A Decentralized Optimization Algorithm for Multi-Agent Job Shop Scheduling with Private Information

Xinmin Zhou, Wenhao Rao, Yaqiong Liu and Shudong Sun ()
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Xinmin Zhou: School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China
Wenhao Rao: School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China
Yaqiong Liu: School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China
Shudong Sun: School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China

Mathematics, 2024, vol. 12, issue 7, 1-22

Abstract: The optimization of job shop scheduling is pivotal for improving overall production efficiency within a workshop. In demand-driven personalized production modes, achieving a balance between workshop resources and the diverse demands of customers presents a challenge in scheduling. Additionally, considering the self-interested behaviors of agents, this study focuses on tackling the problem of multi-agent job shop scheduling with private information. Multiple consumer agents and one job shop agent are considered, all of which are self-interested and have private information. To address this problem, a two-stage decentralized algorithm rooted in the genetic algorithm is developed to achieve a consensus schedule. The algorithm allows agents to evolve independently and concurrently, aiming to satisfy individual requirements. To prevent becoming trapped in a local optimum, the search space is broadened through crossover between agents and agent-based block insertion. Non-dominated sorting and grey relational analysis are applied to generate the final solution with high social welfare. The proposed algorithm is compared using a centralized approach and two state-of-the-art decentralized approaches in computational experiments involving 734 problem instances. The results validate that the proposed algorithm generates non-dominated solutions with strong convergence and uniformity. Moreover, the final solution produced by the developed algorithm outperforms those of the decentralized approaches. These advantages are more pronounced in larger-scale problem instances with more agents.

Keywords: multi-agent scheduling; decentralized decision making; genetic algorithm; negotiation optimization; social welfare (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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