A hybrid multi-objective grey wolf optimizer for dynamic scheduling in a real-world welding industry

Abstract

Welding is one of the most important technologies in manufacturing industries due to its extensive applications. Welding scheduling can affect the efficiency of the welding process greatly. Thus, welding scheduling problem is important in welding production. This paper studies a challenging problem of dynamic scheduling in a real-world welding industry. To satisfy needs of dynamic production, three types of dynamic events, namely, machine breakdown, job with poor quality and job release delay, are considered. Furthermore, controllable processing times (CPT), sequence-dependent setup times (SDST) and job-dependent transportation times (JDTT) are also considered. Firstly, we formulate a model for the multi-objective dynamic welding scheduling problem (MODWSP). The objectives are to minimize the makespan, machine load and instability simultaneously. Secondly, we develop a hybrid multi-objective grey wolf optimizer (HMOGWO) to solve this MODWSP. In the HMOGWO, a modified social hierarchy is designed to improve its exploitation and exploration abilities. To further enhance the exploration, genetic operator is embedded into the HMOGWO. Since one characteristic of this problem is that multiple machines can handle one operation at a time, the solution is encoded as a two-part representation including a permutation vector and a machine assignment matrix. To evaluate the effectiveness of the proposed HMOGWO, we compare it with other well-known multi-objective metaheuristics including NSGA-II, SPEA2, and multi-objective grey wolf optimizer. Experimental studies demonstrate that the proposed HMOGWO outperforms other algorithms in terms of convergence, spread and coverage. In addition, the case study shows that this method can solve the real-world welding scheduling problem well.