Research on Operation Optimization Problem of Material Handling System for Large Ship

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Abstract:

Material supply isthe basic guarantee for the large ship sailing to ocean. In order to improve the efficiency of the supply of the large ship, the support of materials handling system is needed. According to the typical material handling system of the large ship, with the material handlings time shortest as the decision goal and the handling sequence as the decision variable, the analysis and construction of the model for optimizing the systems operation is given in this paper. The improved genetic algorithm is put forward. The specific model is also given and its proved that the analysis and modeling method in this paper can effectively improve the efficiency of the material handling of the large ships.

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3740-3744

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August 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] YongJun Huang, Youxin Wu, and Huabin Liu, TheImproved Genetic Algorithm is Presented to Solve the TSP Problem[J], Computer Engineering and Design, 2007, 28(24): 5909-5911.

Google Scholar

[2] Vui Ann Shim, Kay Chen Tan, and Chia Jun Yong, Evolutionary Algorithms for Solving Multi-objective Travelling Salesman Problem[J], FLEXIBLE SERVICES AND MANUFACTURING JOURNAL, 2011, 23(2): 207-241.

DOI: 10.1007/s10696-011-9099-y

Google Scholar

[3] Jin Hao, Libao Shi, and JIaqi Zhou, The Random Disturbance Ant Colony Algorithm to Solve the Complex TSP Problem[J], System Engineering Theory and Practice, 2002, 22(9): 88-91.

Google Scholar

[4] Gaifang Dong, William W. Guo, and Kevin Tickle, Solving the Traveling Salesman Problem Using Cooperative Genetic Ant Systems[J], Expert System with Applications, 2012, 39(5): 5006-5011.

DOI: 10.1016/j.eswa.2011.10.012

Google Scholar

[5] Weibo Yang and Yanwei Zhao, The Improved Simulated Annealing Algorithm to Solve the TSPProblem[J], Computer Engineering and Application, 2010, 46(15): 34-36.

Google Scholar

[6] Shakouri. GH, Shojaee. K, and Behnam. TM, Investigation on the Choice of the Temperature in the Simulation Annealing: A Mushy State SA for TSP[C], 17th Mediterranean Conference on Control and Automation, Thessaloniki, GREECE, 2009(1-3): 1050-1055.

DOI: 10.1109/med.2009.5164685

Google Scholar

[7] Ping Li, LeifuGao, and Xuwang Liu, An Algorithm Based on Simulated Annealing and Hopfield neural Network to Solve the TSP Problem[J], Science and Technology and Engineering, 2008, 8(14): 3937-3939.

Google Scholar

[8] S.M. Johnson, Optimal Two and Three –stage Production Schedules With Setup Times Included[J], Naval Research Logistics Quarterly, 1954, 1(1): 61-68.

DOI: 10.1002/nav.3800010110

Google Scholar