A Vessel Pickup and Delivery Problem from the Disruption Management in Offshore Supply Vessel Operations

This paper considers a vessel pickup and delivery problem that arises in the case of disruptions in the supply vessel logistics in the offshore oil and gas industry. The problem can be modelled as a multi-vehicle pickup and delivery problem where delivery orders are transported by supply vessels from an onshore supply base (depot) to a set of offshore oil and gas installations, while pickup orders are to be transported from the installations back to the supply base (i.e. backload). We present both an arc-flow and a path-flow formulation for the problem. For the path-flow formulation we also propose an efficient dynamic programming algorithm for generating the paths, which represent feasible vessel voyages. It is shown through a computational study on various realistic test instances provided by a major oil and gas company that the path-flow model is superior with respect to computational performance.acceptedVersio

The non-emergency patient transport modelled as a team orienteering problem

This work presents an improved model to solve the non-emergency patients transport (NEPT) service issues given the new rules recently established in Portugal. The model follows the same principle of the Team Orienteering Problem by selecting the patients to be included in the routes attending the maximum reduction in costs when compared with individual transportation. This model establishes the best sets of patients to be transported together. The model was implemented in AMPL and a compact formulation was solved using NEOS Server. A heuristic procedure based on iteratively solving Orienteering Problems is presented, and this heuristic provides good results in terms of accuracy and computation time. Euclidean instances as well as asymmetric real data gathered from Google maps were used, and the model has a promising performance mainly with asymmetric cost matrices.Project GATOP - Genetic Algorithms for Team Orienteering Problem (Ref PTDC/EME-GIN/120761/2010), financed by national funds by FCT / MCTES, and co-funded by the European Social Development Fund (FEDER) through the COMPETE - Programa Operacional Fatores de Competitividade (POFC) Ref FCOMP-01-0124-FEDER-020609. This work has been par tially supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/201

Creating lasso-solutions for the traveling salesman problem with pickup and delivery by Tabu search

Traveling Salesman Problem, Pickup and Delivery, Tabu Search, Lasso Solutions,

A simulated annealing algorithm based solution method for a green vehicle routing problem with fuel consumption

This chapter presents a new G-VRP model that aims to reduce the fuel consumption of the vehicle’s gas tank. The fuel consumption of a vehicle is related to total vehicle weight through route and thus, this changes the CO2 levels as a result of the changes of total weight and distance for any arc {i, j} in the route. To minimize CO2 levels, a simulated annealing-based algorithm is proposed. About the experiments, firstly, we applied small-VRP problem set for defining the proposed algorithm and then, the Christofides et al. (Combinatorial optimization. Wiley, 1979) small/medium scale C1–C14 datasets are used with proposed G-VRP model and a convex composition solution with two objective functions. The proposed methods are compared with statistical analysis techniques to explain the statistical significance of solutions. The procedures are also tested using additional examples previously analyzed in the literature. The result has shown good solutions for minimizing the emitted CO2 levels. © Springer Nature Switzerland AG 2019