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Critical-edge based tabu search algorithm for solving large-scale multi-vehicle Chinese postman problem.

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This study introduces CTA-kroutes, a novel algorithm for the challenging min-max multi-vehicle Chinese postman problem. It efficiently balances path lengths in large road networks, improving planning for tasks like urban inspection.

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Area of Science:

  • Operations Research
  • Graph Theory
  • Algorithm Design

Background:

  • The min-max multi-vehicle Chinese postman problem (MM-MVCPP) is NP-hard, crucial for road network path planning.
  • Large-scale networks present challenges in solution time and path length balancing.

Purpose of the Study:

  • To develop an efficient algorithm for solving the MM-MVCPP in large-scale road networks.
  • To improve the equalization of path lengths among multiple vehicles.

Main Methods:

  • Proposed a critical-edge tabu search algorithm (CTA-kroutes).
  • Initial solution generation via Eulerian path segmentation for balanced lengths.
  • Iterative optimization using tabu search and critical edge movements.
  • Solution refinement through de-duplication and optimal reconstruction.

Main Results:

  • CTA-kroutes effectively balances multi-vehicle path lengths.
  • Demonstrated applicability and efficiency on large-scale road networks.
  • Improved solution quality compared to existing methods (implied).

Conclusions:

  • CTA-kroutes provides an effective approach for the MM-MVCPP in large networks.
  • The algorithm enhances path equalization, crucial for practical applications.
  • Offers a viable solution for complex road network planning challenges.