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Rich vehicle routing optimization based on variable neighborhood descent and differential evolution algorithm.

Haifei Zhang1, Yuzhou Zhang2, Fen Zhao2

  • 1College of information engineering of Nanjing, XiaoZhuang University, Nanjing, 211171, Jiangsu, China. 18215199587@163.com.

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|September 25, 2025
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Summary
This summary is machine-generated.

This study optimizes the Rich Vehicle Routing Problem (RVRP) using a hybrid Differential Evolution and Variable Neighborhood Descent algorithm. The enhanced method finds diverse, optimal logistics paths, outperforming existing solutions.

Keywords:
Differential evolution algorithmLogistics distributionMulti constraint problemMulti-modal multi-objective optimizationVehicle routing problem

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Area of Science:

  • Operations Research
  • Computer Science
  • Logistics Management

Background:

  • The standard vehicle routing problem (VRP) lacks realism for complex logistics.
  • Existing VRP solutions often fail to provide diverse optimal paths for multi-objective decision-making.

Purpose of the Study:

  • To model the Rich Vehicle Routing Problem (RVRP) as a multi-modal, multi-objective optimization problem.
  • To develop an advanced algorithm for solving RVRP with practical constraints.

Main Methods:

  • Formulated RVRP with complex road network, load, time window, and demand splitting constraints.
  • Developed a hybrid algorithm combining Differential Evolution (DE) with Oppositional Learning (OL) and Variable Neighborhood Descent (VND).
  • Integrated OL to expand DE's search range and VND to prevent local optima.

Main Results:

  • The proposed method effectively solves RVRP, identifying multiple equivalent optimal paths for logistics distribution.
  • Experimental results on benchmark instances demonstrate superior performance compared to state-of-the-art RVRP methods.
  • The algorithm achieved the best comprehensive performance in finding diverse optimal solutions.

Conclusions:

  • The optimized mathematical model and hybrid algorithm enhance the diversity and quality of optimal solutions for RVRP.
  • This approach offers a robust and efficient solution for practical logistics distribution challenges.
  • The method shows significant potential for real-world engineering applications in vehicle routing.