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An Improved Chimp-Inspired Optimization Algorithm for Large-Scale Spherical Vehicle Routing Problem with Time

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Summary
This summary is machine-generated.

This study introduces a new model for the vehicle routing problem with time windows on a spherical surface. An improved chimp optimization algorithm effectively solves this complex routing challenge for unmanned vehicles.

Keywords:
chimp optimization algorithmimproved chimp optimization algorithmmetaheuristic algorithmspherical VRPTW model

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Area of Science:

  • Operations Research
  • Artificial Intelligence
  • Logistics and Supply Chain Management

Background:

  • The vehicle routing problem with time windows (VRPTW) is typically studied on a 2D plane.
  • Research on spherical surfaces is limited, hindering applications for aerial and ground-based delivery systems.
  • Existing chimp optimization algorithms struggle with discrete problems and convergence speed.

Purpose of the Study:

  • To propose a novel three-dimensional spherical VRPTW model for realistic delivery scenarios.
  • To enhance the chimp optimization algorithm for improved performance on discrete optimization problems.
  • To evaluate the effectiveness of the improved algorithm in solving the spherical VRPTW.

Main Methods:

  • Mapping customer nodes to a three-dimensional sphere to create the spherical VRPTW model.
  • Integrating multiple-population strategy, genetic operators, and local search into the chimp optimization algorithm (ChOA).
  • Developing an improved chimp optimization algorithm (MG-ChOA) tailored for discrete optimization.

Main Results:

  • The proposed MG-ChOA demonstrates superior exploration ability and convergence speed compared to the original ChOA.
  • The algorithm effectively solves the discrete spherical VRPTW model.
  • Experimental results show the MG-ChOA outperforms existing algorithms in solving the spherical VRPTW.

Conclusions:

  • The developed three-dimensional spherical VRPTW model is suitable for unmanned vehicle logistics.
  • The enhanced MG-ChOA is a highly effective and superior algorithm for discrete optimization problems like the spherical VRPTW.
  • This research advances the application of optimization algorithms in complex, real-world delivery scenarios.