1. A Decentralized Optimization Algorithm for Multi-Agent Job Shop Scheduling with Private Information.
- Author
-
Zhou, Xinmin, Rao, Wenhao, Liu, Yaqiong, and Sun, Shudong
- Subjects
- *
PRODUCTION scheduling , *OPTIMIZATION algorithms , *GREY relational analysis , *FLOW shops , *SOCIAL services , *GENETIC algorithms , *JOB shops , *TABU search algorithm - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF