Metaheuristic Approaches for the Minimum Cost Hybrid Berth Allocation Problem

Abstract

The Minimum Cost Hybrid Berth Allocation problem is defined as follows: for a given list of vessels with fixed handling times, the appropriate intervals in berth and time coordinates have to be determined in such a way that the total cost is minimized. The costs are influenced by positioning of vessels, time of berthing, and time of completion for all vessels. Having in mind that the speed of finding high-quality solutions is of crucial importance for designing an efficient and reliable decision support system in container terminal, metaheuristic methods are the obvious choice for solving MCHBAP. In this chapter, we survey Evolutionary Algorithm (EA), Bee Colony Optimization (BCO), and Variable Neighborhood Descent (VND) metaheuristics, and propose General Variable Neighborhood Search (GVNS) approach for MCHBAP. All four metaheuristics are evaluated and compared against each other and against exact solver on real-life and randomly generated instances from the literature. The analysis of the obtained results shows that on instances reflecting real-life situations, all four metaheuristics were able to find optimal solutions in short execution times. The newly proposed GVNS showed to be superior over the remaining three metaheuristics in the sense of running times. Randomly generated instances were out of reach for exact solver, while EA, BCO, VND, and GVNS easily provided high-quality solutions in each run. The results obtained on generated data set show that the newly proposed GVNS outperformed EA, BCO, and VND regarding the running times while preserving the high quality of solutions. The computational analysis indicates that MCHBAP can be successfully addressed by GVNS and we believe that it is applicable to related problems in maritime transportation.