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A hybrid metaheuristic approach for the capacitated arc routing problem

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  • Chen, Yuning
  • Hao, Jin-Kao
  • Glover, Fred

Abstract

The capacitated arc routing problem (CARP) is a difficult combinatorial optimization problem that has been intensively studied in the last decades. We present a hybrid metaheuristic approach (HMA) to solve this problem which incorporates an effective local refinement procedure, coupling a randomized tabu thresholding procedure with an infeasible descent procedure, into the memetic framework. Other distinguishing features of HMA include a specially designed route-based crossover operator for solution recombination and a distance-and-quality based replacement criterion for pool updating. Extensive experimental studies show that HMA is highly scalable and is able to quickly identify either the best known results or improved best known results for almost all currently available CARP benchmark instances. In particular, it discovers an improved best known result for 15 benchmark instances (6 classical instances and 9 large-sized instances whose optima are unknown). Furthermore, we analyze some key elements and properties of the HMA-CARP algorithm to better understand its behavior.

Suggested Citation

  • Chen, Yuning & Hao, Jin-Kao & Glover, Fred, 2016. "A hybrid metaheuristic approach for the capacitated arc routing problem," European Journal of Operational Research, Elsevier, vol. 253(1), pages 25-39.
    • Handle: RePEc:eee:ejores:v:253:y:2016:i:1:p:25-39
    DOI: 10.1016/j.ejor.2016.02.015
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Jesica Armas & Peter Keenan & Angel A. Juan & Seán McGarraghy, 2019. "Solving large-scale time capacitated arc routing problems: from real-time heuristics to metaheuristics," Annals of Operations Research, Springer, vol. 273(1), pages 135-162, February.
    2. Diego Pecin & Eduardo Uchoa, 2019. "Comparative Analysis of Capacitated Arc Routing Formulations for Designing a New Branch-Cut-and-Price Algorithm," Transportation Science, INFORMS, vol. 53(6), pages 1673-1694, November.
    3. Zsuzsanna Nagy & Ágnes Werner-Stark & Tibor Dulai, 2022. "An Artificial Bee Colony Algorithm for Static and Dynamic Capacitated Arc Routing Problems," Mathematics, MDPI, vol. 10(13), pages 1-38, June.
    4. Chen, Yuning & Hao, Jin-Kao, 2018. "Two phased hybrid local search for the periodic capacitated arc routing problem," European Journal of Operational Research, Elsevier, vol. 264(1), pages 55-65.
    5. Thibaut Vidal, 2017. "Node, Edge, Arc Routing and Turn Penalties: Multiple Problems—One Neighborhood Extension," Operations Research, INFORMS, vol. 65(4), pages 992-1010, August.
    6. Yu, Mingzhu & Jin, Xin & Zhang, Zizhen & Qin, Hu & Lai, Qidong, 2019. "The split-delivery mixed capacitated arc-routing problem: Applications and a forest-based tabu search approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 132(C), pages 141-162.
    7. López-Ibáñez, Manuel & Dubois-Lacoste, Jérémie & Pérez Cáceres, Leslie & Birattari, Mauro & Stützle, Thomas, 2016. "The irace package: Iterated racing for automatic algorithm configuration," Operations Research Perspectives, Elsevier, vol. 3(C), pages 43-58.
    8. Lu, Yongliang & Benlic, Una & Wu, Qinghua, 2018. "A memetic algorithm for the Orienteering Problem with Mandatory Visits and Exclusionary Constraints," European Journal of Operational Research, Elsevier, vol. 268(1), pages 54-69.
    9. Zhou, Qing & Benlic, Una & Wu, Qinghua & Hao, Jin-Kao, 2019. "Heuristic search to the capacitated clustering problem," European Journal of Operational Research, Elsevier, vol. 273(2), pages 464-487.
    10. Li, Jiliu & Qin, Hu & Shen, Huaxiao & Tsui, Kwok Leung, 2019. "The unilateral transportation problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 132(C), pages 1-29.

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