Multi-Objective Grey Wolf Optimizer Based on Reinforcement Learning for Distributed Hybrid Flowshop Scheduling Towards Mass Personalized Manufacturing

Abstract

As an emerging production paradigm, mass personalized manufacturing (MPM) realizes personalized product customization under the premise of ensuring large-scale production. In this paradigm, the rapid switching of the type and quantity of manufacturing tasks increases the difficulty of scheduling. Hence, this paper proposes the distributed hybrid flowshop scheduling problem with an order modularization and tasks assigning method (DHFSP-OMTA), where heterogeneous customer orders are decomposed into standard and personalized production tasks and assigned to different factories. Meantime, towards MPM, a novel mixed integer linear programming model is established to minimize the makespan and total energy consumption simultaneously. Considering the high complexity of DHFSP-OMTA, a multi-objective grey wolf optimizer based on reinforcement learning (MOGWO-RL) is designed. This paper contains the following three improvements. Firstly, the variable tasks splitting method combines two heuristic-rule initialization to produce a high-quality population. Secondly, a variable neighborhood search based on reinforcement learning is designed to improve the search quality and jump out of the local optimum. Thirdly, an efficient merging batches method is presented to save transferring energy. The advantages of the proposed algorithm are verified by 18 improved test instances based on the Taillard benchmark with the MPM feature. The results show that MOGWO-RL has the best effectiveness and stability of all comparison algorithms. Therefore, it can be used as a novel method to solve MPM's scheduling problem.

Keywords: Mass personalization manufacturing, distributed hybrid flowshop scheduling, orders modularization and tasks assigning, MOGWO-RL