Berth allocation is one of the crucial points for efficient management of ports. This problem is complex due to all possible combinations for assigning ships to available compatible berths. This paper focuses on solving the Berth Allocation Problem (BAP) by optimising port operations using an innovative model. The problem analysed in this work deals with the Discrete and Dynamic Berth Allocation Problem (DDBAP). We propose a novel mathematical formulation expressed as a Mixed Integer Linear Programming (MILP) for solving the DDBAP. Furthermore, we adapted a metaheuristic solution approach based on the Bee Colony Optimisation (BCO) for solving large-sized combinatorial BAPs. In order to assess the solution performance and efficiency of the proposed model, we introduce a new set of instances based on real data of the Livorno port (Italy), and a comparison between the BCO algorithm and CPLEX in solving the DDBAP is performed. Additionally, the application of the proposed model to a real berth scheduling (Livorno port data) and a comparison with the Ant Colony Optimisation (ACO) metaheuristic are carried out. Results highlight the feasibility of the proposed model and the effectiveness of BCO when compared to both CPLEX and ACO, achieving computation times that ensure a real-time application of the method.

Car-Sharing Systems (CSSs) are becoming increasingly popular in urban areas replacing car ownership. The most attractive CSSs give users the opportunity to make one-way trips. This behavior creates an unbalanced status between stations. Hence, some users could leave the system because they may not find a car/parking place available near their origin/destination. In recent years, CSSs are employing Electric Vehicles (EVs). A recent technology applied to EVs, called Vehicle-to-Grid (V2G), has allowed selling energy, transferring it from vehicle batteries to an electric grid. In this paper, we adopt Electric Cars (ECs) with V2G for one-way station-based CSSs. We suggest ECs distribution among stations at the beginning of each day, simultaneously making the most of V2G technology and satisfying CSSs customers’ requests. These distributions represent the final configurations that should be obtained through overnight vehicle relocation. The proposed models have been applied to a real-sized case achieving promising results.