Genetic Algorithm for Scheduling Yard Cranes in Port Container Terminals
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Genetic Algorithm for Scheduling Yard Cranes in Port Container Terminals

Genetic Algorithm for Scheduling Yard Cranes in Port Container Terminals


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This dissertation, "Genetic Algorithm for Scheduling Yard Cranes in Port Container Terminals" by Wan-sze, Tsang, 曾韻詩, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Abstract of thesis entitled Genetic Algorithm for Scheduling Yard Cranes in Port Container Terminals Submitted by Tsang Wan Sze For the Degree of Master of Philosophy at the University of Hong Kong in August 2003 As speed is of paramount importance in today's marine transportation industry, port container terminals are under tremendous pressure to improve their operational efficiency. Managing the operations of a port container terminal is a challenging task, requiring the routine solution of a series of difficult operational planning problems. Yard operation is generally considered to be at terminal operations, and the efficiency of yard operations determines the overall quality of a terminal's operations. To achieve a smooth flow of containers in a yard, yard cranes need to be positioned at the right location at the right time to pick up containers from or load containers onto trucks. Currently, human judgment is used to schedule yard cranes and to determine the timing of yard crane jobs and yard crane coverage. Since such a scheduling problem need to be solved frequently and quickly, the current manual decision making process is error-prone. Hence, a more effective scheduling algorithm is needed. This study considers the Yard Crane Scheduling Problem - the problem of scheduling a yard crane to perform a given set of container handling jobs with different ready times with the objective of minimizing the sum of job waiting times. An integer program is built to model the problem. It is shown that the problem is NP-complete. In view of the complexity of the problem, some of its properties are identified and an appropriate algorithm is developed to find a lower bound for the optimal solution of the problem. A numerical example is used to illustrate how the algorithm works. As the Yard Crane Scheduling Problem is NP-complete, a solution algorithm is needed to search for an effective schedule for the problem. Since genetic algorithm (GA) is superior to other heuristics due to their inherent features of global search and convergence, GA is used in this research. Each of the key features of the proposed GA (including representation, population, fitness, selection, crossover and mutation) is discussed in detailed. As crossover plays an important role in the performance of a GA, one of the main thrusts of this study is to identify an effective crossover scheme for the Yard Crane Scheduling Problem. All possible combinations for the five common crossover schemes reported in GA literature are considered, and different combinations of the crossover schemes are constructed based on the different proportions of parents to be crossovered. The performance of all the combined crossover schemes is evaluated by a comprehensive set of randomly-generated test problems based on realistic yard operations data. The computational results show that the best combined crossover scheme outperforms the five common crossover schemes. This study focuses on only one key component of yard operations. It is recommended that further studies can be carried out to consider scheduling of multiple yard cranes working in the same yard block, and the impact of container reshuffles on yard crane scheduling. W.S. TSANG (473 words) DOI: 10.5353/th_b2954460 Subjects: Cargo handling - Mathematical


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Product Details
  • ISBN-13: 9781374717206
  • Publisher: Open Dissertation Press
  • Publisher Imprint: Open Dissertation Press
  • Height: 279 mm
  • No of Pages: 108
  • Weight: 268 gr
  • ISBN-10: 1374717207
  • Publisher Date: 27 Jan 2017
  • Binding: Paperback
  • Language: English
  • Spine Width: 6 mm
  • Width: 216 mm


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