Abstract
Onshore oil fields may contain hundreds of wells that use sophisticated and complex equipments. These equipments need regular maintenance to keep the wells at maximum productivity. When the productivity of a well decreases, a specially-equipped vehicle called a workover rig must visit this well to restore its full productivity. Given a heterogeneous fleet of workover rigs and a set of wells requiring maintenance, the workover rig routing problem (WRRP) consists of finding rig routes that minimize the total production loss of the wells over a finite horizon. The wells have different loss rates, need different services, and may not be serviced within the horizon. On the other hand, the number of available workover rigs is limited, they have different initial positions, and they do not have the same equipments. This paper presents and compares four heuristics for the WRRP: an existing variable neighborhood search heuristic, a branch-price-and-cut heuristic, an adaptive large neighborhood search heuristic, and a hybrid genetic algorithm. These heuristics are tested on practical-sized instances involving up to 300 wells, 10 rigs on a 350-period horizon. Our computational results indicate that the hybrid genetic algorithm outperforms the other heuristics on average and in most cases.
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Acknowledgments
Glaydston Mattos Ribeiro acknowledges the Espírito Santo Research Foundation and the National Council for Scientific and Technological Development for their financial support. Guy Desaulniers and Jacques Desrosiers acknowledge the National Science and Engineering Research Council of Canada for its financial support.
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Appendix: Detailed results
Appendix: Detailed results
In this appendix, we report the detailed results of our computational experiments that were summarized in Tables 1, 2, 3, and 4. There is one table per parameter combination (\(|W|,\,|K|,\,H\)). In each table, the first column specifies the instance number (out of 10 instances). The next column indicates the best-known solution value (BKUB). Out of these 80 upper bounds, 66 correspond to optimal solutions provided by the exact BPC algorithm of Ribeiro et al. (2012) and 14 are new best-known values (in bold face) obtained by HGA and reasserted by ALNS (5) and BPC (4). For each heuristic, the tables report four columns (only three for BPC): best solution value found over all runs (Best), average solution value (Avg), average computational time in seconds (Time), and the average solution value deviation with respect to BKUB in percentage (Dev) computed as \(\hbox {Dev} = 100 \times \left( \hbox {BKUB} - \hbox {Avg} \right) /\hbox {BKUB}.\)
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Ribeiro, G.M., Desaulniers, G., Desrosiers, J. et al. Efficient heuristics for the workover rig routing problem with a heterogeneous fleet and a finite horizon. J Heuristics 20, 677–708 (2014). https://doi.org/10.1007/s10732-014-9262-1
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DOI: https://doi.org/10.1007/s10732-014-9262-1