多式联运供应链的协调与协同优化研究
|
摘要
随着制造业全球化趋势的不断深化,国际集装箱多式联运已经成为国际贸易的重要桥梁,其运输服务网络的覆盖日趋全球化。在这样的背景下,多式联运更加强调物流企业之间的相互协作、以及不同运输方式之间的无缝衔接,以满足不同客户的个性化、一体化服务要求。而如何提高多式联运供应链的运营效率和服务质量不仅引起了人们的广泛关注,而且已成为物流优化领域研究的热点问题。
本文旨在从多式联运全局优化、以及局部协调的角度,对上述问题展开讨论。首先在分布式决策下研究企业之间的协商机制、以及多式联运供应链的动态构建方法；然后考虑多式联运中的不确定性、及碳排放等问题,以运输速度为契合点进行扩展研究；最后以集装箱码头的泊位和岸桥为例,讨论了动态不确定环境下物流终端的作业优化对多式联运计划的可靠性以及货物按时交付率的影响。本文具体的研究工作包括如下三个方面：
第一、针对多式联运中的动态性、自治性、合作性等特点,提出了一种基于多agent系统的多式联运供应链动态构建方法,旨在通过agent间的信息交互与协商,构建出满足托运人要求的运输方案。构建过程分为招投标决策和中标决策。在招投标决策中,采用重复拍卖机制,通过价格引导使承运人自主压缩运输时间,以满足托运人的时间约束,从而创建出多式联运网络。在中标决策中,通过拉格朗日启发式算法得到满足时间约束的多式联运运输方案。仿真实验表明,针对绝大部分问题实例,这种方法能够有效地求解出满足时间约束下的优化解；同时,相比固定步长下的更新机制,自主决策下的方案更新机制可以有效加快协商过程的收敛性。
第二、将运输时间和中转时间的不确定性、以及碳排放等问题引入多式联运供应链的动态构建,建立了多式联运随机模型。一方面,从运输速度和燃油消耗之间的关系入手,采用基于活动的方法估算运输过程中的碳排放量；另一方面,采用机会约束来描述不确定环境下的时间窗约束满足情况,并用近似确定性等价形式代替机会约束,以提高模型的求解速度。在招投标决策中,采用基于动态中转备选集的网络变形、以及考虑期望值的节点预处理来改进上一章的动态协商机制。在中标决策中,针对模型中的机会约束,设计了一种基于变长染色体的改进遗传算法求解模型。通过仿真结果可以看出,针对大部分问题实例,本章推导出的近似确定性等价形式可以有效加快算法的求解速度。仿真实验还分析了机会约束中置信水平的灵敏度,并验证了该模型下的求解方案能够有效地降低运输过程中的碳排放量。
第三、针对船舶实际到港时刻的动态不确定性,提出了一种基于鲁棒反应式策略的泊位和岸桥联合调度方法,在分布式决策下设计了多agent系统的框架模型。预调度阶段,在以计划延误时间为服务性指标的泊位分配模型中,引入缓冲时间长度作为鲁棒性指标,建立了基于冗余策略的鲁棒泊位分配模型,并采用CPLEX求解。执行阶段,采用as soon as possible的调整策略和基于合同网的协商机制,合理调配泊位和岸桥,进一步提高系统计划执行的稳定性。仿真实验表明,针对实际中的问题规模,CPLEX可以有效地求得最优解；此外,鲁棒泊位分配方案在不确定环境下的抗干扰能力明显高于传统方案,而相对于仅采用泊位实时调度策略,在执行阶段采用泊位和岸桥联合实时调度策略可以更有效地降低不确定性带来的影响,进一步提高系统执行的稳定性。
With the acceleration of manufacturing globalization, the international container multi-modal transport has become an important bridge in the international trade, which is supported by its increasingly globalized transportation service network. In order to provide personalized and integrated service, the multi-modal transport pays more emphasis on cooperation of logistics enterprises and seamless connection of transport modes. The problem of improving the operational efficiency and service quality for supply chain in multi-modal transport has attracted great attention, and become a hotspot in the field of logistics optimization.
