An adaptive biased random-key genetic algorithm for the tactical berth allocation problem

The Tactical Berth Allocation Problem (TBAP) is an integrated solution approach for berth allocation and quay crane assignment in container terminal operations. TBAP aims to allocate ships to berthing positions and assign them to quay crane profiles with some quay cranes per time step. The objectives are to maximize the total value of the quay crane profiles assigned to ships and minimize the housekeeping costs derived from the transshipment container flows between ships. In this research paper, we develop an adaptive Biased Random-Key Genetic Algorithm (A-BRKGA) to solve the TBAP. The A-BRKGA is a recent method with online parameter control, so users have no visible parameters. The strategy for parameter adapting is based on deterministic rules and self-adaptive schemes. Computational results show that the A-BRKGA is competitive with the current state-of-the-art methods and can find the best-known solutions for most tested instances.