Multi-objective optimization of large scale berth allocation and quay crane assignment problems

This paper describes the use of multi-objective optimization on a berth allocation and quay crane assignment problem (BACAP). The BACAP involves the simultaneous optimization of two highly-coupled container terminal operations, namely berth allocation and quay crane assignment, which have been traditionally solved as individual problems. The developed multi-objective evolutionary algorithm (MOEA) is validated on a large scale BACAP dataset, consisting of 23 berths and 87 quay cranes, generated based on the port conditions at the Pasir Panjang container terminal, which is the largest container terminal in Singapore. Optimization results show that the multi-objective optimization approach offers the port manager flexibility in selecting a desirable solution for implementation.