Vehicle routing problem with reverse cross-docking: An adaptive large neighborhood search algorithm
Cross-docking is a logistics strategy that aims at less transportation costs and fast customer deliveries. Incorporating an efficient vehicle routing could increase the benefits of the cross-docking. In this paper, the vehicle routing problem with reverse cross-docking (VRP-RCD) is studied. Reverse...
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| Main Authors: | , , , |
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| 格式: | text |
| 語言: | English |
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Institutional Knowledge at Singapore Management University
2020
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| 在線閱讀: | https://ink.library.smu.edu.sg/sis_research/6106 https://ink.library.smu.edu.sg/context/sis_research/article/7109/viewcontent/Gunawan2020_Chapter_VehicleRoutingProblemWithRever.pdf |
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| 機構: | Singapore Management University |
| 語言: | English |
| 總結: | Cross-docking is a logistics strategy that aims at less transportation costs and fast customer deliveries. Incorporating an efficient vehicle routing could increase the benefits of the cross-docking. In this paper, the vehicle routing problem with reverse cross-docking (VRP-RCD) is studied. Reverse logistics has attracted more attention due to its ability to gain more profit and maintain the competitiveness of a company. VRP-RCD includes a four-level supply chain network: suppliers, cross-dock, customers, and outlets, with the objective of minimizing vehicle operational and transportation costs. A two-phase heuristic that employs an adaptive large neighborhood search (ALNS) with various destroy and repair operators is proposed to solve benchmark instances. The simulated annealing framework is embedded to discover a vast search space during the search process. Experimental results show that our proposed ALNS obtains optimal solutions for 24 out of 30 problems of the first set of benchmark instances while getting better results for all instances in the second set of benchmark instances compared to optimization software. |
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