An effective deep actor-critic reinforcement learning method for solving the flexible job shop scheduling problem

Wan, Lanjun; Cui, Xueyan; Zhao, Haoxin; Li, Changyun; Wang, Zhibing

doi:10.1007/s00521-024-09654-6

Neural Comput & Applic

2024

DOI: 10.1007/s00521-024-09654-6

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An effective deep actor-critic reinforcement learning method for solving the flexible job shop scheduling problem

Lanjun Wan,

Xueyan Cui,

Haoxin Zhao

et al.

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Cited by 3 publications

References 32 publications

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A dynamic flexible job shop scheduling method based on collaborative agent reinforcement learning

Shao,

Yu,

Ding

et al. 2024

Flex Serv Manuf J

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A dynamic flexible job shop scheduling method based on collaborative agent reinforcement learning

Shao,

Yu,

Ding

et al. 2024

Flex Serv Manuf J

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An Optimization Method for Green Permutation Flow Shop Scheduling Based on Deep Reinforcement Learning and MOEA/D

Lu,

Yuan,

Sitahong

et al. 2024

Machines

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This paper addresses the green permutation flow shop scheduling problem (GPFSP) with energy consumption consideration, aiming to minimize the maximum completion time and total energy consumption as optimization objectives, and proposes a new method that integrates end-to-end deep reinforcement learning (DRL) with the multi-objective evolutionary algorithm based on decomposition (MOEA/D), termed GDRL-MOEA/D. To improve the quality of solutions, the study first employs DRL to model the PFSP as a sequence-to-sequence model (DRL-PFSP) to obtain relatively better solutions. Subsequently, the solutions generated by the DRL-PFSP model are used as the initial population for the MOEA/D, and the proposed job postponement energy-saving strategy is incorporated to enhance the solution effectiveness of the MOEA/D. Finally, by comparing the GDRL-MOEA/D with the MOEA/D, NSGA-II, the marine predators algorithm (MPA), the sparrow search algorithm (SSA), the artificial hummingbird algorithm (AHA), and the seagull optimization algorithm (SOA) through experimental tests, the results demonstrate that the GDRL-MOEA/D has a significant advantage in terms of solution quality.

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Real-Time Scheduling with Independent Evaluators: Explainable Multi-Agent Approach

Isakov,

Peregorodiev,

Tomilov

et al. 2024

Technologies

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This study introduces a multi-agent reinforcement learning approach to address the challenges of real-time scheduling in dynamic environments, with a specific focus on healthcare operations. The proposed system integrates the Human-in-the-Loop (HITL) paradigm, providing continuous feedback from human evaluators, and it employs a sophisticated reward function to attenuate the effects of human-driven events. Novel mapping between reinforcement learning (RL) concepts and the Belief–Desire–Intention (BDI) framework is developed to enhance the explainability of the agent’s decision-making. A system is designed to adapt to changes in patient conditions and preferences while minimizing disruptions to existing schedules. Experimental results show a notable decrease in patient waiting times compared to conventional methods while adhering to operator-induced constraints. This approach offers a robust, explainable, and adaptable solution for the challenging tasks of scheduling in the environments that require human-centered decision-making.

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An effective deep actor-critic reinforcement learning method for solving the flexible job shop scheduling problem

Cited by 3 publications

References 32 publications

A dynamic flexible job shop scheduling method based on collaborative agent reinforcement learning

A dynamic flexible job shop scheduling method based on collaborative agent reinforcement learning

An Optimization Method for Green Permutation Flow Shop Scheduling Based on Deep Reinforcement Learning and MOEA/D

Real-Time Scheduling with Independent Evaluators: Explainable Multi-Agent Approach

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