OPTIMIZATION OF TERMINAL SERVICEABILITY BASED ON CHAOTIC GA-BASED METHOD

被引:1
|
作者
Wu, C. H. [1 ]
Leung, Polly P. L. [2 ]
Dong, N. [3 ]
Ho, G. T. S. [1 ]
Kwong, C. K. [2 ]
Ip, W. H. [2 ,4 ]
机构
[1] Hang Seng Univ Hong Kong, Dept Supply Chain & Informat Management, Hong Kong, Peoples R China
[2] Hong Kong Polytech Univ, Dept Ind & Syst Engn, Hong Kong, Peoples R China
[3] Tianjin Univ, Sch Elect Engn & Informat Engn, Tianjin 300072, Peoples R China
[4] Univ Saskatchewan, Dept Mech Engn, Saskatoon, SK, Canada
关键词
Chaotic Genetic Algorithms (CGA); berth allocation; service priority; terminal serviceability; BERTH ALLOCATION PROBLEM; DECISION-SUPPORT-SYSTEM; CONTAINER TERMINALS; GENETIC ALGORITHM; OPERATIONS-RESEARCH; SCHEDULING PROBLEM; TABU SEARCH; CLASSIFICATION; SVR;
D O I
10.22452/mjcs.vol32no1.5
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Minimizing cargo handling time and waiting time of ship are some of the most critical tasks for terminal operators during berth allocation planning. An efficient and effective berth allocation planning approach is not only significant for improving a terminal's productivity but worth even more in enhancing terminal serviceability. As berths are no longer leased by specific ship lines or ship companies in the majority of terminals, ships of various sizes and various cargo handling volume at a particular terminal of call are competing for the same berth for handling. As a result, there are always concerns from both terminal operators and ship companies regarding the service priority. This research contributes to deal with the dilemma terminal operators encountered in balancing service priority and terminal productivity maximization during berth allocation. This research deals with berth allocation problem which treats calling ships at various service priorities with physical constraints. The problem encountered is to determine how to cope with various ships with various attributes in the system, and the objective is to minimize the total service time of a set of given calling ships through proper berth allocation. This research adopts a chaotic genetic algorithm-based method to deal with the problem. The new formulation and method have been proposed and results obtained have been compared with the existing one in literature. The results show the improved feasibility of the proposed formulation and improved convergence speed of the proposed method over the existing one. Also, higher terminal serviceability is indicated.
引用
收藏
页码:62 / 82
页数:21
相关论文
共 50 条
  • [41] A GA-based household scheduler
    Konrad Meister
    Martin Frick
    Kay W. Axhausen
    Transportation, 2005, 32 : 473 - 494
  • [42] GA2RM: A GA-Based Action Rule Mining Method
    Hashemi, Shervin
    Shamsinejad, Pirooz
    INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS, 2021, 20 (02)
  • [43] GA-based learning in behaviour based robotics
    Gu, DB
    Hu, HS
    Reynolds, J
    Tsang, E
    2003 IEEE INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN ROBOTICS AND AUTOMATION, VOLS I-III, PROCEEDINGS, 2003, : 1521 - 1526
  • [44] A HYBRID METHOD FOR INTRUSION DETECTION WITH GA-BASED FEATURE SELECTION
    Chen, Zh-Xian
    Huang, Hao
    INTELLIGENT AUTOMATION AND SOFT COMPUTING, 2011, 17 (02): : 175 - 186
  • [45] GA-Based Robustness Evaluation Method for Digital Image Watermarking
    Boato, G.
    Conotter, V.
    De Natale, F. G. B.
    DIGITAL WATERMARKING, PROCEEDINGS, 2008, 5041 : 294 - 307
  • [46] Fuzzy system design by a GA-based method for data classification
    Wong, CC
    Chen, CC
    CYBERNETICS AND SYSTEMS, 2002, 33 (03) : 253 - 270
  • [47] Implementation and evaluation for GA-based pipe route planning method
    Ito, T
    SIMULATION IN INDUSTRY 2001, 2001, : 462 - 466
  • [48] Study on GA-based matching method of railway vehicle wheels
    School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, China
    J. Chem. Pharm. Res., 4 (536-542):
  • [49] Improved GA-based method for multiple protein sequence alignment
    Nguyen, HD
    Yoshihara, I
    Yamamori, K
    Yasunaga, M
    CEC: 2003 CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-4, PROCEEDINGS, 2003, : 1826 - 1832
  • [50] Humanoid walking gait optimization using GA-based neural network
    Tang, Z
    Zhou, CJ
    Sun, ZQ
    ADVANCES IN NATURAL COMPUTATION, PT 2, PROCEEDINGS, 2005, 3611 : 252 - 261