A multi-strategy self-adaptive differential evolution algorithm for assembly hybrid flowshop lot-streaming scheduling with component sharing

Abstract

Lot-streaming helps to achieve a more balanced utilization of parallel machines and more timely assembly of components, while component sharing increases the flexibility and commonality of assembly operations. Thus, this work addresses an assembly hybrid flowshop lot-streaming scheduling problem with component sharing. A mixed-integer linear programming model is formulated to scrutinize the coupling relations among variables i.e. sub-lot splitting, machine allocation, processing sequencing, and assembly sequencing, and to minimize the maximum completion time and work-in-process inventory lexicographically. To solve the above problem efficiently, a multi-strategy self-adaptive differential evolution (MSDE) algorithm is developed. In MSDE, three problem-specific strategies that consider component integrity and specific requirements of production and assembly are integrated to enhance the initial population in terms of diversity and solution quality. A Q-learning-based selection mechanism is proposed to self-adaptively select an appropriate combination from mutation and crossover operators for achieving a balance between exploration and exploitation. An inventory reduction strategy is appended to largely reduce work-in-process components without extending completion time. Four conclusions are drawn from extensive experiments: (1) The ensemble of three population initialization strategies is superior to each individual one; (2) The Q-learning-based optimizer selection is more effective and robust than the single optimizer-based one; (3) The work-in-process inventory reduction strategy demonstrates remarkable effectiveness for most solutions; (4) MSDE outperforms the existing state-of-the-art algorithms in most cases.