港口拖轮调度优化及其仿真研究
|
摘要
在港口物流系统中,拖轮作业系统是船舶在港内的“第一服务站”。研究如何对拖轮资源进行合理地调度,以节省拖轮作业前后的无效作业时间,提高港口营运效率,减少资源消耗,进而降低船舶在港停留时间,是保证港口正常的生产作业、提高港口竞争力需要解决的重要问题。围绕上述问题,本文主要做了以下工作：
(1)基于混合流水线调度理论,构建了考虑多停泊基地的两阶段拖轮调度模型。针对模型特点,设计了混合模拟退火算法(HSA)对其进行求解。数值例实验结果显示,HSA算法的求解结果优于单纯SA算法、现行调度规则的求解结果。另外,通过对不同作业模式下最优方案时的拖轮总作业时间进行比较分析,总结出合理选择拖轮作业模式的经验规律。
(2)通过运用所设计的MHSA算法对拖轮在计划期内是否中途返回停泊基地的总作业时间及利用率进行比较,为拖轮在不同作业任务之间是否中途返回基地提供决策支持,可以大幅度地减少燃油消耗,提高拖轮资源的利用率。
(3)对于考虑移泊作业的三阶段调度模型,在验证模型和所设计的混合模拟退火算法(MHSA2)的有效性的基础之上,通过对不同参数的灵敏度分析显示：到港船舶类型所占比例和移泊作业所占比例对拖轮总作业时间的影响最大,总作业时间对拖轮配备情况反应出极弱敏感性,而对装卸作业时间分布不敏感。
(4)基于所设计的仿真流程,运用Arena软件对拖轮助泊作业系统进行仿真实现。仿真运行的结果显示：在所有到港船舶中,数量较多的船型、大型船的平均在港时间及等待拖轮作业时间要明显长于其他船型。另外,仿真系统与运用所设计算法下拖轮利用率的比较说明：通过运用本文所设计的算法进行调度,在同样的资源配备条件下,可以为更多的到港船舶提供助泊服务。
The tugboat operation system is'the first service station' in the whole port logistics system. Studying how to make feasible scheduling plans for tugboats, is an important topic which can greatly improve the port operation efficiency, reduce the fuel consumption, and improve the port competitiveness. The main work in this dissertation about the above problems is as follows:
(1) A2-stage tugboat scheduling model considering multi-anchorage bases was formulated. The hybrid simulated annealing algorithm (HSA) was proposed to solve the problem. Numerical experiments reveal that the HSA resolved results are better than those from the simple SA and existing scheduling rules, which prove the effieciency of the proposed algorithm. By comparisons between the total operation times under different operation modes, the empiristic rules for rational selection on operation modes were concluded.
(2) By comparisons between the total operation times and utilization of tugboats on whether tugboats return to the anchorage bases during the planning horizon using the proposed MHSA, decision support can be made on whether tugboats should return to the anchorage bases after finishing each operation, thus the fuel consumption can be reduced and the utilization of tugboats can be improved.
(3) After testifying the efficiency of the formulated3-stage model considering the shifting-berth operation and the proposed MHSA2algorithm, the dissertation pointed out by the sensitivity analysis on different parameters, that the total operation time of tugboats is most sensitive to the proportion of the shifting-berth operation and ship style, and influenced slightly by the tugboat deployment scheme, and not sensitive to the handling times.
(4) The simulation of the tugboat operation system was realized with the Arena software based on the designed simulation procedure. Results of the simulation run can be concuded as follows:the turnaround and waiting times of ships with more quantities, larger scales are obviously larger than those of normal ships. Besides, comparisons between the tugboat utilization from the simulation system and proposed algorithm point out:by using the proposed algorithm to make the scheduling plans for tugboats, more ships can be served under the same tugboat resources compared with the existing operation system.
(1)基于混合流水线调度理论,构建了考虑多停泊基地的两阶段拖轮调度模型。针对模型特点,设计了混合模拟退火算法(HSA)对其进行求解。数值例实验结果显示,HSA算法的求解结果优于单纯SA算法、现行调度规则的求解结果。另外,通过对不同作业模式下最优方案时的拖轮总作业时间进行比较分析,总结出合理选择拖轮作业模式的经验规律。
(2)通过运用所设计的MHSA算法对拖轮在计划期内是否中途返回停泊基地的总作业时间及利用率进行比较,为拖轮在不同作业任务之间是否中途返回基地提供决策支持,可以大幅度地减少燃油消耗,提高拖轮资源的利用率。
(3)对于考虑移泊作业的三阶段调度模型,在验证模型和所设计的混合模拟退火算法(MHSA2)的有效性的基础之上,通过对不同参数的灵敏度分析显示：到港船舶类型所占比例和移泊作业所占比例对拖轮总作业时间的影响最大,总作业时间对拖轮配备情况反应出极弱敏感性,而对装卸作业时间分布不敏感。
(4)基于所设计的仿真流程,运用Arena软件对拖轮助泊作业系统进行仿真实现。仿真运行的结果显示：在所有到港船舶中,数量较多的船型、大型船的平均在港时间及等待拖轮作业时间要明显长于其他船型。另外,仿真系统与运用所设计算法下拖轮利用率的比较说明：通过运用本文所设计的算法进行调度,在同样的资源配备条件下,可以为更多的到港船舶提供助泊服务。
The tugboat operation system is'the first service station' in the whole port logistics system. Studying how to make feasible scheduling plans for tugboats, is an important topic which can greatly improve the port operation efficiency, reduce the fuel consumption, and improve the port competitiveness. The main work in this dissertation about the above problems is as follows:
(1) A2-stage tugboat scheduling model considering multi-anchorage bases was formulated. The hybrid simulated annealing algorithm (HSA) was proposed to solve the problem. Numerical experiments reveal that the HSA resolved results are better than those from the simple SA and existing scheduling rules, which prove the effieciency of the proposed algorithm. By comparisons between the total operation times under different operation modes, the empiristic rules for rational selection on operation modes were concluded.
(2) By comparisons between the total operation times and utilization of tugboats on whether tugboats return to the anchorage bases during the planning horizon using the proposed MHSA, decision support can be made on whether tugboats should return to the anchorage bases after finishing each operation, thus the fuel consumption can be reduced and the utilization of tugboats can be improved.
(3) After testifying the efficiency of the formulated3-stage model considering the shifting-berth operation and the proposed MHSA2algorithm, the dissertation pointed out by the sensitivity analysis on different parameters, that the total operation time of tugboats is most sensitive to the proportion of the shifting-berth operation and ship style, and influenced slightly by the tugboat deployment scheme, and not sensitive to the handling times.
(4) The simulation of the tugboat operation system was realized with the Arena software based on the designed simulation procedure. Results of the simulation run can be concuded as follows:the turnaround and waiting times of ships with more quantities, larger scales are obviously larger than those of normal ships. Besides, comparisons between the tugboat utilization from the simulation system and proposed algorithm point out:by using the proposed algorithm to make the scheduling plans for tugboats, more ships can be served under the same tugboat resources compared with the existing operation system.
引文
[1]靳志宏.物流调度与协调.北京：中国物资出版社,2008.
[2]靳志宏,关志民.运营管理.北京：机械工业出版社,2007.
[3]Michael Pinedo著.调度：原理、算法和系统.北京：清华大学出版社,2007.
[4]Ruiz Ruben, Maroto Concepcion. A comprehensive review and evaluation of permutation flowshop heuristics. European Journal of Operational Research.2005,165(2):479-494.
[5]李娜.集装箱码头连续泊位与岸桥调度联合优化研究：(博士学位论文).大连：大连海事大学,2012.
[6]屈国强.求解流水车间调度问题的瓶颈指向启发式算法.计算机集成制造系统.2012,18(2)：356-363.
[7]贾兆红,朱建建,陈华平.柔性作业车间调度的动态禁忌粒子群优化算法.华南理工大学学报(自然科学版).2012,40(1)：69-76.
[8]Johnson S. M.. Optimal two- and three-stage production schedules with setup times included. Naval Research Logistics Quarterly.1954,1(1):61-68.
[9]Thornton H. W., Hunsucke J. L.. A new heuristic for minimal makespan in flow shops with multiple processors and no intermediate storage. European Journal of Operational Research.2004,152(1):96-114.
[10]Jackson J. R.. An extension of Johnson's results on job lot scheduling. Naval Research Logistics Quarterly.1956,5(3):201-203.
[11]刘志雄.调度问题中的粒子群优化方法及其应用研究：(博士学位论文).武汉：武汉理工大学,2005.
[12]Low Chinyao. Simulated annealing heauristic for flow shop scheduling problems with unrelated parallel machines. Computers and Operations Research.2005,32:2013-2025.
[13]Jin Zhihong, Yang Zan, Ito Takahiro. Metaheuristic algorithms for the multistage hybrid flowshop scheduling problem. International Journal of Production Economics.2006,100(2):322-334.
[14]Koulamas Christos, Kyparisis George J.. Concurrent flowshop scheduling to minimize makespan. European Journal of Operational Research.2004,156(2):524-529.
[15]Vallada Eva, Ruiz Ruben, Minella Gerardo. Minimising total tardiness in the m-machine flowshop problem:A review and evaluation of heuristics and metaheuristics. Computers & Operations Research.2008,35(4):1350-1373.
[16]王凌,周刚,许烨,等.混合流水线调度研究进展.化工自动化及仪表.2011,38(1)：1-8.
[17]Li Der-Chiang, et al. Two-machine flowshop scheduling with truncated learning to minimize the total completion time. Computers & Industrial Engineering.2011,61(3):655-662.
