Search Results

The Berth Allocation and Scheduling Problem (BASP) is a critical optimization challenge in maritime logistics, aiming to assign arriving vessels to berths efficiently, while adhering to practical constraints. Exploiting the connection of BASP with the Heterogeneous Vehicle Routing Problem with Time Windows (HVRPTW), we propose a mixed integer linear programming (MILP) formulation for a variant of BASP which is of utmost importance in real-world scenarios: the Dynamic Discrete Berth Allocation and Scheduling Problem with Time Windows (DDBASPTW). Consequently, inspired by the wealth of constructive and improvement heuristics for VRP, we design, implement and experimentally evaluate three constructive heuristics, Nearest Neighbour (NN), Insertion (INS), a quick-and-dirty variant of Insertion (qd-INS), as well as two improvement heuristics, Swap and Reinsert, taking into consideration both the online and the offline scenario with respect to vessel arrivals. Finally, we propose, implement and experimentally evaluate, custom-tailored variants for DDBASPTW of a single-solution metaheuristic, the Adaptive Large Neighborhood Search (ALNS), and of two population-based metaheuristics, the Genetic Algorithm (GA) and the Cuckoo Search Algorithm (CSA), which are aimed to solve the offline version of the problem. An extensive experimental evaluation compares these techniques against a generic state-of-the-art MILP solver. Results demonstrate that certain variants of INS not only are extremely fast and deliver competitive solutions, achieving a practical trade-off between execution times and quality of solutions. The improvement heuristics further refine the initial solutions, especially for weaker constructive approaches, offering a lightweight yet effective enhancement mechanism. The metaheuristics consistently yield high-quality solutions with significantly lower computational times compared to the exact MILP solver, making them well-suited for use in real-time or large-scale operational environments.