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A linearly decreasing deterministic annealing algorithm for the multi-vehicle dial-a-ride problem.

Amir Mortazavi1, Milad Ghasri1, Tapabrata Ray1

  • 1School of Engineering and Technology, UNSW Canberra, Canberra, ACT, Australia.

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This study introduces an enhanced deterministic annealing algorithm for solving large-scale dial-a-ride problems (DARPs). The new method efficiently finds optimal solutions, outperforming existing algorithms in speed and solution quality for transportation logistics.

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Area of Science:

  • Operations Research
  • Transportation Science
  • Computer Science

Background:

  • The dial-a-ride problem (DARP) is a complex variant of pick-up and delivery problems with significant real-world transportation applications.
  • Solving large-scale multi-vehicle DARPs efficiently remains a challenge in logistics and operations research.

Purpose of the Study:

  • To propose and evaluate an enhanced deterministic annealing algorithm for large-scale multi-vehicle DARPs.
  • To demonstrate the algorithm's ability to guarantee feasible solutions and accelerate the search for optimal outcomes.

Main Methods:

  • Development of an enhanced deterministic annealing algorithm incorporating advanced local search operators.
  • Implementation of a linearly decreasing deterministic annealing schedule to manage search space exploration.
  • Systematic experimental comparison against state-of-the-art methods like ALNS, ELS, and DA on standard benchmarks.

Main Results:

  • The proposed algorithm consistently explores the feasible search space, ensuring a valid solution at any termination point.
  • Experimental results indicate the enhanced algorithm achieves competitive objective values.
  • The method demonstrates superior average speed compared to leading algorithms like ALNS, ELS, and DA across various benchmarks.

Conclusions:

  • The enhanced deterministic annealing algorithm offers a robust and efficient approach for solving large-scale multi-vehicle DARPs.
  • This method provides a valuable tool for improving transportation logistics and operational efficiency.