Li, Yantong, Wang, Xingqi, Zhang, Shuai ORCID: 0000-0002-9796-058X and Zhou, Shanshan (2023) Vessel-UAV collaborative routing problem for offshore oil and gas fields inspection. In: 20th IEEE International Conference on Networking, Sensing and Control (Hybrid Mode), 25th - 27th October 2023, Marseille, France. (In Press)

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Abstract

This paper proposes a new mode of vessel-UAV collaborative inspection to address the challenges of high cost, low frequency, and high risk in the traditional offshore oilfield inspection. In the studied problem, a patrol vessel with a UAV departs from the port, sails to a specific location, releases the UAV for oilfield inspection, and retrieves the UAV at another location after the inspection. Considering the spatio-temporal cooperation between ships and UAVs and the variation of endurance capa- bility of UAVs, the collaborative inspection routing problem of ”single vessel and UAV take off to visit multiple target points” is studied, with a target of minimizing the total variable cost of the vessel and UAV and the sailing time of the vessel. A mixed integer second-order cone programming model based on the time period and the moment of time was established, respectively, to calculate the objective function value of the inspection routing cost. The commercial solver CPLEX is used to solve the proposed models. Numerical experiments based on actual and randomly generated cases are conducted to verify the efficiency of the model. In addition, sensitivity analysis is performed in our paper, including UAV endurance and speed, which can inspire managers for scheduling optimization in practice.

Item Type:	Conference or Conference Paper (Paper)
Uncontrolled Keywords:	offshore oilfield inspection; vessel-UAV collaborative; integrated routing optimization; mixed integer second-order cone programming
Subjects:	H Social Sciences > HC Economic History and Conditions H Social Sciences > HD Industries. Land use. Labor T Technology > T Technology (General)
Faculty / School / Research Centre / Research Group:	Faculty of Business
Last Modified:	01 Sep 2023 11:25
URI:	http://gala.gre.ac.uk/id/eprint/43841

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