A self-learning interior search algorithm based on reinforcement learning for energy-aware job shop scheduling problem with outsourcing option

被引:5
作者
Liu, Xinyu [1 ]
Liu, Lu [2 ]
Jiang, Tianhua [2 ]
机构
[1] Yantai Automobile Engn Profess Coll, Sch Vehicle Applicat Engn, Yantai 265599, Peoples R China
[2] Ludong Univ, Sch Transportat, Yantai, Shandong, Peoples R China
关键词
Job shop; outsourcing option; energy-aware scheduling; interior search algorithm; COLONY OPTIMIZATION APPROACH; 2-MACHINE FLOW-SHOP; GENETIC ALGORITHM; TRANSPORTATION; OPERATIONS;
D O I
10.3233/JIFS-224624
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Energy-aware scheduling has been viewed as a feasible way to reduce energy consumption during the production process. Recently, energy-aware job shop scheduling problems (EAJSPs) have received wide attention in the manufacturing area. However, the majority of previous literature about EAJSPs supposed that all jobs are fabricated in the in-house workshop, while the outsourcing of jobs to some available subcontractors is neglected. To get close to practical production, the outsourcing and scheduling are simultaneously determined in an energy-aware job shop problem with outsourcing option (EAJSP-OO). To formulate the considered problem, a novel mathematical model is constructed to minimize the sum of completion time cost, outsourcing cost and energy consumption cost. Considering the strong complexity, a self-learning interior search algorithm (SLISA) is developed based on reinforcement learning. In the SLISA, a new Q-learning algorithm is embedded to dynamically select search strategies to prevent blind search in the iteration process. Extensive experiments are carried out to evaluate the performance of the proposed algorithm. Simulation results indicate that the SLISA is superior to the compared existing algorithms in more than 50% of the instances of the considered EAFJSP-OO problem.
引用
收藏
页码:10085 / 10100
页数:16
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