A strip-packing constructive algorithm with deep reinforcement learning for dynamic resource-constrained seru scheduling problems

Abstract

This study focuses on unspecified dynamic seru scheduling problems with resource constraints (UDSS-R) in seru production system (SPS). A mixed integer linear programming model is formulated to minimize the makespan, which is solved sequentially from both allocation and scheduling perspectives by a strip-packing constructive algorithm (SPCA) with deep reinforcement learning (DRL). The training samples are trained by the DRL model, and the reward values obtained are calculated by SPCA to train the network so that the agent can find a better solution. The output of DRL is the scheduling order of jobs in serus, while the solution of UDSS-R is solved by SPCA. Finally, a set of test instances are generated to conduct computational experiments with different instance scales for the DRL-SPCA, and the results confirm the effectiveness of proposed DRL-SPCA in solving UDSS-R with more outstanding performance in terms of solution quality and efficiency, across three data scales (10 serus × 100 jobs, 20 serus × 250 jobs, and 30 serus × 400 jobs), compared with GA and SAA, the Avg. RPD of DRL-SPCA decreased by 9.93% and 7.56%, 13.36% and 10.72%, and 9.09% and 7.08%, respectively. In addition, the Avg. CPU time was reduced by 29.53% and 27.93%, 57.48% and 57.04%, and 61.73% and 61.76%, respectively.