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Related Experiment Video

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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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Genetic Programming Hyper-Heuristics with Vehicle Collaboration for Uncertain Capacitated Arc Routing Problems.

Jordan MacLachlan1, Yi Mei2, Juergen Branke3

  • 1School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington 6140, New Zealand maclacjord@ecs.vuw.ac.nz.

Evolutionary Computation
|November 16, 2019
PubMed
Summary

This study introduces a new method for the Uncertain Capacitated Arc Routing Problem (UCARP) using vehicle collaboration. The approach significantly improves performance in uncertain environments, especially for large-scale routing tasks.

Keywords:
Arc routinggenetic programminghyper-heuristicstochastic optimisation.

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Area of Science:

  • Operations Research
  • Optimization
  • Logistics

Background:

  • The Uncertain Capacitated Arc Routing Problem (UCARP) is crucial for real-world applications like disaster response and waste management.
  • Existing research on routing under uncertainty has largely overlooked UCARP and collaborative strategies.
  • Stochastic models offer a more realistic representation of operational challenges compared to deterministic models.

Purpose of the Study:

  • To address the limitations in current UCARP research by introducing a novel collaborative, multi-vehicle framework.
  • To develop an effective algorithm for solving UCARP that accounts for real-world uncertainties and vehicle interactions.
  • To enhance the efficiency and robustness of routing operations in uncertain environments.

Main Methods:

  • A new Solution Construction Procedure (SCP) was developed to generate UCARP solutions within a collaborative, multi-vehicle system.
  • The framework incorporates collaborative activities for managing unexpected capacity depletion (route failure) and during vehicle refill processes.
  • A Genetic Programming Hyper-Heuristic (GPHH) algorithm was employed to evolve the optimal routing policy for the collaborative framework.

Main Results:

  • The proposed heuristic, incorporating vehicle collaboration and a GP-evolved routing policy, significantly outperformed existing state-of-the-art algorithms.
  • Performance improvements were particularly pronounced on test instances with a higher number of tasks and vehicles.
  • The study demonstrated the substantial benefits of inter-vehicle collaboration in mitigating the negative impacts of uncertainty in routing operations.

Conclusions:

  • Vehicle collaboration is a highly effective strategy for managing uncertainty in Capacitated Arc Routing Problems.
  • The developed GPHH algorithm combined with the collaborative SCP offers a powerful and efficient solution for UCARP.
  • This research advances the field of uncertain routing problems by providing a robust framework for practical, large-scale applications.