Deep reinforcement learning guided improvement heuristic for job shop scheduling

• Main Authors: ZHANG, Cong, CAO, Zhiguang, SONG, Wen, WU, Yaoxin, ZHANG, Jie
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Deep Reinforcement Learning; Graph Neural Network; Job Shop Scheduling; Combinatorial Optimization; Graphics and Human Computer Interfaces; OS and Networks
• Online Access: https://ink.library.smu.edu.sg/sis_research/9329; https://ink.library.smu.edu.sg/context/sis_research/article/10329/viewcontent/1334_Deep_Reinforcement_Learni.pdf
• Institution: Singapore Management University

Recent studies in using deep reinforcement learning (DRL) to solve Job-shop scheduling problems (JSSP) focus on construction heuristics. However, their performance is still far from optimality, mainly because the underlying graph representation scheme is unsuitable for modelling partial solutions at...