Tote delivery optimisation for multi-tote storage and retrieval autonomous mobile robot system with multiple workstations.
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| Title: | Tote delivery optimisation for multi-tote storage and retrieval autonomous mobile robot system with multiple workstations. |
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| Authors: | Yu, Runfang1,2 (AUTHOR), Liu, Xiaotao1,2 (AUTHOR) liuxiaotao@hnie.edu.cn |
| Source: | International Journal of Production Research. Jul2026, Vol. 64 Issue 14, p5913-5940. 28p. |
| Subjects: | Heuristic algorithms, Genetic algorithms, Mixed integer linear programming, Bins, Autonomous robots, Automated materials handling |
| Abstract: | This study addresses the multi-workstation tote delivery optimisation problem (MTDOP) in multi-tote storage and retrieval autonomous mobile robot (MTSR AMR) systems, aiming to minimise the order fulfillment makespan under fixed order assignments. A mixed-integer programming model is formulated to simultaneously optimise three key decisions: tote set composition, delivery sequencing, and robot scheduling. We propose scenario-specific algorithms for the MTDOP. For high-order-volume scenarios with pending orders, a two-stage heuristic is developed: a total-distance-based adaptive large neighbourhood search (TD-ALNS) for tote set composition and delivery sequencing, followed by a genetic algorithm for robot scheduling. For low-order-volume scenarios with fully released orders, a hybrid genetic algorithm (HGA) with embedded iterated local search performs joint optimisation. Numerical experiments demonstrate that the proposed TD-ALNS/HGA approach not only significantly outperforms Gurobi and the only existing popularity-driven benchmark, achieving an average makespan reduction of 20.7% in large-scale instances, but also clearly surpasses three other metaheuristic algorithms, including local search, variable neighbourhood search, and simulated annealing. Managerial insights based on sensitivity analysis include clustering co-ordered SKUs, adopting moderate warehouse layouts, controlling batch sizes, and prioritise expanding robots buffer capacities and put-wall capacities over increasing their quantities. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd 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. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 195127051 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Tote delivery optimisation for multi-tote storage and retrieval autonomous mobile robot system with multiple workstations. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yu%2C+Runfang%22">Yu, Runfang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Xiaotao%22">Liu, Xiaotao</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> liuxiaotao@hnie.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. Jul2026, Vol. 64 Issue 14, p5913-5940. 28p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Heuristic+algorithms%22">Heuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Mixed+integer+linear+programming%22">Mixed integer linear programming</searchLink><br /><searchLink fieldCode="DE" term="%22Bins%22">Bins</searchLink><br /><searchLink fieldCode="DE" term="%22Autonomous+robots%22">Autonomous robots</searchLink><br /><searchLink fieldCode="DE" term="%22Automated+materials+handling%22">Automated materials handling</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This study addresses the multi-workstation tote delivery optimisation problem (MTDOP) in multi-tote storage and retrieval autonomous mobile robot (MTSR AMR) systems, aiming to minimise the order fulfillment makespan under fixed order assignments. A mixed-integer programming model is formulated to simultaneously optimise three key decisions: tote set composition, delivery sequencing, and robot scheduling. We propose scenario-specific algorithms for the MTDOP. For high-order-volume scenarios with pending orders, a two-stage heuristic is developed: a total-distance-based adaptive large neighbourhood search (TD-ALNS) for tote set composition and delivery sequencing, followed by a genetic algorithm for robot scheduling. For low-order-volume scenarios with fully released orders, a hybrid genetic algorithm (HGA) with embedded iterated local search performs joint optimisation. Numerical experiments demonstrate that the proposed TD-ALNS/HGA approach not only significantly outperforms Gurobi and the only existing popularity-driven benchmark, achieving an average makespan reduction of 20.7% in large-scale instances, but also clearly surpasses three other metaheuristic algorithms, including local search, variable neighbourhood search, and simulated annealing. Managerial insights based on sensitivity analysis include clustering co-ordered SKUs, adopting moderate warehouse layouts, controlling batch sizes, and prioritise expanding robots buffer capacities and put-wall capacities over increasing their quantities. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=195127051 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/00207543.2026.2616669 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 28 StartPage: 5913 Subjects: – SubjectFull: Heuristic algorithms Type: general – SubjectFull: Genetic algorithms Type: general – SubjectFull: Mixed integer linear programming Type: general – SubjectFull: Bins Type: general – SubjectFull: Autonomous robots Type: general – SubjectFull: Automated materials handling Type: general Titles: – TitleFull: Tote delivery optimisation for multi-tote storage and retrieval autonomous mobile robot system with multiple workstations. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yu, Runfang – PersonEntity: Name: NameFull: Liu, Xiaotao IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 00207543 Numbering: – Type: volume Value: 64 – Type: issue Value: 14 Titles: – TitleFull: International Journal of Production Research Type: main |
| ResultId | 1 |
