Berth and quay crane coordinated scheduling using multi-objective chaos cloud particle swarm optimization algorithm - Li, Ming-Wei; Hong, Wei-Chiang; Geng, Jing; Wang, Jianlun (Neural computing & applications, 2017)
The demand for the maritime transportation has significantly increased over the past 20 years due to the rapid pace of globalization. Terminal managers confront the challenge in establishing the appropriate berth and quay crane (QC) coordinated schedule to achieve the earliest departure time of ship and to provide efficient service. In this paper, we propose a multi-objective berth and QC coordinated scheduling model, namely M-B&QC, by taking the minimum additional trucking distance and the port time of ships as the optimization objectives, with the constraints based on demand of port operations and vessel berthing. To solve the M-B&QC model, the particle coding rule and the particle feasible-integer processing module (namely PF-IP) for improving PSO performance are employed to determine the computation strategies of individual historical optimal value p i G and global optimal value P g G of particle for the multi-objective optimization. In addition, the global disturbance with cat mapping function (namely GDCM) and local search with cloud model (namely LSCM) are also hybridized, namely PM&CCPSO algorithm, to solve the M-B&QC model. Numerical experiments including eight combined examples are conducted to test the performance of the proposed programming model and the modified solving algorithm.