[18]Kis Tamas, Pesch Erwin. A review of exact solution methods for the non-preemptive multiprocessor flowshop problem. European Journal of Operational Research.2005,164(3):592-608.
[19]Woo Hoon Shik, Yim Dong Soon. A heuristic algorithm for mean flowtime objective in flowshop scheduling. Computers Operations Research.1998,25(3):175-182.
[20]肖建华.多处理器任务调度问题的新近似算法.计算机工程.2005,31(24)：50-52.
[21]轩华,唐立新.带多处理器任务的动态混合流水车间调度问题.计算机集成制造系统.2007,13(11)：2254-2260.
[22]Pan Quan-Ke, Wang Ling. Effective heuristics for the blocking flowshop scheduling problem with makespan minimization. Omega.2012,40(2):218-229.
[23]Costa Wagner Emanoel, Goldbarg Marco Cesar, Goldbarg Elizabeth G.. New VNS heuristic for total flowtime flowshop scheduling problem. Expert Systems with Applications.2012,39(9):8149-8161.
[24]Xiao W., Hao P., Zhang S.. Hybrid Flow Shop Scheduling Using Genetic Algorithms. Proc of the 3rd World Congress on Intelligent Control and Automation.2000:537-541.
[25]Arroyo Jose Elias Claudio, Armentano Vinicius Amaral. Genetic local search for multi-objective flowshop scheduling problems. European Journal of Operational Research.2005,167(3):717-738.
[26]杨美荣,崔曼曼,史建锋,等.遗传算法在多品种小批量模式MPS中的应用(优先出版).计算机工程与应用.2012.
[27]伍麟珺.基于改进遗传算法的天线方向图研究.广西轻工业.2012,(1)：59-60.
[28]秦毅,侯强,刘志霞.基于遗传算法的多模式资源受限项目调度问题研究(优先出版).辽宁工程技术大学学报(社会科学版).2012.
[29]刘爱军,杨育,邢青松,等.复杂制造环境下的改进非支配排序遗传算法(优先出版).计算机集成制造系统.2012.
[30]Naderi B., et al. An improved simulated annealing for hybrid flowshops with sequence-dependent setup and transportation times to minimize total completion time and total tardiness. Expert Systems with Applications.2009,36(6):9625-9633.
[31]路飞,田国会,姜健,等.用基于模拟退火机制的多种群并行遗传算法解Job-Shop调度问题.山东工业大学学报.2001,(4)：361-364.
[32]谭伟,赵蔓,李向.基于模拟退火遗传算法的多项目调度问题研究.微计算机信息.2009,(9)：220-222.
[33]史烨,李凯.并行机问题的模拟退火调度算法研究.运筹与管理.2011,(4)：104-107.
[34]Wang Xianpeng, Tang Lixin. A tabu search heuristic for the hybrid flowshop scheduling with finite intermediate buffers. Computers & Operations Research.2009,36(3):907-918.
[35]Eksioglu Burak, Eksioglu Sandra Duni, Jain Pramod. A tabu search algorithm for the flowshop scheduling problem with changing neighborhoods. Computers & Industrial Engineering.2008,54(1): 1-11.
[36]Liao Li-Man, Huang Ching-Jen. Tabu search for non-permutation flowshop scheduling problem with minimizing total tardiness. Applied Mathematics and Computation.2010,217(2):557-567.
[37]Ying Kuo Ching, Lin Shih Wei. Multiprocessor task scheduling in multistage hybrid flow-shops:An ant colony system approach. International Journal of Production Research.2006,44(16):3161-3177.
[38]何小虎.优化蚁群算法在排课中的应用策略.计算机与数字工程.2012,(7)：33-34.
[39]孙晓莹,徐红霞.基于蚁群算法的煤矿运输车辆调度应用研究.煤炭技术.2012,(7)：140-142.
[40]张向丰,董淑娟.蚁群算法改进及其实现.电脑开发与应用.2012,(7)：14-15.
[41]祝文康,钟育彬.基于改进蚁群算法的物流车辆调度问题研究.江南大学学报(自然科学版).2012,(3)：273-276.
[42]Wang Xianpeng, Tang Lixin. A discrete particle swarm optimization algorithm with self-adaptive diversity control for the permutation flowshop problem with blocking. Applied Soft Computing.2012, 12(2):652-662.
[43]Jarboui Bassem, et al. A combinatorial particle swarm optimisation for solving permutation flowshop problems. Computers & Industrial Engineering.2008,54(3):526-538.
[44]童言,张焕国.利用基于粒子群优化和模拟退火的混合策略优化正形置换(英文).中国通信.2012,(1)：49-57.
[45]Sugimoto M., Kobayashi H., Ishibashi A.. The development of the support system for tugs operation. Marine and Maritime.2001, Marine Technology Ⅳ:325-334.
