Vessel-UAV Collaborative Optimization for the Offshore Oil and Gas Pipelines Inspection

This paper investigates a two-stage optimization problem arising from the vessel-UAV collaborative inspection practice for oil and gas pipelines in an offshore oil and gas field (OF). Multiple vessels depart from a base to perform pipeline inspections using loaded UAVs such that the total travel cost of all vessels is minimized. A two-stage formulation with fuzzy parameters is established to address the collaborative optimization problem. The first-stage solves a UAV routing problem to determine the fuzzy service time at each OF. In particular, we use the triangular fuzzy number to characterize the inspection time uncertainty of UAVs on pipelines. The obtained fuzzy service time serves as the input for the second-stage vessel routing problem, which includes chance constraints. We then apply the credibility theory to transform the model into a deterministic and tractable mixed-integer linear program (MILP), which can be solved using commercial solvers. However, given the NP-hardness of the studied problems, commercial solvers have difficulty when practical-sized instances are involved. To this end, we further develop a saving heuristic and a genetic-tabu search heuristic to solve the first- and second-stage models, respectively. We conduct numerical experiments using a practical-sized illustrative example to demonstrate the effectiveness and efficiency of the proposed methods.