Solving the flow-shop scheduling problem with human factors and two competing agents with deep reinforcement learning

Abstract

With the development of globalized production, customer satisfaction has become a critical concern for companies. Multi-agent models have gained attention, aiming to meet customer demands while reducing costs. The bi-agent flow-shop scheduling problem (BAFSP) in the manufacturing industry is the main focus of this article, where agents represent different customers. The objective of BAFSP is to minimize the total weighted makespan for agents. The BAFSP introduces various position-dependent learning effects. Furthermore, each task has an independent release date, aligned with real production scenarios. A deep reinforcement learning (DRL) approach based on the transformer architecture is proposed to address the BAFSP with learning effects (BAFSP-LE). Experimental results demonstrate that the proposed method outperforms other commonly used heuristics. To explore the feasibility of combining DRL with metaheuristics, the proposed DRL method is used as an initial solution generator. The method improves the performance of these metaheuristics within the same computation time.

KEYWORDS:

• Flow-shop scheduling
• multi-agent scheduling
• learning effect
• deep reinforcement learning
• release date

Acknowledgements

The authors thank the Institute of Network Science and Big Data of Northeastern University and Dalian Maritime University for their help.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Additional information

Funding

This work is partially supported by the National Natural Science Foundation of China [grant numbers 62276058, 61902057 and 41774063], Fundamental Research Funds for the Central Universities [grant number N2217003] and Joint Fund of Science & Technology Department of Liaoning Province and State Key Laboratory of Rob.