This dissertation aims to study the above problem through the coordination and collaboration in enterprises, as well as the optimization and scheduling in the logistics terminal. The dissertation includes three research points:(a) the negotiation mechanism and dynamic formation of supply chain in multi-modal transport;(b) decision of transportation speed considering uncertainty and carbon emission; and (c) the impact of resource optimization in logistics terminal on the reliability and on-time delivery of multi-modal transport plan. More specifically, this study can be summarized as follows:
Firstly, considering the dynamics, autonomy, and cooperation, a dynamic formation strategy of supply chain for multi-modal transport based on multi-agent system is proposed. The transportation plan is made to meet the demand of consignor through information exchange and negotiation. The formation is divided into bidding decision and winning bidding decision. In bidding decision, the repeated auction mechanism is adopted to create the network of multi-modal transport. To meet the temporal constraint, a price induction mechanism is designed to make carriers reducing their transport time. In winning bidding decision, a Lagrangian-based heuristic algorithm is proposed to obtain the multi-modal transport plan. The simulation experiments show that the formation strategy can provide an effective solution for most of the instances; meanwhile, compared with the mechanism of fixed step size, the autonomous decision can efficiently accelerate the convergence of the negotiation process.
Secondly, to deal with the time uncertainty of transport and transit, as well as carbon emission of multi-modal transport, a stochastic model is proposed. Considering the relationship between transport speed and fuel consumption, the activity-based approach is adopted to estimate the carbon emission. The chance constraint is used to describe the satisfaction of time window constraint under uncertainty, and its approximate deterministic equivalent form is obtained to improve the solving speed. To improve the dynamic negotiation of bidding decision in the previous chapter, the network deformation based on alternative set of dynamic transit and node preprocessing with expectation consideration is designed. Considering the nonlinearity of objective function and chance constraint, an improved genetic algorithm based on variable-length chromosome is designed for winning bidding decision. The simulation experiments show that the approximate deterministic equivalent form can efficiently accelerate the convergent speed. Besides, the sensitivity analysis on the confidence level of chance constraint is also conducted and the solution of this model can effectively reduce carbon emission.
Lastly, considering the dynamics and uncertainty of vessel arrival time, an integrated scheduling for berth and quay cranes based on robust and reactive policy is proposed. And a model based on multi-agent system under distributed decision is designed. In pre-scheduling stage, using total delay time of vessels as service measure and length of buffer time as robustness measure, the problem is formulated as a robust berth allocation model based on a redundancy policy. And CPLEX is adopted to solve the model. In scheduling stage, for improving the stability of system plan, the as soon as possible strategy and the negotiation mechanism based on contract network protocol are adopted to make full use of berth and quay cranes. The simulation experiments show that CPLEX can solve the optimal solution, and the robust berth allocation plan with real-time scheduling of berth and quay cranes can obviously diminish the effects from uncertainty and improve the stability of system performance.
