A heuristic-assisted deep reinforcement learning algorithm for flexible job shop scheduling with transport constraints
Abstract Automated guided vehicles (AGVs) are widely used for transportation in flexible job shop (FJS) systems, and their transportation task scheduling has the same substantial impact on production efficiency as machine scheduling does. However, traditional FJS scheduling methods often prioritize...
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Springer
2025-03-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-025-01828-6 |
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| author | Xiaoting Dong Guangxi Wan Peng Zeng |
| author_facet | Xiaoting Dong Guangxi Wan Peng Zeng |
| author_sort | Xiaoting Dong |
| collection | DOAJ |
| description | Abstract Automated guided vehicles (AGVs) are widely used for transportation in flexible job shop (FJS) systems, and their transportation task scheduling has the same substantial impact on production efficiency as machine scheduling does. However, traditional FJS scheduling methods often prioritize job sequencing and machine selection while ignoring the impact of AGV transportation, resulting in suboptimal scheduling solutions and even difficulties in implementation. To address this issue, this paper formulates a cooperative scheduling model by introducing the AGV scheduling problem into the classical FJS scheduling problem, abbreviated as the FJS-AGV problem, with the objective of minimizing the makespan. With respect to the FJS-AGV problem, a heuristic-assisted deep Q-network (HA-DQN) algorithm is proposed, which leverages heuristic rules to enable the decision agent to perform multiple actions at each decision point, which includes determining the responses to the following questions: Which operation should be processed next? On which machine? By which AGV? This decision mechanism enables the agent to make more informed decisions, leading to improved performance and resource allocation in the FJS-AGV system. The practicability of the proposed FJS-AGV model and the efficiency of the HA-DQN algorithm in solving the FJS-AGV problem are verified through various international benchmarks. Specifically, when solving instances in a large benchmark, the HA-DQN algorithm yields a significant 12.63% reduction in makespan compared with that when traditional heuristics are employed. |
| format | Article |
| id | doaj-art-e1c1fcc1e9cf4d9a898471cb3e3dd54d |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-e1c1fcc1e9cf4d9a898471cb3e3dd54d2025-08-20T03:52:28ZengSpringerComplex & Intelligent Systems2199-45362198-60532025-03-0111512110.1007/s40747-025-01828-6A heuristic-assisted deep reinforcement learning algorithm for flexible job shop scheduling with transport constraintsXiaoting Dong0Guangxi Wan1Peng Zeng2State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of SciencesState Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of SciencesState Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of SciencesAbstract Automated guided vehicles (AGVs) are widely used for transportation in flexible job shop (FJS) systems, and their transportation task scheduling has the same substantial impact on production efficiency as machine scheduling does. However, traditional FJS scheduling methods often prioritize job sequencing and machine selection while ignoring the impact of AGV transportation, resulting in suboptimal scheduling solutions and even difficulties in implementation. To address this issue, this paper formulates a cooperative scheduling model by introducing the AGV scheduling problem into the classical FJS scheduling problem, abbreviated as the FJS-AGV problem, with the objective of minimizing the makespan. With respect to the FJS-AGV problem, a heuristic-assisted deep Q-network (HA-DQN) algorithm is proposed, which leverages heuristic rules to enable the decision agent to perform multiple actions at each decision point, which includes determining the responses to the following questions: Which operation should be processed next? On which machine? By which AGV? This decision mechanism enables the agent to make more informed decisions, leading to improved performance and resource allocation in the FJS-AGV system. The practicability of the proposed FJS-AGV model and the efficiency of the HA-DQN algorithm in solving the FJS-AGV problem are verified through various international benchmarks. Specifically, when solving instances in a large benchmark, the HA-DQN algorithm yields a significant 12.63% reduction in makespan compared with that when traditional heuristics are employed.https://doi.org/10.1007/s40747-025-01828-6Flexible manufacturing systemCooperative scheduling of machines and AGVsMarkov decision processHeuristic-assisted DQN algorithm |
| spellingShingle | Xiaoting Dong Guangxi Wan Peng Zeng A heuristic-assisted deep reinforcement learning algorithm for flexible job shop scheduling with transport constraints Complex & Intelligent Systems Flexible manufacturing system Cooperative scheduling of machines and AGVs Markov decision process Heuristic-assisted DQN algorithm |
| title | A heuristic-assisted deep reinforcement learning algorithm for flexible job shop scheduling with transport constraints |
| title_full | A heuristic-assisted deep reinforcement learning algorithm for flexible job shop scheduling with transport constraints |
| title_fullStr | A heuristic-assisted deep reinforcement learning algorithm for flexible job shop scheduling with transport constraints |
| title_full_unstemmed | A heuristic-assisted deep reinforcement learning algorithm for flexible job shop scheduling with transport constraints |
| title_short | A heuristic-assisted deep reinforcement learning algorithm for flexible job shop scheduling with transport constraints |
| title_sort | heuristic assisted deep reinforcement learning algorithm for flexible job shop scheduling with transport constraints |
| topic | Flexible manufacturing system Cooperative scheduling of machines and AGVs Markov decision process Heuristic-assisted DQN algorithm |
| url | https://doi.org/10.1007/s40747-025-01828-6 |
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