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Iteration-related various learning particle swarm optimization for quay crane scheduling problem

Yu, Mingzhu; Cong, Xuwen Cong; Niu, Ben; Qu, Rong

Authors

Mingzhu Yu

Xuwen Cong Cong

Ben Niu

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RONG QU rong.qu@nottingham.ac.uk
Associate Professor



Abstract

Quay crane scheduling is critical in reducing operation costs at container terminals. Designing a schedule to handling containers in an efficient order can be difficult. For this problem which is proved NP-hard, heuristic algorithms are effective to obtain preferable solutions within limited computational time. When solving discrete optimization problems, particles are very susceptible to local optimum in Standard Particle Swarm Optimization (SPSO). To overcome this shortage, this paper proposes an iteration-related various learning particle swarm optimization (IVLPSO). This algorithm employs effective mechanisms devised to obtain satisfactory quay crane operating schedule efficiently. Superior solutions can save up to 5 h for handling a batch of containers, thus significantly reduces costs for terminals. Numerical studies show that the proposed algorithm outperforms state-of-the-art existing algorithms. A series of experimental results demonstrate that IVLPSO performs quite well on obtaining satisfactory Pareto set with quick convergence.

Start Date Sep 18, 2018
Publication Date Oct 6, 2018
Publisher Springer Publishing Company
Volume 952
Pages 201-212
Series Title Communications in computer and information science
Series Number 952
Book Title Bio-inspired computing: theories and applications: 13th International Conference, BIC-TA 2018, Beijing, China, November 2–4, 2018, Proceedings, Part II
APA6 Citation Yu, M., Cong, X. C., Niu, B., & Qu, R. (2018). Iteration-related various learning particle swarm optimization for quay crane scheduling problem. In Bio-inspired computing: theories and applications: 13th International Conference, BIC-TA 2018, Beijing, China, November 2–4, 2018, Proceedings, Part II, 201-212. doi:10.1007/978-981-13-2829-9_19
DOI https://doi.org/10.1007/978-981-13-2829-9_19
Publisher URL https://link.springer.com/chapter/10.1007%2F978-981-13-2829-9_19

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