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A Data-Driven Three-Layer Algorithm for Split Delivery Vehicle Routing Problem with 3D Container Loading Constraint

Published: 19 July 2018 Publication History

Abstract

Split Delivery Vehicle Routing Problem with 3D Loading Constraints (3L-SDVRP) can be seen as the most important problem in large-scale manufacturing logistics. The goal is to devise a strategy consisting of three NP-hard planning components: vehicle routing, cargo splitting and container loading, which shall be jointly optimized for cost savings. The problem is an enhanced variant of the classical logistics problem 3L-CVRP, and its complexity leaps beyond current studies of solvability. Our solution employs a novel data-driven three-layer search algorithm (DTSA), which we designed to improve both the efficiency and effectiveness of traditional meta-heuristic approaches, through learning from data and from simulation.
A detailed experimental evaluation on real data shows our algorithm is versatile in solving this practical complex constrained multi-objective optimization problem, and our framework may be of general interest. DTSA performs much better than the state-of-the-art algorithms both in efficiency and optimization performance. Our algorithm has been deployed in the UAT (User Acceptance Test) environment; conservative estimates suggest that the full usage of our algorithm would save millions of dollars in logistics costs per year, besides savings due to automation and more efficient routing.

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Cited By

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  • (2024)PEACH: A Multi-Objective Evolutionary Algorithm for Complex Vehicle Routing with Three-Dimensional Loading ConstraintsProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3654333(231-234)Online publication date: 14-Jul-2024
  • (2024)An Adaptive Interactive Routing-Packing Strategy for Split Delivery Vehicle Routing Problem with 3D Loading ConstraintsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3653991(249-257)Online publication date: 14-Jul-2024
  • (2024)Learned Unmanned Vehicle Scheduling for Large-Scale Urban LogisticsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.335168725:7(7933-7944)Online publication date: Jul-2024
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        cover image ACM Other conferences
        KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
        July 2018
        2925 pages
        ISBN:9781450355520
        DOI:10.1145/3219819
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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        Published: 19 July 2018

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        Author Tags

        1. container loading
        2. logistics
        3. transportation planning

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        • Program for NSFC
        • National Key Research &Development Program of China
        • Shanghai Rising-Star Program

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        KDD '18 Paper Acceptance Rate 107 of 983 submissions, 11%;
        Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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        View all
        • (2024)PEACH: A Multi-Objective Evolutionary Algorithm for Complex Vehicle Routing with Three-Dimensional Loading ConstraintsProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3654333(231-234)Online publication date: 14-Jul-2024
        • (2024)An Adaptive Interactive Routing-Packing Strategy for Split Delivery Vehicle Routing Problem with 3D Loading ConstraintsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3653991(249-257)Online publication date: 14-Jul-2024
        • (2024)Learned Unmanned Vehicle Scheduling for Large-Scale Urban LogisticsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.335168725:7(7933-7944)Online publication date: Jul-2024
        • (2024)A variable neighborhood search for the green vehicle routing problem with two-dimensional loading constraints and split deliveryEuropean Journal of Operational Research10.1016/j.ejor.2024.01.049316:2(597-616)Online publication date: Jul-2024
        • (2024)Solving biobjective traveling thief problems with multiobjective reinforcement learningApplied Soft Computing10.1016/j.asoc.2024.111751161:COnline publication date: 1-Aug-2024
        • (2024)Large-Scale Global OptimizationIntelligent Optimization10.1007/978-981-97-3286-9_13(253-263)Online publication date: 25-May-2024
        • (2024)Knowledge-Guided Optimization for Complex Vehicle Routing with 3D Loading ConstraintsParallel Problem Solving from Nature – PPSN XVIII10.1007/978-3-031-70055-2_9(133-148)Online publication date: 7-Sep-2024
        • (2023)Mô hình tối ưu hóa tuyến đường vận chuyển trong chuỗi cung ứng lạnh nông sảnCan Tho University Journal of Science10.22144/ctu.jvn.2023.02359:ETMD(1-8)Online publication date: 19-May-2023
        • (2023)VeLP: Vehicle Loading Plan Learning from Human Behavior in Nationwide Logistics SystemProceedings of the VLDB Endowment10.14778/3626292.362630517:2(241-249)Online publication date: Oct-2023
        • (2022)Multiobjective Optimization-Aided Decision-Making System for Large-Scale Manufacturing PlanningIEEE Transactions on Cybernetics10.1109/TCYB.2021.304971252:8(8326-8339)Online publication date: Aug-2022
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