Particle swarm optimization algorithm for the berth allocation problem

•We propose a particle swarm optimization (PSO) algorithm for the dynamic berth allocation problem.•Two sets of benchmark instances were tested and results were compared with other leading algorithms.•Our PSO is able to obtain the optimal solutions in shorter computational time than other methods. T...

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Bibliographic Details
Published in:Expert systems with applications 2014-03, Vol.41 (4), p.1543-1550
Main Authors: Ting, Ching-Jung, Wu, Kun-Chih, Chou, Hao
Format: Article
Language:English
Subjects:
Algorithms
Allocations
Applied sciences
Berth allocation problem
Container port
Exact sciences and technology
Exact solutions
Ground, air and sea transportation, marine construction
Logistics
Marine and water way transportation and traffic
Mathematical programming
Oceans
Operational research and scientific management
Operational research. Management science
Optimization
Particle swarm optimization
Ports
Queuing theory. Traffic theory
Scheduling, sequencing
Ships
Swarm intelligence
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Summary:•We propose a particle swarm optimization (PSO) algorithm for the dynamic berth allocation problem.•Two sets of benchmark instances were tested and results were compared with other leading algorithms.•Our PSO is able to obtain the optimal solutions in shorter computational time than other methods. The berth allocation is one of the major container port optimization problems. In both port operator’s and ocean carriers’ perspective, the minimization of the time a ship at the berth may be considered as an objective with respect to port operations. This paper focuses on the discrete and dynamic berth allocation problem (BAP), which assigns ships to discrete berth positions and minimizes the total waiting times and handling times for all ships. We formulate a mixed integer programming (MIP) model for the BAP. Since BAP is a NP-hard problem, exact solution approaches cannot solve the instances of realistic size optimally within reasonable time. We propose a particle swarm optimization (PSO) approach to solve the BAP. The proposed PSO is tested with two sets of benchmark instances in different sizes from the literature. Experimental results show that the PSO algorithm is better than the other compared algorithms in terms of solution quality and computation time.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2013.08.051