本文旨在从多式联运全局优化、以及局部协调的角度,对上述问题展开讨论。首先在分布式决策下研究企业之间的协商机制、以及多式联运供应链的动态构建方法；然后考虑多式联运中的不确定性、及碳排放等问题,以运输速度为契合点进行扩展研究；最后以集装箱码头的泊位和岸桥为例,讨论了动态不确定环境下物流终端的作业优化对多式联运计划的可靠性以及货物按时交付率的影响。本文具体的研究工作包括如下三个方面：
第一、针对多式联运中的动态性、自治性、合作性等特点,提出了一种基于多agent系统的多式联运供应链动态构建方法,旨在通过agent间的信息交互与协商,构建出满足托运人要求的运输方案。构建过程分为招投标决策和中标决策。在招投标决策中,采用重复拍卖机制,通过价格引导使承运人自主压缩运输时间,以满足托运人的时间约束,从而创建出多式联运网络。在中标决策中,通过拉格朗日启发式算法得到满足时间约束的多式联运运输方案。仿真实验表明,针对绝大部分问题实例,这种方法能够有效地求解出满足时间约束下的优化解；同时,相比固定步长下的更新机制,自主决策下的方案更新机制可以有效加快协商过程的收敛性。
第二、将运输时间和中转时间的不确定性、以及碳排放等问题引入多式联运供应链的动态构建,建立了多式联运随机模型。一方面,从运输速度和燃油消耗之间的关系入手,采用基于活动的方法估算运输过程中的碳排放量；另一方面,采用机会约束来描述不确定环境下的时间窗约束满足情况,并用近似确定性等价形式代替机会约束,以提高模型的求解速度。在招投标决策中,采用基于动态中转备选集的网络变形、以及考虑期望值的节点预处理来改进上一章的动态协商机制。在中标决策中,针对模型中的机会约束,设计了一种基于变长染色体的改进遗传算法求解模型。通过仿真结果可以看出,针对大部分问题实例,本章推导出的近似确定性等价形式可以有效加快算法的求解速度。仿真实验还分析了机会约束中置信水平的灵敏度,并验证了该模型下的求解方案能够有效地降低运输过程中的碳排放量。
第三、针对船舶实际到港时刻的动态不确定性,提出了一种基于鲁棒反应式策略的泊位和岸桥联合调度方法,在分布式决策下设计了多agent系统的框架模型。预调度阶段,在以计划延误时间为服务性指标的泊位分配模型中,引入缓冲时间长度作为鲁棒性指标,建立了基于冗余策略的鲁棒泊位分配模型,并采用CPLEX求解。执行阶段,采用as soon as possible的调整策略和基于合同网的协商机制,合理调配泊位和岸桥,进一步提高系统计划执行的稳定性。仿真实验表明,针对实际中的问题规模,CPLEX可以有效地求得最优解；此外,鲁棒泊位分配方案在不确定环境下的抗干扰能力明显高于传统方案,而相对于仅采用泊位实时调度策略,在执行阶段采用泊位和岸桥联合实时调度策略可以更有效地降低不确定性带来的影响,进一步提高系统执行的稳定性。
With the acceleration of manufacturing globalization, the international container multi-modal transport has become an important bridge in the international trade, which is supported by its increasingly globalized transportation service network. In order to provide personalized and integrated service, the multi-modal transport pays more emphasis on cooperation of logistics enterprises and seamless connection of transport modes. The problem of improving the operational efficiency and service quality for supply chain in multi-modal transport has attracted great attention, and become a hotspot in the field of logistics optimization.
This dissertation aims to study the above problem through the coordination and collaboration in enterprises, as well as the optimization and scheduling in the logistics terminal. The dissertation includes three research points:(a) the negotiation mechanism and dynamic formation of supply chain in multi-modal transport;(b) decision of transportation speed considering uncertainty and carbon emission; and (c) the impact of resource optimization in logistics terminal on the reliability and on-time delivery of multi-modal transport plan. More specifically, this study can be summarized as follows:
Firstly, considering the dynamics, autonomy, and cooperation, a dynamic formation strategy of supply chain for multi-modal transport based on multi-agent system is proposed. The transportation plan is made to meet the demand of consignor through information exchange and negotiation. The formation is divided into bidding decision and winning bidding decision. In bidding decision, the repeated auction mechanism is adopted to create the network of multi-modal transport. To meet the temporal constraint, a price induction mechanism is designed to make carriers reducing their transport time. In winning bidding decision, a Lagrangian-based heuristic algorithm is proposed to obtain the multi-modal transport plan. The simulation experiments show that the formation strategy can provide an effective solution for most of the instances; meanwhile, compared with the mechanism of fixed step size, the autonomous decision can efficiently accelerate the convergence of the negotiation process.
