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A Greedy Tabu Dual Heuristic algorithm for the cyclic pickup and delivery problem with 3D loading constraints.

Wei Xu1, Bingjie Li1, Wenjie Cui1

  • 1Shandong University of Science and Technology, College of Transportation, Qingdao, 266590, China.

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This study optimizes auto parts logistics by integrating vehicle routing with 3D loading. The new Greedy-Tabu Dual-Heuristic algorithm significantly cuts travel distance and waiting times, improving efficiency.

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Cyclic pickup and deliveryHybrid multi-modelPath optimizationThree-dimensional loading

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Area of Science:

  • Operations Research
  • Supply Chain Management
  • Logistics Optimization

Background:

  • Automotive industry growth hinges on efficient auto parts supply chain logistics.
  • Reducing logistics costs and enhancing transportation efficiency are critical challenges.
  • Existing methods often fail to integrate multi-vehicle routing with complex 3D loading constraints.

Purpose of the Study:

  • To address the joint optimization of multi-vehicle pickup and delivery transportation paths under time window constraints.
  • To incorporate three-dimensional (3D) loading of goods into the logistics optimization model.
  • To develop a comprehensive optimization model for inbound auto parts logistics.

Main Methods:

  • Development of a comprehensive optimization model for inbound auto parts logistics.
  • Proposal of a Greedy-Tabu Dual-Heuristic algorithm, integrating Improved Greedy and Enhanced Tabu Search with ɛ-sampling.
  • Evaluation of the algorithm's performance through various case studies and comparisons with traditional methods.

Main Results:

  • The proposed method achieved a 26.67% reduction in total distance traveled and a 46.60% decrease in waiting time compared to the traditional Milk-Run approach.
  • Average vehicle load utilization improved by 17% and vehicle volume utilization by 15% against standard 3D loading methods.
  • The Greedy-Tabu Dual-Heuristic algorithm demonstrated superiority, efficiency, and stability across different problem sizes.

Conclusions:

  • The developed optimization model and Greedy-Tabu Dual-Heuristic algorithm are effective for complex auto parts logistics.
  • The integrated approach significantly enhances transportation efficiency and resource utilization in the automotive supply chain.
  • The findings confirm the practical applicability and superiority of the proposed methods for real-world logistics scenarios.