Supply vessel routing and scheduling under uncertain demand

We solve a supply vessel planning problem arising in upstream offshore petroleum logistics. A fleet of supply vessels delivers all the necessary equipment and materials to a set of offshore installations from an onshore supply base, according to a delivery schedule or sailing plan. Supply vessels, being the major cost contributor, are chartered on a long-term basis. The planning of supply vessels implies resolving the trade-off between the cost of the delivery schedule and the reliability of deliveries on the scheduled voyages, i.e. the service level. The execution of a sailing plan is affected by stochastic demands at the installations since a high demand fluctuation quite often leads to insufficient vessel capacity to perform a voyage according to the sailing plan. In addition, the average demand level at the installations may change over time, while the number of vessels in the sailing plan remains the same. Maintaining a reliable flow of supplies under stochastic demand therefore leads to additional costs and reduced service level. We present a novel methodology for reliable supply vessel planning and scheduling, enabling planners to construct delivery schedules having a low expected total cost. The methodology involves the construction of delivery schedules with different reliability levels using an adaptive large neighborhood search metaheuristic algorithm combined with a discrete event simulation procedure for the computation of the expected solution cost. Keywords: maritime logistics, supply vessel planning, recourse, reliable vessel schedules, metaheuristic, simulationpublishedVersio

Robust Solution Approach for the Dynamic and Stochastic Vehicle Routing Problem

The dynamic and stochastic vehicle routing problem (DSVRP) can be modelled as a stochastic program (SP). In a two-stage SP with recourse model, the first stage minimizes the a priori routing plan cost and the second stage minimizes the cost of corrective actions, performed to deal with changes in the inputs. To deal with the problem, approaches based either on stochastic modelling or on sampling can be applied. Sampling-based methods incorporate stochastic knowledge by generating scenarios set on realizations drawn from distributions. In this paper we proposed a robust solution approach for the capacitated DSVRP based on sampling strategies. We formulated the problem as a two-stage stochastic program model with recourse. In the first stage the a priori routing plan cost is minimized, whereas in the second stage the average of higher moments for the recourse cost calculated via a set of scenarios is minimized. The idea is to include higher moments in the second stage aiming to compute a robust a priori routing plan that minimizes transportation costs while permitting small changes in the demands without changing solution structure. Additionally, the approach allows managers to choose between optimality and robustness, that is, transportation costs and reconfiguration. The computational results on a generic dynamic benchmark dataset show that the robust routing plan can cover unmet demand while incurring little extra costs as compared to the preplanning. We observed that the plan of routes is more robust; that is, not only the expected real cost, but also the increment within the planned cost is lower