A hybrid many-objective evolutionary algorithm for flexible job-shop scheduling problem with transportation and setup times

This paper addresses a many-objective flexible job-shop scheduling problem with transportation and setup times (MaOFJSP_T/S) where the objective is to minimize the makespan, total workload, workload of the critical machine, and penalties of earliness/tardiness. We first present a mathematical model as the representation of the problem, and then establish a network graph model to describe the structural characteristics of the problem and develop a new neighborhood structure. The neighborhood structure defines four move types for different objectives. Next, we propose a hybrid many-objective evolutionary algorithm (HMEA), which is designed to better balance exploitation and exploration. In this algorithm, the tabu search with the neighborhood structure is proposed to improve the local search ability. A reference-point based non-dominated sorting selection is presented to guide the algorithm to search towards the Pareto-optimal front and maintain diversity of solutions. Through three sets of experiments based on 28 benchmark instances, the partial and overall effects of this algorithm are evaluated. The experimental results demonstrate the effectiveness of the proposed HMEA in solving the MaOFJSPT/S.

Automated summary

This paper addresses a many-objective flexible job-shop scheduling problem and proposes a new hybrid many-objective evolutionary algorithm to solve the problem. The algorithm, using strategies such as tabu search and reference-point based non-dominated sorting selection, effectively improves the quality and diversity of solutions.