Discrete optimization algorithms for distributed bi-agent flowshop scheduling with release dates

•Consideration of release date to simulate real-world production.•Domination of branch-and-bound algorithm to popular CPLEX optimizer.•Introduction of an EAF-DA heuristic algorithm for rapid solution generation.•Effectiveness of bi-objective metaheuristic with improving strategies. The globalization...

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Veröffentlicht in:	Swarm and evolutionary computation Jg. 98; S. 102101
Hauptverfasser:	Bai, Danyu, Zheng, Wenjia, Zang, Chenbo, Yang, Jie, Wu, Chin-Chia, Qin, Hu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.10.2025
Schlagworte:	Bi-agent scheduling Bi-objective problem Branch and bound Distributed flowshop scheduling Q-learning-based artificial bee colony Distributed flowshop scheduling Q-learning-based artificial bee colony Branch and bound Bi-objective problem Bi-agent scheduling
ISSN:	2210-6502
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Zusammenfassung:	•Consideration of release date to simulate real-world production.•Domination of branch-and-bound algorithm to popular CPLEX optimizer.•Introduction of an EAF-DA heuristic algorithm for rapid solution generation.•Effectiveness of bi-objective metaheuristic with improving strategies. The globalization of production has accelerated the growth of contract manufacturing, as brand firms increasingly outsource production to specialized manufacturers to reduce costs and improve efficiency. To meet rising production demands, contract manufacturers establish production facilities across global regions, leveraging localized advantages in labor costs, raw material access, and logistics infrastructure. Contract manufacturers in distributed assembly-line systems face the critical challenge of dynamically coordinating order allocation across decentralized facilities to satisfy multi-client requirements. This study introduces a distributed bi-agent permutation flowshop scheduling for minimizing the makespans of both agents while considering release dates to simulate real-world production scenarios. An exact branch-and-bound algorithm is proposed for optimizing the weighted sum of two objectives. A novel Q-learning-based artificial bee colony algorithm is presented to construct high-quality Pareto frontiers for the bi-objective optimization problem. The effectiveness of the proposed algorithms is validated through a comprehensive set of numerical experiments.
ISSN:	2210-6502
DOI:	10.1016/j.swevo.2025.102101