[46]Van Phuoc-Bui, et al. Modeling and control allocation for ship berthing system design. ICCAS.2010: 195-200.
[47]潘永良,朱泓.对超大型船舶实施护航拖轮监护的探讨.港口科技.2010,(11)：36-40.
[48]李柏然.港作拖轮的性能要求和发展趋势.物流技术.2010,(8)：136-145.
[49]吴中强.全回转港作拖轮推进装置特点及推拖模式分析.中国水运.2011,11(1)：98-100.
[50]Yang Lin-Jia, et al. Experimental study on braking force characteristics of tugboat. Journal of Hydrodynamics.2010,22(5):332-337.
[51]张鹏.拖轮协助船舶操纵建模与仿真：(硕士学位论文).大连：大连海事大学,2008.
[52]李亮等.拖轮协助大船数学模型在MATLAB上的实现.中国水运.2008,8(7)：21-23.
[53]刘谊信.关于港口拖轮主要设备配件的管理探讨.科技创新导报.2009,(9)：38.
[54]熊振南,翁跃宗,张寿桂.超大型船舶靠离泊操纵中拖轮助操的应用.集美大学学报(自然科学版).2009,14(3)：260-264.
[55]郭万庶.航海模拟器中拖轮拖带作业数学模型的研究：(硕士学位论文).大连：大连海事大学,2010.
[56]刘斌.拖轮选型优化研究：(硕士学位论文).大连：大连海事大学,2011.
[57]蒲子芳.航海模拟器中拖轮顶推作业数学模型的研究：(硕士学位论文).大连：大连海事大学,2011.
[58]张琳.浅谈拖轮节能降耗的途径.交通财会.2011,(8)：66-70.
[59]翟俊升,李德源,刘巍.港作拖轮经济使用年限的确定.大连海运学院学报.1992,18(3)：309-314.
[60]刘志雄,王少梅.带特殊工艺约束的双目标并行多机调度问题研究.计算机集成制造系统.2005,11(11)：1616-1620.
[61]刘志雄,王少梅.基于粒子群算法的并行多机调度问题研究.计算机集成制造系统.2006,12(2)：183-187.
[62]Wang Su, Bo Meng. Resource allocation and scheduling problem based on genetic algorithm and ant colony optimization.2007,4426:879-886.
[63]何涛,朱宏辉.遗传算法在拖轮调度中的应用.物流技术.2008,27(4)：138-139.
[64]刘光.基于多目标粒子群算法的港口调度系统设计与实现：(硕士学位论文).哈尔滨：哈尔滨工程大学,2008.
[65]Liu Zhixiong. Hybrid Evolutionary Strategy Optimization for Port Tugboat Operation Scheduling. Third Internation Symposium on Intelligent Information Technology Application.2009: 511-515.
[66]He Tao, Ye Wenwen. Hybrid Evolutionary Strategy Optimization for Port Tugboat Operation Scheduling.2nd International Conference on Information Science and Engineering.2010:1438-1441.
[67]Wang Su, et al. An Improved Discrete Particle Swarm Optimization for Tugboaat Scheduling Problem in Container Terminal. International Conference on Information Management, Innovation Management and Industrial Engineering.2010:593-596.
[68]刘志雄.考虑最短距离作业的港口拖轮作业调度优化.西南交通大学学报.2011,46(5)：875-881.
[69]Wang Su, Kaku Iko, Chen Guoyue. Research on the Modeling of Tugboat Assignment Problem in Container Terminal. Advanced Materials Research.2012,433-440:1957-1961.
[70]董良才,徐子奇,宓为建.基于遗传算子粒子群算法的拖轮动态调度.数学的实践与认识.2012,42(6)：122-133.
[71]Yu Wenhui. Heuristics Algorithm for Simulation and Optimization System of Port Tugboats Allocation. International Conference on Internet Computing and Information Services, ICICIS.2011: 306-309.
[72]熊军魁.港口拖轮优化配置研究：(硕士学位论文).武汉：武汉理工大学,2002.
[73]熊军魁.港口生产与拖轮配置问题探讨.船海工程.2003,(1)：45-46.
[74]潘文军,王少梅.基于神经网络的聚类分析方法及在港口设备配置中的应用.交通与计算机.2003,21(5)：44-47.
[75]陈伟.仿真优化方法及其在港口设备配置中的应用研究：(硕士学位论文).武汉：武汉理工大学,2002.
[76]陈伟,沈斌,王少梅.启发式仿真优化方法在设备配置中的应用研究.系统仿真学报.2005,17(9)：2280-2283.
[77]刘志雄,王少梅.港口拖轮作业的计算机仿真研究.系统仿真学报.2004,16(1)：45-47.
[78]Zhixiong Liu, Shaomei Wang. Operation Resource Allocation Optimization of Discrete Event System Based on Simulation. Journal of Wuhan University of Technology.2005,29(2):327-330.
