A mixed-integer linear programming model for optimal vessel scheduling in offshore oil and gas operations.

*(English)*Zbl 1373.90020Summary: This paper introduces a non-standard vehicle routing problem (VRP) arising in the oil and gas industry. The problem involves multiple offshore production facilities, each of which requires regular servicing by support vessels to replenish essential commodities such as food, water, fuel, and chemicals. The support vessels are also required to assist with oil off-takes, in which oil stored at a production facility is transported via hose to a waiting tanker. The problem is to schedule a series of round trips for the support vessels so that all servicing and off-take requirements are fulfilled, and total cost is minimized. Other constraints that must be considered include vessel suitability constraints (not every vessel is suitable for every facility), depot opening constraints (base servicing can only occur during specified opening periods), and off-take equipment constraints (the equipment needed for off-take support can only be deployed after certain commodities have been offloaded). Because of these additional constraints, the scheduling problem under consideration is far more difficult than the standard VRP. We formulate a mixed-integer linear programming (MILP) model for determining the optimal vessel schedule. We then verify the model theoretically and show how to compute the vessel utilization ratios for any feasible schedule. Finally, simulation results are reported for a real case study commissioned by Woodside Energy Ltd, Australia’s largest dedicated oil and gas company.

##### MSC:

90B06 | Transportation, logistics and supply chain management |

90B10 | Deterministic network models in operations research |

90C11 | Mixed integer programming |

90C27 | Combinatorial optimization |

##### Keywords:

optimal scheduling; vehicle routing problem; mixed-integer linear programming; oil and gas industry
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\textit{E. Mardaneh} et al., J. Ind. Manag. Optim. 13, No. 4, 1601--1623 (2017; Zbl 1373.90020)

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