Academic Journal
Profit-Oriented Multi-Objective Dynamic Flexible Job Shop Scheduling with Multi-Agent Framework Under Uncertain Production Orders.
| Title: | Profit-Oriented Multi-Objective Dynamic Flexible Job Shop Scheduling with Multi-Agent Framework Under Uncertain Production Orders. |
|---|---|
| Authors: | Ma, Qingyao, Lu, Yao, Chen, Huawei |
| Source: | Machines; Oct2025, Vol. 13 Issue 10, p932, 31p |
| Subject Terms: | Profit maximization, Scheduling, Production management (Manufacturing), Multiagent systems, Multi-objective optimization, Mathematical optimization, Reinforcement learning, Production scheduling |
| Abstract: | In the highly competitive manufacturing environment, customers are increasingly demanding punctual, flexible, and customized deliveries, compelling enterprises to balance profit, energy efficiency, and production performance while seeking new scheduling methods to enhance dynamic responsiveness. Although deep reinforcement learning (DRL) has made progress in dynamic flexible job shop scheduling, existing research has rarely addressed profit-oriented optimization. To tackle this challenge, this paper proposes a novel multi-objective dynamic flexible job shop scheduling (MODFJSP) model that aims to maximize net profit and minimize makespan on the basis of traditional FJSP. The model incorporates uncertainties such as new job insertions, fluctuating due dates, and high-profit urgent jobs, and establishes a multi-agent collaborative framework consisting of "job selection–machine assignment." For the two types of agents, this paper proposes adaptive state representations, reward functions, and variable action spaces to achieve the dual optimization objectives. The experimental results show that the double deep Q-network (DDQN), within the multi-agent cooperative framework, outperforms PPO, DQN, and classical scheduling rules in terms of solution quality and robustness. It achieves superior performance on multiple metrics such as IGD, HV, and SC, and generates bi-objective Pareto frontiers that are closer to the ideal point. The results demonstrate the effectiveness and practical value of the proposed collaborative framework for solving MODFJSP. [ABSTRACT FROM AUTHOR] |
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| Database: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Profit-Oriented Multi-Objective Dynamic Flexible Job Shop Scheduling with Multi-Agent Framework Under Uncertain Production Orders. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ma%2C+Qingyao%22">Ma, Qingyao</searchLink><br /><searchLink fieldCode="AR" term="%22Lu%2C+Yao%22">Lu, Yao</searchLink><br /><searchLink fieldCode="AR" term="%22Chen%2C+Huawei%22">Chen, Huawei</searchLink> – Name: TitleSource Label: Source Group: Src Data: Machines; Oct2025, Vol. 13 Issue 10, p932, 31p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Profit+maximization%22">Profit maximization</searchLink><br /><searchLink fieldCode="DE" term="%22Scheduling%22">Scheduling</searchLink><br /><searchLink fieldCode="DE" term="%22Production+management+%28Manufacturing%29%22">Production management (Manufacturing)</searchLink><br /><searchLink fieldCode="DE" term="%22Multiagent+systems%22">Multiagent systems</searchLink><br /><searchLink fieldCode="DE" term="%22Multi-objective+optimization%22">Multi-objective optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Production+scheduling%22">Production scheduling</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In the highly competitive manufacturing environment, customers are increasingly demanding punctual, flexible, and customized deliveries, compelling enterprises to balance profit, energy efficiency, and production performance while seeking new scheduling methods to enhance dynamic responsiveness. Although deep reinforcement learning (DRL) has made progress in dynamic flexible job shop scheduling, existing research has rarely addressed profit-oriented optimization. To tackle this challenge, this paper proposes a novel multi-objective dynamic flexible job shop scheduling (MODFJSP) model that aims to maximize net profit and minimize makespan on the basis of traditional FJSP. The model incorporates uncertainties such as new job insertions, fluctuating due dates, and high-profit urgent jobs, and establishes a multi-agent collaborative framework consisting of "job selection–machine assignment." For the two types of agents, this paper proposes adaptive state representations, reward functions, and variable action spaces to achieve the dual optimization objectives. The experimental results show that the double deep Q-network (DDQN), within the multi-agent cooperative framework, outperforms PPO, DQN, and classical scheduling rules in terms of solution quality and robustness. It achieves superior performance on multiple metrics such as IGD, HV, and SC, and generates bi-objective Pareto frontiers that are closer to the ideal point. The results demonstrate the effectiveness and practical value of the proposed collaborative framework for solving MODFJSP. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Machines is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/machines13100932 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 31 StartPage: 932 Subjects: – SubjectFull: Profit maximization Type: general – SubjectFull: Scheduling Type: general – SubjectFull: Production management (Manufacturing) Type: general – SubjectFull: Multiagent systems Type: general – SubjectFull: Multi-objective optimization Type: general – SubjectFull: Mathematical optimization Type: general – SubjectFull: Reinforcement learning Type: general – SubjectFull: Production scheduling Type: general Titles: – TitleFull: Profit-Oriented Multi-Objective Dynamic Flexible Job Shop Scheduling with Multi-Agent Framework Under Uncertain Production Orders. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ma, Qingyao – PersonEntity: Name: NameFull: Lu, Yao – PersonEntity: Name: NameFull: Chen, Huawei IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 20751702 Numbering: – Type: volume Value: 13 – Type: issue Value: 10 Titles: – TitleFull: Machines Type: main |
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