[79]杨志华.港口拖轮优化配置研究.系统仿真技术.2012,8(2)：169-174.
[80]靳志宏,计明军.物流实用优化技术.北京：中国物资出版社,2008.
[81]Metropolis N., et al. Equation of state calculations by fast Computing mathines. Journal of Chemical Physics.1953,21:1087-1092.
[82]田澎,王浣尘,张冬茉.旅行商问题(TSP)的模拟退火求解.上海交通大学学报.1995,30(S1)：111-116.
[83]庞哈利,郑秉霖,徐心和.一种自适应的模拟退火算法.控制与决策.1999,14(5)：477-480.
[84]靳利霞.蛋白质结构预测方法：(博士学位论文).大连：大连理工大学,2002.
[85]计明军.若干随机性全局优化算法的研究：(博士学位论文).大连：大连理工大学,2004.
[86]李红芳,侯朝胜.模拟退火遗传算法与结构优化设计.天津理工学院学报.2004,(4)：96-98.
[87]康立山,谢云,尤矢勇等.非数值并行算法——模拟退火算法.北京：科学出版社,1994.
[88]邢文训,谢金星.现代优化计算方法.北京：清华大学出版社,1999.
[89]张晓萍,石伟,刘玉坤.物流系统仿真.北京：清华大学出版社,2011.
[90]卫军胡,韩九强,孙国基.离散事件系统仿真技术在制造系统调度中的应用.系统仿真学报.2000, 12(1)：27-30.
[91]Lacksonen T.. Empirical comparison of search algorithms for discrete event simulation. Computers& Industrial Engineering.2001, (40):133-148.
[92]Law A. M., Kelton W. D.. Simulation Modeling and Analysis (3rd edition). Beijing:Tsinghua University Press,2000.
[93]程光,邬洪迈,陈永刚.工业工程与系统仿真.北京：冶金工业出版社,2007.
[94]刘玉坤,石伟,张晓萍等.描述离散动态系统的集合标识Petri网：STPNs.系统工程理论与实践.2004,(7)：59-65.
[95]刘玉坤.装配型供应链库存系统建模与分析：(博士学位论文).北京：清华大学,2004.
[96]朱顺应,朱凯,王丽铮.基于Arena的三峡枢纽运输系统仿真模型.水运工程.2011,450(2)：40-44.
[97]杨磊.Y医药物流中心作业系统Arena仿真优化应用研究：(硕士学位论文).西安：长安大学,2009.
[98]李立,王晓东,邱梦,等.基于Arena的集装箱码头内部道路运输系统仿真研究.河北工业大学学报.2010,39(2)：64-68.
[99]袁子文,张培林,董升平.基于Arena的武钢工业港原料码头物流系统仿真研究.交通科技.2009,(4)：151-153.
[100]方绍强,卫克,陈伟鹏,等.飞行保障过程UML建模与ARENA仿真.系统工程理论与实践.2008,(11)：165-170.
[101]屈乾沁,关晓兰,刘志萍.基于Arena的电子商务物流配送体系的建模与仿真研究.物流技术.2010,29(1)：90-92.
[102]郑耿灶,苏平,豆之敬.基于仿真优化方法的混合装配线平衡.工业工程.2010,13(3)：121-125.
[103]马文强,杜子平,李东坡.仿真优化在制造系统生产调度中的研究进展.现代制造工程.2012,(3)：10-14.
[104]Kelton W. David, Sadowski Randall P., T. Sturrock David. Simulation with Arena (Third Edition). US:The McGraw-Hill Companies, Inc.,2006.
[2]靳志宏,关志民.运营管理.北京：机械工业出版社,2007.
[3]Michael Pinedo著.调度：原理、算法和系统.北京：清华大学出版社,2007.
[4]Ruiz Ruben, Maroto Concepcion. A comprehensive review and evaluation of permutation flowshop heuristics. European Journal of Operational Research.2005,165(2):479-494.
[5]李娜.集装箱码头连续泊位与岸桥调度联合优化研究：(博士学位论文).大连：大连海事大学,2012.
[6]屈国强.求解流水车间调度问题的瓶颈指向启发式算法.计算机集成制造系统.2012,18(2)：356-363.
[7]贾兆红,朱建建,陈华平.柔性作业车间调度的动态禁忌粒子群优化算法.华南理工大学学报(自然科学版).2012,40(1)：69-76.
[8]Johnson S. M.. Optimal two- and three-stage production schedules with setup times included. Naval Research Logistics Quarterly.1954,1(1):61-68.
[9]Thornton H. W., Hunsucke J. L.. A new heuristic for minimal makespan in flow shops with multiple processors and no intermediate storage. European Journal of Operational Research.2004,152(1):96-114.
[10]Jackson J. R.. An extension of Johnson's results on job lot scheduling. Naval Research Logistics Quarterly.1956,5(3):201-203.
