Integrated job shop scheduling and layout planning: a hybrid evolutionary method for optimizing multiple objectives

Abstract

The facility layout planning (FLP) and the job shop scheduling problem (JSSP) are two major design issues that impact on the efficiency and productivity of manufacturing systems. The interactions between these two combinatorial optimization problems are widely known. Although, a great deal of research has been focused on solving these problems, relatively few techniques have been developed for solving them as an inter-dependent problem, none of which consider multiple objectives to better reflect practical manufacturing scenarios. Also, traditional approaches do not consider the transportation delay between two consecutive operations while solving JSSPs. Focusing on the autonomy of the manufacturing environment, this paper presents a multi-objective evolutionary method for solving JSSP that considers transportation delays and FLP as an integrated problem, which presents the final solutions as a Pareto-optimal set. In this research, a hybrid genetic algorithm by incorporating variable neighborhood search is applied to simultaneously optimize makespan and mean flow time for JSSPs, as well as total material handling cost and closeness rating scores for FLPs. This is an extension to the authors’ previous work.