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A Hybrid Metaheuristic Algorithm for the Quay Crane Scheduling Problem
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Author(s): Laura Cruz-Reyes (National Mexican Institute of Technology, Mexico), Arturo Lam Alvarez (National Mexican Institute of Technology, Mexico), Marcela Quiroz-Castellanos (National Mexican Institute of Technology, Mexico), Claudia Gómez (National Mexican Institute of Technology, Mexico), Nelson Rangel Valdez (National Mexican Institute of Technology, Mexico), Guadalupe Castilla Valdez (National Mexican Institute of Technology, Mexico)and Javier Gonzalez Barbosa (National Mexican Institute of Technology, Mexico)
Copyright: 2016
Pages: 19
Source title:
Handbook of Research on Military, Aeronautical, and Maritime Logistics and Operations
Source Author(s)/Editor(s): Alberto Ochoa-Zezzatti (Juarez City University, Mexico), Jöns Sánchez (Consejo Nacional De Ciencie Y Tecnologia (CONACYT), Mexico), Miguel Gastón Cedillo-Campos (Transportation Systems and Logistics National Laboratory, Mexican Institute of Transportation, Mexico)and Margain de Lourdes (Polytechnic University of Aguascalientes, Mexico)
DOI: 10.4018/978-1-4666-9779-9.ch012
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Abstract
This chapter presents a novel hybrid algorithm for the quay crane scheduling problem (QCSP). QCSP consists of scheduling a sequence of unloading and loading movements for cranes assigned to a vessel, minimizing the total completion time of all the tasks. The proposed algorithm integrates two well-known metaheuristics: Greedy Randomized Adaptive Search Procedure and Ant Colony System; it also incorporates a repositioning strategy of idle cranes to reduce the interference generated by the quay cranes. The experimental results show that the proposed method is able to find quality solutions in short times. In experiments reported in the literature with crafted instances of QCSP, heuristic running time varies from seconds in small instances to hours in instances of medium size. Currently, the industry requirements are up to a maximum of approximately five minutes. The hybrid algorithm presented in this chapter allows addressing these requirements, by finding good quality solutions in significantly shorter time for larger problems, which represents an advantage in real environments.
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