[11]刘志雄.调度问题中的粒子群优化方法及其应用研究：(博士学位论文).武汉：武汉理工大学,2005.
[12]Low Chinyao. Simulated annealing heauristic for flow shop scheduling problems with unrelated parallel machines. Computers and Operations Research.2005,32:2013-2025.
[13]Jin Zhihong, Yang Zan, Ito Takahiro. Metaheuristic algorithms for the multistage hybrid flowshop scheduling problem. International Journal of Production Economics.2006,100(2):322-334.
[14]Koulamas Christos, Kyparisis George J.. Concurrent flowshop scheduling to minimize makespan. European Journal of Operational Research.2004,156(2):524-529.
[15]Vallada Eva, Ruiz Ruben, Minella Gerardo. Minimising total tardiness in the m-machine flowshop problem:A review and evaluation of heuristics and metaheuristics. Computers & Operations Research.2008,35(4):1350-1373.
[16]王凌,周刚,许烨,等.混合流水线调度研究进展.化工自动化及仪表.2011,38(1)：1-8.
[17]Li Der-Chiang, et al. Two-machine flowshop scheduling with truncated learning to minimize the total completion time. Computers & Industrial Engineering.2011,61(3):655-662.
[18]Kis Tamas, Pesch Erwin. A review of exact solution methods for the non-preemptive multiprocessor flowshop problem. European Journal of Operational Research.2005,164(3):592-608.
[19]Woo Hoon Shik, Yim Dong Soon. A heuristic algorithm for mean flowtime objective in flowshop scheduling. Computers Operations Research.1998,25(3):175-182.
[20]肖建华.多处理器任务调度问题的新近似算法.计算机工程.2005,31(24)：50-52.
[21]轩华,唐立新.带多处理器任务的动态混合流水车间调度问题.计算机集成制造系统.2007,13(11)：2254-2260.
[22]Pan Quan-Ke, Wang Ling. Effective heuristics for the blocking flowshop scheduling problem with makespan minimization. Omega.2012,40(2):218-229.
[23]Costa Wagner Emanoel, Goldbarg Marco Cesar, Goldbarg Elizabeth G.. New VNS heuristic for total flowtime flowshop scheduling problem. Expert Systems with Applications.2012,39(9):8149-8161.
[24]Xiao W., Hao P., Zhang S.. Hybrid Flow Shop Scheduling Using Genetic Algorithms. Proc of the 3rd World Congress on Intelligent Control and Automation.2000:537-541.
[25]Arroyo Jose Elias Claudio, Armentano Vinicius Amaral. Genetic local search for multi-objective flowshop scheduling problems. European Journal of Operational Research.2005,167(3):717-738.
[26]杨美荣,崔曼曼,史建锋,等.遗传算法在多品种小批量模式MPS中的应用(优先出版).计算机工程与应用.2012.
[27]伍麟珺.基于改进遗传算法的天线方向图研究.广西轻工业.2012,(1)：59-60.
[28]秦毅,侯强,刘志霞.基于遗传算法的多模式资源受限项目调度问题研究(优先出版).辽宁工程技术大学学报(社会科学版).2012.
[29]刘爱军,杨育,邢青松,等.复杂制造环境下的改进非支配排序遗传算法(优先出版).计算机集成制造系统.2012.
[30]Naderi B., et al. An improved simulated annealing for hybrid flowshops with sequence-dependent setup and transportation times to minimize total completion time and total tardiness. Expert Systems with Applications.2009,36(6):9625-9633.
[31]路飞,田国会,姜健,等.用基于模拟退火机制的多种群并行遗传算法解Job-Shop调度问题.山东工业大学学报.2001,(4)：361-364.
[32]谭伟,赵蔓,李向.基于模拟退火遗传算法的多项目调度问题研究.微计算机信息.2009,(9)：220-222.
[33]史烨,李凯.并行机问题的模拟退火调度算法研究.运筹与管理.2011,(4)：104-107.
[34]Wang Xianpeng, Tang Lixin. A tabu search heuristic for the hybrid flowshop scheduling with finite intermediate buffers. Computers & Operations Research.2009,36(3):907-918.
[35]Eksioglu Burak, Eksioglu Sandra Duni, Jain Pramod. A tabu search algorithm for the flowshop scheduling problem with changing neighborhoods. Computers & Industrial Engineering.2008,54(1): 1-11.
[36]Liao Li-Man, Huang Ching-Jen. Tabu search for non-permutation flowshop scheduling problem with minimizing total tardiness. Applied Mathematics and Computation.2010,217(2):557-567.
[37]Ying Kuo Ching, Lin Shih Wei. Multiprocessor task scheduling in multistage hybrid flow-shops:An ant colony system approach. International Journal of Production Research.2006,44(16):3161-3177.
