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Rig Routing with Possible Returns and Stochastic Drilling Times

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Mathematical Optimization Theory and Operations Research (MOTOR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12755))

Abstract

We consider a real-world vehicle routing problem with time windows, arising in drilling rigs routing and well servicing on a set of sites with different geographical locations. Each site includes a predetermined number of wells which must be processed within a given time window. The same rig can visit a site several times, but the overall number of site visits by rigs is bounded from above. Each well is drilled by one rig without preemptions. It is required to find the routes of the rigs, minimizing the total traveling distance. We also consider a stochastic generalization of the problem, where the drilling times are supposed to be random variables with known discrete distributions. New mixed-integer linear programming models are formulated and tested experimentally. A randomized greedy algorithm is proposed for approximate solving the problem in stochastic formulation, if the number of possible realizations of drilling times is so high that existing MIP solvers are not suitable.

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Notes

  1. 1.

    The choice of this solver was based on a preliminary experiment, which indicated that on the MIP instances considered here Gurobi has an advantage to other solvers available to us (e.g. it was approximately twice as fast in comparison with CPLEX 12.10.0.0).

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Acknowledgement

The research is supported by Russian Science Foundation grant 21-41-09017. A server of Sobolev Institute of Mathematics, Omsk Branch is used for computing.

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Authors and Affiliations

  1. Sobolev Institute of Mathematics, Novosibirsk, Russia

    Pavel Borisovsky, Anton Eremeev, Yulia Kovalenko & Lidia Zaozerskaya

Authors
  1. Pavel Borisovsky
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  2. Anton Eremeev
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  3. Yulia Kovalenko
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  4. Lidia Zaozerskaya
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Editor information

Editors and Affiliations

  1. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  2. Krasovsky Institute of Mathematics and Mechanics, Ekaterinburg, Russia

    Michael Khachay

  3. Matrosov Institute for System Dynamics and Control Theory, Irkutsk, Russia

    Alexander Kazakov

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Borisovsky, P., Eremeev, A., Kovalenko, Y., Zaozerskaya, L. (2021). Rig Routing with Possible Returns and Stochastic Drilling Times. In: Pardalos, P., Khachay, M., Kazakov, A. (eds) Mathematical Optimization Theory and Operations Research. MOTOR 2021. Lecture Notes in Computer Science(), vol 12755. Springer, Cham. https://doi.org/10.1007/978-3-030-77876-7_4

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  • DOI: https://doi.org/10.1007/978-3-030-77876-7_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-77875-0

  • Online ISBN: 978-3-030-77876-7

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