Secondly, to deal with the time uncertainty of transport and transit, as well as carbon emission of multi-modal transport, a stochastic model is proposed. Considering the relationship between transport speed and fuel consumption, the activity-based approach is adopted to estimate the carbon emission. The chance constraint is used to describe the satisfaction of time window constraint under uncertainty, and its approximate deterministic equivalent form is obtained to improve the solving speed. To improve the dynamic negotiation of bidding decision in the previous chapter, the network deformation based on alternative set of dynamic transit and node preprocessing with expectation consideration is designed. Considering the nonlinearity of objective function and chance constraint, an improved genetic algorithm based on variable-length chromosome is designed for winning bidding decision. The simulation experiments show that the approximate deterministic equivalent form can efficiently accelerate the convergent speed. Besides, the sensitivity analysis on the confidence level of chance constraint is also conducted and the solution of this model can effectively reduce carbon emission.
Lastly, considering the dynamics and uncertainty of vessel arrival time, an integrated scheduling for berth and quay cranes based on robust and reactive policy is proposed. And a model based on multi-agent system under distributed decision is designed. In pre-scheduling stage, using total delay time of vessels as service measure and length of buffer time as robustness measure, the problem is formulated as a robust berth allocation model based on a redundancy policy. And CPLEX is adopted to solve the model. In scheduling stage, for improving the stability of system plan, the as soon as possible strategy and the negotiation mechanism based on contract network protocol are adopted to make full use of berth and quay cranes. The simulation experiments show that CPLEX can solve the optimal solution, and the robust berth allocation plan with real-time scheduling of berth and quay cranes can obviously diminish the effects from uncertainty and improve the stability of system performance.
引文
[1]UNCTAD. Review of Maritime Transport 2011. New York and Geneva:United Nations Publication,2011.
[2]刘竹芃.天津港集装箱海铁联运研究.中国铁路,2012,(7)：26-28.
[3]港口经理人.http://www.thechoice.com.cn/resource_center/newsletter_display.php.2013(1).
[4]Taylor J C, Jackson G C. Conflict, power, and evolution in the intermodal transportation industry's channel of distribution. Transportation Journal,2000,39 (3):5-17.
[5]Vernimmen B, Dullaert W, Engelen S. Schedule unreliability in liner shipping:origin and consequence for hinterland supply chain. Maritime Economics & Logistics,2007,9(3): 193-213
[6]BunkerWorld Rotterdam. Monthly Bunker Prices,2012. http://www.bunkerworld.com/ prices/port/nl/rtm/.
[7]Olivier J G J, Greet J M, Jeroen A H W P. Trends in global CO2 emissions.2012 Report, PBL Netherlands Environmental Assessment Agency,2012.
[8]梁晓强.航运企业第三方物流绿色供应链实施现状与策略探讨.现代商贸工业,2009,21(023)：8-9.
[9]黄小兵,唐文胜.基于Agent系统的概念,方法和应用.计算机与现代化,2000,4：6-11.
[10]刘宝碇,赵瑞清,王纲.不确定规划及应用.北京：清华大学出版社,2004.
[11]吴平,韩才元.船舶和机车用高速柴油机废气再循环试验.国外内燃机车,2007,(2)：25-31.
[12]张云鹏,李庆祥.轮胎式集装箱门式起重机节能技术研究.交通节能与环保,2008,(2)：9-11.
[13]刘洪波,汪锋,张志平.集装箱轮胎吊”油改电”技术在港口节能减排中的应用.水运工程,2011,(9)：123-125.
[14]鄙克存,戴瑜兴,李加升.一种新型电子静止式岸电电源.电力电子技术,2011,45(2)：86-88.
[15]COSCO. COSCO sustainability report 2010. Beijing:COSCO,2011. http://www.cosco. com/GC_report/GC_report2011/index-cn.html.
[16]Notteboom T E, Vernimmen B. The effect of high fuel costs on liner service configuration in container shipping. Journal of Transport Geography,2009,17(5):325-337
[17]Corbett J J, Wang H F, Winebrake'J J. The effectiveness and costs of speed reductions on emissions from international shipping. Transportation Research Part D:Transport and Environment,2009,14(8):593-598.