[38]何小虎.优化蚁群算法在排课中的应用策略.计算机与数字工程.2012,(7)：33-34.
[39]孙晓莹,徐红霞.基于蚁群算法的煤矿运输车辆调度应用研究.煤炭技术.2012,(7)：140-142.
[40]张向丰,董淑娟.蚁群算法改进及其实现.电脑开发与应用.2012,(7)：14-15.
[41]祝文康,钟育彬.基于改进蚁群算法的物流车辆调度问题研究.江南大学学报(自然科学版).2012,(3)：273-276.
[42]Wang Xianpeng, Tang Lixin. A discrete particle swarm optimization algorithm with self-adaptive diversity control for the permutation flowshop problem with blocking. Applied Soft Computing.2012, 12(2):652-662.
[43]Jarboui Bassem, et al. A combinatorial particle swarm optimisation for solving permutation flowshop problems. Computers & Industrial Engineering.2008,54(3):526-538.
[44]童言,张焕国.利用基于粒子群优化和模拟退火的混合策略优化正形置换(英文).中国通信.2012,(1)：49-57.
[45]Sugimoto M., Kobayashi H., Ishibashi A.. The development of the support system for tugs operation. Marine and Maritime.2001, Marine Technology Ⅳ:325-334.
[46]Van Phuoc-Bui, et al. Modeling and control allocation for ship berthing system design. ICCAS.2010: 195-200.
[47]潘永良,朱泓.对超大型船舶实施护航拖轮监护的探讨.港口科技.2010,(11)：36-40.
[48]李柏然.港作拖轮的性能要求和发展趋势.物流技术.2010,(8)：136-145.
[49]吴中强.全回转港作拖轮推进装置特点及推拖模式分析.中国水运.2011,11(1)：98-100.
[50]Yang Lin-Jia, et al. Experimental study on braking force characteristics of tugboat. Journal of Hydrodynamics.2010,22(5):332-337.
[51]张鹏.拖轮协助船舶操纵建模与仿真：(硕士学位论文).大连：大连海事大学,2008.
[52]李亮等.拖轮协助大船数学模型在MATLAB上的实现.中国水运.2008,8(7)：21-23.
[53]刘谊信.关于港口拖轮主要设备配件的管理探讨.科技创新导报.2009,(9)：38.
[54]熊振南,翁跃宗,张寿桂.超大型船舶靠离泊操纵中拖轮助操的应用.集美大学学报(自然科学版).2009,14(3)：260-264.
[55]郭万庶.航海模拟器中拖轮拖带作业数学模型的研究：(硕士学位论文).大连：大连海事大学,2010.
[56]刘斌.拖轮选型优化研究：(硕士学位论文).大连：大连海事大学,2011.
[57]蒲子芳.航海模拟器中拖轮顶推作业数学模型的研究：(硕士学位论文).大连：大连海事大学,2011.
[58]张琳.浅谈拖轮节能降耗的途径.交通财会.2011,(8)：66-70.
[59]翟俊升,李德源,刘巍.港作拖轮经济使用年限的确定.大连海运学院学报.1992,18(3)：309-314.
[60]刘志雄,王少梅.带特殊工艺约束的双目标并行多机调度问题研究.计算机集成制造系统.2005,11(11)：1616-1620.
[61]刘志雄,王少梅.基于粒子群算法的并行多机调度问题研究.计算机集成制造系统.2006,12(2)：183-187.
[62]Wang Su, Bo Meng. Resource allocation and scheduling problem based on genetic algorithm and ant colony optimization.2007,4426:879-886.
[63]何涛,朱宏辉.遗传算法在拖轮调度中的应用.物流技术.2008,27(4)：138-139.
[64]刘光.基于多目标粒子群算法的港口调度系统设计与实现：(硕士学位论文).哈尔滨：哈尔滨工程大学,2008.
[65]Liu Zhixiong. Hybrid Evolutionary Strategy Optimization for Port Tugboat Operation Scheduling. Third Internation Symposium on Intelligent Information Technology Application.2009: 511-515.
[66]He Tao, Ye Wenwen. Hybrid Evolutionary Strategy Optimization for Port Tugboat Operation Scheduling.2nd International Conference on Information Science and Engineering.2010:1438-1441.
[67]Wang Su, et al. An Improved Discrete Particle Swarm Optimization for Tugboaat Scheduling Problem in Container Terminal. International Conference on Information Management, Innovation Management and Industrial Engineering.2010:593-596.
[68]刘志雄.考虑最短距离作业的港口拖轮作业调度优化.西南交通大学学报.2011,46(5)：875-881.
[69]Wang Su, Kaku Iko, Chen Guoyue. Research on the Modeling of Tugboat Assignment Problem in Container Terminal. Advanced Materials Research.2012,433-440:1957-1961.
