Applying multi-phase particle swarm optimization to solve bulk cargo port scheduling problem

Abstract

Factors related to bulk cargo port scheduling are very complex and peculiar. Changes in the factors will affect the reusability of a model, so establishing a reliable scheduling model for bulk cargo ports is particularly important. This paper sorts the factors affecting bulk cargo port scheduling, such as the num-ber of vessels, the number of berths, vessel-berthing constraints (basic fac-tors), the service priority, and the makespan (special factors), and then estab-lishes the non-deterministic polynomial (NP) model, which aims to minimize the total service time and makespan. Lastly, it solves the model based on the multi-phase particle swarm optimization (MPPSO) algorithm and Matlab. Some important conclusions are obtained. (1) For the model neglecting prior-ity, the total service time is the smallest, whereas the maximum waiting time and maximum operating time are relatively large, and the makespan is the latest. (2) For the model considering priority, the total service time is relative-ly large, whereas the maximum waiting time and maximum operating time are relatively small, and the makespan is relatively early. (3) For the model considering the makespan, the total service time is the mostlargest, whereas the maximum waiting time and especially the maximum operating time are the smallest, and the makespan is the earliest. We can choose different models according to different situations in bulk cargo port scheduling.