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A comparison of three metaheuristics for the workover rig routing problem

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  • Ribeiro, Glaydston Mattos
  • Laporte, Gilbert
  • Mauri, Geraldo Regis

Abstract

The workover rig routing problem (WRRP) is a variant of the Vehicle Routing Problem with Time Windows (VRPTW) and arises in the operations of onshore oil fields. In this problem, a set of workover rigs located at different positions must service oil wells requesting maintenance as soon as possible. When a well requires maintenance, its production is reduced or stopped for safety reasons and some workover rig must service it within a given deadline. It is therefore important to service the wells in a timely fashion in order to minimize the production loss. Whereas for classical VRPTWs the objective is to minimize route length, in the WRRP the objective is to minimize the total lost production, equal to the sum of arrival times at the wells, multiplied by production loss rates. The WRRP generalizes the Delivery Man Problem with Time Windows by considering multiple open vehicle routes and multiple depots. This paper compares three metaheuristics for the WRRP: an iterated local search, a clustering search, and an Adaptive Large Neighborhood Search (ALNS). All approaches, in particular ALNS, have yielded good solutions for instances derived from a real-life setting.

Suggested Citation

  • Ribeiro, Glaydston Mattos & Laporte, Gilbert & Mauri, Geraldo Regis, 2012. "A comparison of three metaheuristics for the workover rig routing problem," European Journal of Operational Research, Elsevier, vol. 220(1), pages 28-36.
    • Handle: RePEc:eee:ejores:v:220:y:2012:i:1:p:28-36
    DOI: 10.1016/j.ejor.2012.01.031
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    References listed on IDEAS

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    1. Stefan Ropke & David Pisinger, 2006. "An Adaptive Large Neighborhood Search Heuristic for the Pickup and Delivery Problem with Time Windows," Transportation Science, INFORMS, vol. 40(4), pages 455-472, November.
    2. Alexandre Venturin Faccin Pacheco & Glaydston Mattos Ribeiro & Geraldo Regis Mauri, 2010. "A Grasp with Path-Relinking for the Workover Rig Scheduling Problem," International Journal of Natural Computing Research (IJNCR), IGI Global, vol. 1(2), pages 1-14, April.
    3. Ropke, Stefan & Pisinger, David, 2006. "A unified heuristic for a large class of Vehicle Routing Problems with Backhauls," European Journal of Operational Research, Elsevier, vol. 171(3), pages 750-775, June.
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    6. Matteo Fischetti & Gilbert Laporte & Silvano Martello, 1993. "The Delivery Man Problem and Cumulative Matroids," Operations Research, INFORMS, vol. 41(6), pages 1055-1064, December.
    7. A N Letchford & J Lysgaard & R W Eglese, 2007. "A branch-and-cut algorithm for the capacitated open vehicle routing problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(12), pages 1642-1651, December.
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    Cited by:

    1. Albert Einstein Fernandes Muritiba & Tibérius O. Bonates & Stênio Oliveira Da Silva & Manuel Iori, 2021. "Branch-and-Cut and Iterated Local Search for the Weighted k -Traveling Repairman Problem: An Application to the Maintenance of Speed Cameras," Transportation Science, INFORMS, vol. 55(1), pages 139-159, 1-2.
    2. Rabello, Rômulo Louzada & Mauri, Geraldo Regis & Ribeiro, Glaydston Mattos & Lorena, Luiz Antonio Nogueira, 2014. "A Clustering Search metaheuristic for the Point-Feature Cartographic Label Placement Problem," European Journal of Operational Research, Elsevier, vol. 234(3), pages 802-808.
    3. Bakker, Steffen J. & Wang, Akang & Gounaris, Chrysanthos E., 2021. "Vehicle routing with endogenous learning: Application to offshore plug and abandonment campaign planning," European Journal of Operational Research, Elsevier, vol. 289(1), pages 93-106.
    4. Luo, Zhixing & Qin, Hu & Lim, Andrew, 2014. "Branch-and-price-and-cut for the multiple traveling repairman problem with distance constraints," European Journal of Operational Research, Elsevier, vol. 234(1), pages 49-60.

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