[18]Psaraftis H N, Kontovas C A. Balancing the economic and environmental performance of maritime transportation. Transportation Research Part D:Transport and Environment,2010, 15(8):458-462.
[19]Cariou P. Is slow steaming a sustainable means of reducing CO2 emissions from container shipping? Transportation Research Part D:Transport and Environment,2011,16(3): 260-264.
[20]朱小宁.集装箱多式联运通道的规划问题：[博士学位论文].北京：北京交通大学,2000.
[21]马彩雯.多式联运的虚拟企业运作模式研究：[博士学位论文].大连：大连海事大学,2007.
[22]Maurizio B, Azedine B, Mohamed R. Object oriented model for container terminal distributed simulation. European Journal of Operational Research,2006,175(3):1731-1751.
[23]于汝民.现代集装箱码头经营管理.北京：人民交通出版社,2007.8(1).
[24]Agarwal R, Ergun O. Shipping scheduling and network design for cargo routing in liner shipping. Transportation Science,2008,42(2):175-196.
[25]Shintani K, Imai A, Nishimura E, et al. The container shipping network design problem with empty container repositioning. Transportation Research Part C,2007,43(1):39-59.
[26]王琳.不确定环境下的班轮企业资源管理研究：[博士学位论文].天津：南开大学,2010.
[27]Sandra U N, Christian P, Roberto W C. An effective memtic algorithm for the cumulative capacitated vehicle routing problem. Computers & Operations Research,2010,37(11): 1877-1885.
[28]Agostinho A, Marielle C, Rosa F, et al. The robust vehicle routing problem with time windows. Computers & Operations Research,2013,40(3):856-866.
[29]李广儒,杨大奔,任大伟.集卡动态调度路径优化算法.交通运输工程学报,2012,12(3)：86-91.
[30]Heaver T D. The Evolving Roles of Shipping Lines in International Logistics. International Journal of Maritime Economics,2002,4(3):210-230.
[31]孙光圻,闵德权.港航合作的战略价值与策略模式.中国港口,2000,(7)：43-44.
[32]周甫宾,曹蕾.港航合作模式的评述.中国水运,2009,9(5)：53-54.
[33]Yevdokimov Y V. Measuring economic benefits of intermodal transportation. Transportation Law Journal,2000,27(3):439-452.
[34]Handman A L. Intermodalism-a solution for congestion at the millennium. Review of Policy Research,2002,19(2):51-61.
[35]Szyliowicz J S. Decision-making, intermodal transportation, and sustainable mobility: towards a new paradigm. International Social Science Journal,2003,55(2):185-197.
[36]Janic M. Modelling the full costs of an intermodal and road freight transport network. Transportation Research Part D,2007,12(1):33-44.
[37]魏际刚,荣朝和.中国集装箱多式联运发展的宏观经济因素分析.中国软科学,2000,(8)：39-44.
[38]肖平安.我国国际集装箱多式联运之探讨.交通企业管理,2006,21(8)：46-47.
[39]郭琴.我国多式联运发展的若干思考.物流工程与管理,2010,32(4)：3-4.
[40]Arnold P, Peeters D, Thomas I. Modelling a rail/road intennodal transportation system. Transportation Research Part E,2004,40(3):255-270.
[41]汪涛,朱晓宁,马金元.三级网络结构的铁路集装箱一体化服务体系设计.物流技术,2009,28(10)：45-47.
[42]洪雁.铁路集装箱运输系统规划若干问题研究：[博士学位论文].北京：北京交通大学,2008.
[43]Imai A, Nishimura E, Hattori M, et al. Berth allocation at indented berths for mega-containerships. European Journal of Operational Research,2007,179(2):579-593.
[44]Imai A, Chen H C, Nishimura E, Papadimitriou S. The simultaneous berth and quay crane allocation problem. Transportation Research Part E,2008,44(5):900-920.
