Selecting Algorithms for Large Berth Allocation Problems

Abstract This paper considers algorithm selection for the berth allocation problem (BAP) under algorithm runtime limits. BAP consists in scheduling ships on berths subject to ship ready times and size constraints, for a certain objective function. For the purposes of strategic port capacity planning, BAP must be solved many times in extensive simulations, needed to account for ship traffic and handling times uncertainties, and alternative terminal designs. The algorithm selection problem (ASP) consists in selecting algorithms with the best performance for a considered application. We propose a new method of selecting a portfolio of algorithms that will solve the considered BAP instances and return good solutions. The portfolio selection is based on the performance on the training instances. The performance is measured by the runtime and solution quality. In order to select the portfolio, a linear program minimizing the solution quality loss, subject to overall runtime limit is used. Thus, the portfolio evolves with the runtime limit, which is a key parameter in designing the port capacity simulations. For the training and validating datasets, random instances and real ship traffic logs are used. A portfolio of heuristics is developed which can be used for solving large instances of BAP, emerging when time horizons of months or years are considered. The evolution of the algorithm portfolios under changing runtime limits is studied. The portfolio abilities to solve new instances are assessed.