Archives > Volume 11 | Number 4 | December 2016 > pp 299–310
Advances in Production Engineering & Management
Volume 11 | Number 4 | December 2016 | pp 299–310
Applying multi-phase particle swarm optimization to solve bulk cargo port scheduling problem
Tang, M.; Gong, D.; Liu, S.; Zhang, H.
ABSTRACT AND REFERENCES (PDF) |
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A B S T R A C T
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 number of vessels, the number of berths, vessel-berthing constraints (basic factors), the service priority, and the makespan (special factors), and then establishes 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 priority, 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 relatively 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.
A R T I C L E I N F O
Keywords • Bulk cargo, Scheduling, Priority, Makespan, Multi-phase particle swarm optimization (MPPSO)
Corresponding author • Gong, D.
Article history • Received 7 October 2016, Revised 9 November 2016, Accepted 10 November 2016
Published on-line • 10 December 2016
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