial automation and the expansion of the manufacturing industry, scheduling and layout planning are crucial considerations for improving productivity and cost-controlling activities in manufacturing environments. The job shop scheduling problem (JSSP) and the facility layout planning (FLP) are both known to be NP-hard problems. A great deal of research, including the use of evolutionary algorithms, has been focused on solving these stubborn problems. The choice of layout considering the scheduling of jobs among the facilities significantly impacts the performance of a manufacturing system. The real-world FLPs and JSSPs are both multi-objective by nature and researchers have only recently modeled them with multiple objectives. Surprisingly, there is a little attention paid to date to developing an integrated approach to FLPs and JSSPs, and none at all considering multiple objectives. This paper presents a genetic algorithm for solving the integrated JSSP and FLP considering multiple objectives and Pareto-optimality. The approach is verified through numerical examples.