[70]董良才,徐子奇,宓为建.基于遗传算子粒子群算法的拖轮动态调度.数学的实践与认识.2012,42(6)：122-133.
[71]Yu Wenhui. Heuristics Algorithm for Simulation and Optimization System of Port Tugboats Allocation. International Conference on Internet Computing and Information Services, ICICIS.2011: 306-309.
[72]熊军魁.港口拖轮优化配置研究：(硕士学位论文).武汉：武汉理工大学,2002.
[73]熊军魁.港口生产与拖轮配置问题探讨.船海工程.2003,(1)：45-46.
[74]潘文军,王少梅.基于神经网络的聚类分析方法及在港口设备配置中的应用.交通与计算机.2003,21(5)：44-47.
[75]陈伟.仿真优化方法及其在港口设备配置中的应用研究：(硕士学位论文).武汉：武汉理工大学,2002.
[76]陈伟,沈斌,王少梅.启发式仿真优化方法在设备配置中的应用研究.系统仿真学报.2005,17(9)：2280-2283.
[77]刘志雄,王少梅.港口拖轮作业的计算机仿真研究.系统仿真学报.2004,16(1)：45-47.
[78]Zhixiong Liu, Shaomei Wang. Operation Resource Allocation Optimization of Discrete Event System Based on Simulation. Journal of Wuhan University of Technology.2005,29(2):327-330.
[79]杨志华.港口拖轮优化配置研究.系统仿真技术.2012,8(2)：169-174.
[80]靳志宏,计明军.物流实用优化技术.北京：中国物资出版社,2008.
[81]Metropolis N., et al. Equation of state calculations by fast Computing mathines. Journal of Chemical Physics.1953,21:1087-1092.
[82]田澎,王浣尘,张冬茉.旅行商问题(TSP)的模拟退火求解.上海交通大学学报.1995,30(S1)：111-116.
[83]庞哈利,郑秉霖,徐心和.一种自适应的模拟退火算法.控制与决策.1999,14(5)：477-480.
[84]靳利霞.蛋白质结构预测方法：(博士学位论文).大连：大连理工大学,2002.
[85]计明军.若干随机性全局优化算法的研究：(博士学位论文).大连：大连理工大学,2004.
[86]李红芳,侯朝胜.模拟退火遗传算法与结构优化设计.天津理工学院学报.2004,(4)：96-98.
[87]康立山,谢云,尤矢勇等.非数值并行算法——模拟退火算法.北京：科学出版社,1994.
[88]邢文训,谢金星.现代优化计算方法.北京：清华大学出版社,1999.
[89]张晓萍,石伟,刘玉坤.物流系统仿真.北京：清华大学出版社,2011.
[90]卫军胡,韩九强,孙国基.离散事件系统仿真技术在制造系统调度中的应用.系统仿真学报.2000, 12(1)：27-30.
[91]Lacksonen T.. Empirical comparison of search algorithms for discrete event simulation. Computers& Industrial Engineering.2001, (40):133-148.
[92]Law A. M., Kelton W. D.. Simulation Modeling and Analysis (3rd edition). Beijing:Tsinghua University Press,2000.
[93]程光,邬洪迈,陈永刚.工业工程与系统仿真.北京：冶金工业出版社,2007.
[94]刘玉坤,石伟,张晓萍等.描述离散动态系统的集合标识Petri网：STPNs.系统工程理论与实践.2004,(7)：59-65.
[95]刘玉坤.装配型供应链库存系统建模与分析：(博士学位论文).北京：清华大学,2004.
[96]朱顺应,朱凯,王丽铮.基于Arena的三峡枢纽运输系统仿真模型.水运工程.2011,450(2)：40-44.
[97]杨磊.Y医药物流中心作业系统Arena仿真优化应用研究：(硕士学位论文).西安：长安大学,2009.
[98]李立,王晓东,邱梦,等.基于Arena的集装箱码头内部道路运输系统仿真研究.河北工业大学学报.2010,39(2)：64-68.
[99]袁子文,张培林,董升平.基于Arena的武钢工业港原料码头物流系统仿真研究.交通科技.2009,(4)：151-153.
[100]方绍强,卫克,陈伟鹏,等.飞行保障过程UML建模与ARENA仿真.系统工程理论与实践.2008,(11)：165-170.
[101]屈乾沁,关晓兰,刘志萍.基于Arena的电子商务物流配送体系的建模与仿真研究.物流技术.2010,29(1)：90-92.
[102]郑耿灶,苏平,豆之敬.基于仿真优化方法的混合装配线平衡.工业工程.2010,13(3)：121-125.
[103]马文强,杜子平,李东坡.仿真优化在制造系统生产调度中的研究进展.现代制造工程.2012,(3)：10-14.
[104]Kelton W. David, Sadowski Randall P., T. Sturrock David. Simulation with Arena (Third Edition). US:The McGraw-Hill Companies, Inc.,2006.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |