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Solving the Quay Crane Scheduling Problem (QCSP) using the Particle Swarm Optimization algorithm (PSO)

Dimakis Georgios-Rafail

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Summary

This thesis has as its object of study one of the most important problems in the world of modern shipping. In the quay crane scheduling problem (QCSP), the goal is to create a crane schedule, which defines a start time for each job on a crane. Initially, the consideration of this problem is based on the technology of quay cranes. Generally, a platform crane system consists of multiple cranes placed on rails parallel to the dock. From the perspective of solving, the main constraints for each crane include maintaining the safety distance and the different activation times of the cranes. Accordingly, each docked vessel for service has a series of container transshipment tasks where each task is differentiated in execution time and location. In other words, we aim for an optimal task matching sequence with cranes by exploiting the above features to form a task schedule that minimizes their processing time. To achieve the above, the Particle Swarm algorithm (PSO) is used, which belongs to the category of swarm intelligence algorithms. The theory behind this algorithm is based on the definition of a solution as a particle in a space. Additionally, the mechanism of the algorithm is based on creating a swarm of particles (solutions) in a space (value space - solution space) and then moving our particle (initial solution) using the positions of the other particles in relation to the velocity and displacement equations to reach a better position (optimal solution).

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