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

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  • Amir Mortazavi
  • Milad Ghasri
  • Tapabrata Ray

Abstract

Dial a ride problem (DARP) is a complex version of the pick-up and delivery problem with many practical applications in the field of transportation. This study proposes an enhanced deterministic annealing algorithm for the solution of large-scale multi-vehicle DARPs. The proposed method always explores the feasible search space; therefore, a feasible solution is guaranteed at any point of termination. This method utilises advanced local search operators to accelerate the search for optimal solutions and it relies on a linearly decreasing deterministic annealing schedule to limit poor jumps during the course of search. This study puts forward a systematic series of experiments to compare the performance of solution methods from various angles. The proposed method is compared with the most efficient methods reported in the literature i.e., the Adaptive Large Neighbourhood Search (ALNS), Evolutionary Local Search (ELS), and Deterministic Annealing (DA) using standard benchmarks. The results suggest that the proposed algorithm is on average faster than the state-of-the-art algorithms in reaching competitive objective values across the range of benchmarks.

Suggested Citation

  • Amir Mortazavi & Milad Ghasri & Tapabrata Ray, 2024. "A linearly decreasing deterministic annealing algorithm for the multi-vehicle dial-a-ride problem," PLOS ONE, Public Library of Science, vol. 19(2), pages 1-26, February.
    • Handle: RePEc:plo:pone00:0292683
    DOI: 10.1371/journal.pone.0292683
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    References listed on IDEAS

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    1. Timo Gschwind & Stefan Irnich, 2015. "Effective Handling of Dynamic Time Windows and Its Application to Solving the Dial-a-Ride Problem," Transportation Science, INFORMS, vol. 49(2), pages 335-354, May.
    2. Timo Gschwind & Michael Drexl, 2019. "Adaptive Large Neighborhood Search with a Constant-Time Feasibility Test for the Dial-a-Ride Problem," Transportation Science, INFORMS, vol. 53(2), pages 480-491, March.
    3. Mohamed Amine Masmoudi & Manar Hosny & Emrah Demir & Erwin Pesch, 2020. "Hybrid adaptive large neighborhood search algorithm for the mixed fleet heterogeneous dial-a-ride problem," Journal of Heuristics, Springer, vol. 26(1), pages 83-118, February.
    4. R M Jorgensen & J Larsen & K B Bergvinsdottir, 2007. "Solving the Dial-a-Ride problem using genetic algorithms," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(10), pages 1321-1331, October.
    5. Xiang, Zhihai & Chu, Chengbin & Chen, Haoxun, 2008. "The study of a dynamic dial-a-ride problem under time-dependent and stochastic environments," European Journal of Operational Research, Elsevier, vol. 185(2), pages 534-551, March.
    6. Jean-François Cordeau, 2006. "A Branch-and-Cut Algorithm for the Dial-a-Ride Problem," Operations Research, INFORMS, vol. 54(3), pages 573-586, June.
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