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Publications
Santos, A.M.P., Fagerholt, K. and Guedes Soares, C. (2023), A stochastic optimization algorithm for the supply vessel planning problem under uncertain demand and uncertain weather conditions, Ocean Engineering, Vol. 278, 114428
The Supply Vessel Planning Problem (SVPP) with stochastic demands and uncertain weather conditions is a maritime transportation problem occurring in oshore oil and gas logistics which generalizes the Periodic Vehicle Routing Problem (PVRP) with Stochastic Demands, Stochastic Travel Times, and Time Windows. A eet of vessels is based at an onshore depot where the cargo to be distributed is kept, and delivers supplies to a set of oshore installations according to a weekly sailing schedule to ensure continuous production at the installations. However, the execution of the planned schedule is frequently disrupted due to adverse weather conditions and uncertain demand from the installations. Proposed methodologies to accommodate, in simultaneous, for the two major sources of uncertainty make use of two-phased methods, where simulation is combined with an optimization algorithm. However, robustness is incorporated through use of a subjective penalty measure to be applied to non-robust voyages, and the cost of recourse actions, which become necessary in the face of schedule disruption, is ignored. Moreover, it is well known that two-phased methods may lead to sub-optimal solutions. In contrast, this paper proposes a two-stage stochastic programming algorithm, allowing for the incorporation of the cost of recourse in the search phase. The cost of each solution is computed by means of a simulation engine embedded within a genetic algorithm, with weather conditions being modelled as a discrete-time Markov chain. For the tested problem instances, the potential benet from solving the stochastic program over solving the deterministic version corresponds to average relative annual cost savings of approximately 12%.
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