Scheduling Trucks in Port Container Terminals by a Genetic Algorithm
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Scheduling Trucks in Port Container Terminals by a Genetic Algorithm

Scheduling Trucks in Port Container Terminals by a Genetic Algorithm


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About the Book

This dissertation, "Scheduling Trucks in Port Container Terminals by a Genetic Algorithm" by Yuxuan, Zhang, 張宇軒, 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 Scheduling Trucks in Port Container Terminals by a Genetic Algorithm Submitted by Zhang Yuxuan For the Degree of Master of Philosophy at the University of Hong Kong in August 2005 The recent trend of trade globalization has significantly increased the importance of the global marine transportation industry. As operation efficiency is of paramount importance to today's marine transportation industry, port container terminals have been working hard to improve their operation performance. In most port container terminals, trucks are frequently used as the transportation equipment to ferry containers between huge container handling equipment within a terminal. Thus, trucks are important equipment linking isolated operations in various terminal locations together to facilitate a smooth flow of containers from the yard to the quay and vice versa. Efficient truck operation can speed up container handling processes, smooth container flows and thus improve a terminal's performance. This research studies the Truck Scheduling Problem - the problem of scheduling a fleet of trucks to perform a set of transportation jobs with sequence-dependent processing times and different ready times to minimize the makespan of all the jobs. The scheduling problem is formulated as a mixed-integer program. It is noted that the scheduling problem is NP-hard and the computational effort for solving even small-scale test problems is prohibitively large. This research proposes to use a Genetic algorithm (GA) to solve the scheduling problem as GAs are often superior to other heuristic algorithms, due to GAs' unique features of global search and convergence. Major components of the GA proposed in this research, such as representation, initialization, fitness evaluation, selection, crossover and mutation, are discussed in detail. Improvements are made to some of the components of a standard GA to enhance its search ability. A heuristic initialization scheme is proposed to generate an initial population with better fitness. As the crossover operator plays an important role in a GA's performance and is regarded as the primary engine of exploitation in GAs, this research develops an effective crossover scheme, called Greedy Crossover, based on the characteristics of the scheduling problem. A comprehensive set of test problems randomly generated based on real-life terminal operations data is used to compare the performance of the proposed GA using Greedy Crossover with that of the GAs using six effective crossover schemes reported in the literature. The computational results demonstrate that Greedy Crossover outperforms the six crossover schemes, with Greedy Crossover's solutions on average 4.05% better than the best solutions found by the six crossover schemes. Zhang Yuxuan (380 words) DOI: 10.5353/th_b3532703 Subjects: Trucks - Routes - Mathematical models Container terminals - Mathematical models Scheduling - Mathematical models Genetic algorithms


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Product Details
  • ISBN-13: 9781361093887
  • Publisher: Open Dissertation Press
  • Publisher Imprint: Open Dissertation Press
  • Height: 279 mm
  • No of Pages: 126
  • Weight: 590 gr
  • ISBN-10: 1361093889
  • Publisher Date: 26 Jan 2017
  • Binding: Hardback
  • Language: English
  • Spine Width: 10 mm
  • Width: 216 mm


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