An effective hybrid algorithm for joint scheduling of machines and AGVs in flexible job shop

作者：Xiaoyu Wen, Yunzhan Fu, Wenchao Yang, Haoqi Wang, Yuyan Zhang, Chunya Sun · 发表于：Measurement and Control · 年份：2023 · DOI：10.1177/00202940231173750 · 被引用次数：33 · 研究领域：Scheduling and Optimization Algorithms、Advanced Manufacturing and Logistics Optimization、Assembly Line Balancing Optimization

Flexible job shops motivated by small batches and multiple orders require the collaboration of machines and automated guided vehicles (AGVs) scheduling to boost shop floor flexibility and productivity. The joint scheduling of machines and AGVs can better achieve global optimization. However, joint scheduling requires two NP hard problems to be solved simultaneously. Therefore, this paper employs a multi-AGV flexible job shop scheduling problem (MA-FJSP) with an effective hybrid algorithm. First of all, a model is established with the objectives of minimizing the makespan, the total AGV running time and the total machine load. To solve the MA-FJSP, high-quality initialization methods and improved elite strategies are designed to improve global convergence in the proposed algorithm. In addition, a problem-knowledge-based neighborhood search is integrated to improve its exploitation capability. At last, a series of comparative experimental studies were performed to exam the effectiveness of the improved algorithm. The results demonstrate that the solutions gained by the proposed algorithm perform well in respect of convergence, diversity and distribution.