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We solve a periodic supply vessel planning problem under demand and weather uncertainty, arising in offshore of oil and gas production. Our study is motivated by the case of the Norwegian energy operator Equinor which supplied us with data. The aim is to determine an optimal fleet composition and a least-cost vessel schedule under uncertain demand at the installations and uncertain weather conditions. We present a methodology incorporating a metaheuristic within a discrete-event simulation model which, applied iteratively for the increasing values of reliability level parameters, yields a vessel schedule of least expected cost.

The installation process of offshore wind farms is complex and requires a high level of coordination, agile scheduling and systematic methods. High risks and costs are involved and especially weather conditions have an impact on the marine operations. A computer tool for optimisation and simulation of marine operations has been developed in close cooperation with a large actor in the offshore wind industry. The tool has a web-based user interface and an optimisation engine that is based on a version of a genetic algorithm and uses an agent-based simulation process to evaluate installation schedules with respect to weather risks, time and costs. The objective is to provide decision support for the early phase planning of wind farm installation by offering an efficient method for estimation of cost and time for future projects.