Smart Automated Guided Vehicle scheduling with flexible battery management: A new formulation and an exact approach
Automated Guided Vehicles (AGVs) have gained widespread application within modern smart transportation or industrial systems. The AGV scheduling problem, particularly considering battery management, holds a pivotal role in enhancing system efficiency, cost-effectiveness, and safety. Existing researc...
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| Published in: | Computers & operations research Vol. 183; p. 107156 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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01.11.2025
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| ISSN: | 0305-0548 |
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| Abstract | Automated Guided Vehicles (AGVs) have gained widespread application within modern smart transportation or industrial systems. The AGV scheduling problem, particularly considering battery management, holds a pivotal role in enhancing system efficiency, cost-effectiveness, and safety. Existing research on the AGV scheduling problem predominantly assumes fixed charging or battery swapping strategies, wherein the duration of each energy replenishment operation remains constant and predetermined. However, allowing AGVs to undergo partial charging durations offers increased flexibility and potential efficiency gains by minimizing downtime. The incorporation of flexible charging introduces additional complexity to the AGV scheduling problem, as it necessitates determining the duration for each charging operation. In this study, we investigate an AGV scheduling problem with flexible charging and charging setup time (ASP-FLC-ST). Initially, we propose a novel mixed-integer linear programming model tailored to address the ASP-FLC-ST. Subsequently, we conduct a structural analysis of the problem, demonstrating its strong NP-hardness and deriving a valid lower bound. To tackle the complexity of the ASP-FLC-ST, we develop a customized exact logic-based Benders decomposition algorithm (LBBD) and introduce an “alternating cut” generation scheme to enhance its performance. Computational experiments conducted on 360 random instances of the ASP-FLC-ST showcase the superiority of our approach over state-of-the-art commercial solvers. Moreover, the devised LBBD method effectively addresses benchmark instances of a reduced counterpart, yielding 173 new best solutions and establishing optimality in 161 instances with open solutions.
•Address a novel AGV scheduling problem with flexible charging strategy.•Formulate a MILP and develop a lower bound to enhance the model.•Design an exact logic-based Benders decomposition method with “alternating cuts”.•Extensive numerical experiments verify the superior performance of LBBD. |
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| AbstractList | Automated Guided Vehicles (AGVs) have gained widespread application within modern smart transportation or industrial systems. The AGV scheduling problem, particularly considering battery management, holds a pivotal role in enhancing system efficiency, cost-effectiveness, and safety. Existing research on the AGV scheduling problem predominantly assumes fixed charging or battery swapping strategies, wherein the duration of each energy replenishment operation remains constant and predetermined. However, allowing AGVs to undergo partial charging durations offers increased flexibility and potential efficiency gains by minimizing downtime. The incorporation of flexible charging introduces additional complexity to the AGV scheduling problem, as it necessitates determining the duration for each charging operation. In this study, we investigate an AGV scheduling problem with flexible charging and charging setup time (ASP-FLC-ST). Initially, we propose a novel mixed-integer linear programming model tailored to address the ASP-FLC-ST. Subsequently, we conduct a structural analysis of the problem, demonstrating its strong NP-hardness and deriving a valid lower bound. To tackle the complexity of the ASP-FLC-ST, we develop a customized exact logic-based Benders decomposition algorithm (LBBD) and introduce an “alternating cut” generation scheme to enhance its performance. Computational experiments conducted on 360 random instances of the ASP-FLC-ST showcase the superiority of our approach over state-of-the-art commercial solvers. Moreover, the devised LBBD method effectively addresses benchmark instances of a reduced counterpart, yielding 173 new best solutions and establishing optimality in 161 instances with open solutions.
•Address a novel AGV scheduling problem with flexible charging strategy.•Formulate a MILP and develop a lower bound to enhance the model.•Design an exact logic-based Benders decomposition method with “alternating cuts”.•Extensive numerical experiments verify the superior performance of LBBD. |
| ArticleNumber | 107156 |
| Author | Chung, Sai-Ho Li, Yantong Zhou, Shanshan Wen, Xin |
| Author_xml | – sequence: 1 givenname: Yantong orcidid: 0000-0002-9703-3882 surname: Li fullname: Li, Yantong organization: School of Maritime Economics and Management, Dalian Maritime University, 116026 Dalian, China – sequence: 2 givenname: Xin orcidid: 0000-0003-0279-6869 surname: Wen fullname: Wen, Xin email: windy.wen@polyu.edu.hk organization: Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong – sequence: 3 givenname: Shanshan orcidid: 0009-0009-4815-3544 surname: Zhou fullname: Zhou, Shanshan organization: School of Maritime Economics and Management, Dalian Maritime University, 116026 Dalian, China – sequence: 4 givenname: Sai-Ho surname: Chung fullname: Chung, Sai-Ho organization: Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong |
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| Keywords | Flexible charging Logic-based Benders decomposition AGV scheduling Alternating benders cut MILP |
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