Integrated Berth Allocation and Quay Crane Assignment Problem: Set partitioning models and computational results

Most of the operational problems in container terminals are strongly interconnected. In this paper, we study the integrated Berth Allocation and Quay Crane Assignment Problem in seaport container terminals. We will extend the current state-of-the-art by proposing novel set partitioning models. To improve the performance of the set partitioning formulations, a number of variable reduction techniques are proposed. Furthermore, we analyze the effects of different discretization schemes and the impact of using a time-variant/invariant quay crane allocation policy. Computational experiments show that the proposed models significantly improve the benchmark solutions of the current state-of-art optimal approaches

The Electric Fleet Size and Mix Vehicle Routing Problem with Time Windows and Recharging Stations

International audienceDue to new regulations and further technological progress in the field of electric vehicles, the research community faces the new challenge of incorporating the electric energy based restrictions into vehicle routing problems. One of these restrictions is the limited battery capacity which makes detours to recharging stations necessary, thus requiring efficient tour planning mechanisms in order to sustain the competitiveness of electric vehicles compared to conventional vehicles. We introduce the Electric Fleet Size and Mix Vehicle Routing Problem with Time Windows and recharging stations (E-FSMFTW) to model decisions to be made with regards to fleet composition and the actual vehicle routes including the choice of recharging times and locations. The available vehicle types differ in their transport capacity, battery size and acquisition cost. Furthermore, we consider time windows at customer locations, which is a common and important constraint in real-world routing and planning problems. We solve this problem by means of branch-and-price as well as proposing a hybrid heuristic, which combines an Adaptive Large Neighbourhood Search with an embedded local search and labelling procedure for intensification. By solving a newly created set of benchmark instances for the E-FSMFTW and the existing single vehicle type benchmark using an exact method as well, we show the effectiveness of the proposed approach

Routing strategy in a distribution network when the driver learning effect is considered

This paper faces one critical short term decision: the construction of routes for daily goods\u2019 deliveries in a distribution network. It investigates the possibility of applying a fixed routing strategy instead of a daily routing optimisation strategy, analysing the benefits derived from the driver\u2019s familiarity with his/her surroundings, customers\u2019 habits and set-ups. The final aim is to provide an effective and flexible decision-making tool to identify the best routing strategy, considering the level of learning invested by each driver. It is assumed that learning effect impacts on the service time, i.e., on the time spent in service operations once arrived at the customer\u2019s site. Results demonstrate that fixed routes strategy can often be better than the daily optimised strategy and strongly depends on the parameters investigated in this work. Two case applications are provided to help the reader\u2019s comprehension and demonstrate the methodology\u2019s potential and its practical implications