[45]Xu D S, Li C L, Leung J Y T. Berth allocation with time-dependent physical limitations on vessels. European Journal of Operational Research,2012,216(1):47-56.
[46]Bazzazi M, Safael N, Javadian N. A genetic algorithm to solve the storage space allocation peoblem in a container terminal. Computer & Industrial Engineering,2009,56(1):44-52.
[47]Xu Y, Chen Q, Quan X. A robust integrated approach to yard space allocation and crane scheduling in container terminals.7th International Conference on Service Systems and Service Management,2010,28-30.
[48]Sharif O, Huynh N. Storage space allocation at marine container terminals using ant-based control. Expert Systems with Applications,2013,40(6):2323-2330.
[49]Choi H R, Park B K, Lee J W, et al. Dispatching of container trucks using genetic algorithm. 4th International Conference on Interaction Sciences,2011,146-151.
[50]Zheng X J, Shu F, Mi W J. Research on genetic algorithm-based simulation of dynamic container truck scheduling. IEEE International Conference on Computer Science and Automation Engineering,2012, (2):131-135.
[51]Angelica L, Giovanni S. Shortest viable path algorithm in multimodal networks. Transportation Research Part A,2001,35(3):225-241.
[52]Delling D, Pajor T, Wagner D. Accelerating multi-modal route planning by access-node. Proceedings of the 17th Annual European Symposium on Algorithms,2009,587-598.
[53]康凯,牛海姣,朱越杰.基于粒子群蚁群算法求解多式联运中运输方式与运输路径集成优化问题.物流工程与管理,2009,31(10)：61-65.
[54]Chang T S. Best routes selection in international intermodal networks. Computers & Operations Research,2008,35(9):2877-2891.
[55]佟璐,聂磊,付慧玲.多式联运路径优化模型与方法研究.物流技术,2010,29(3)：57-60.
[56]魏航,李军,蒲云.时变网络下多式联运的最短路径问题研究.系统工程学报,2007,22(2)：205-209.
[57]刘杰,何世伟,宋瑞.基于运输方式备选集的多式联运动态路径优化研究.铁道学报,2011,33(10)：1-6.
[58]张洁,高亮,李培根.多Agent技术在先进制造中的应用.北京：科技出版社,2004.
[59]Wang SL, Xia H, Liu F. Agent-based modeling and mapping of a manufacturing system. Journal of Materials Processing Technology,2002,129(3):518-523.
[60]李敬华,刘文剑,金天国.基于多Agent的多型号生产调度系统研究.计算机集成制造 系统,2006,12(4)：573-578.
[61]Goldsmith S Y, Phillips L R, Spires S V. A multi-agent system for coordinating international shipping. Agent Mediated Electronic Commerce,1999,91-104.
[62]Sinha-Ray P, Carter J, Field T, et al. Container World:Global agent-based modelling of the container transport business. Proceedings 4th Workshop on Agent-Based Simulation,2003.
[63]杨华龙,东方,郑斌.集装箱航运服务多属性组合拍卖模型与算法.交通运输工程学报,2009,9(5)：111-115.
[64]Robu V, Noot H, Poutre H L, et al. An interactive platform for auction-based allocation of loads in transportation logistics. Proceedings of the 7th international joint conference on Autonomous Agents and Multiagent Systems,2008,3-10.
[65]Robu V, Poutre H L. Designing bidding strategies in sequential auctions for risk averse agents. Multiagent and Grid Systems-Advances in Agent-mediated Automated Negotiations, 2010,6(5):437-457.
[66]Robu V, Noot H, Poutre H L, et al. A multi-agent platform for auction-based allocation of loads in transportation logistics. Expert Systems with Applications,2011,38(4):3483-3491.
[67]Dong J W, Li Y J. Agent-based design and organization of intermodal freight transportation systems. International Conference on Machine Learning and Cybernetics,2003,4: 2269-2274.
[68]Dullaert W, Neutens T, Berghe G V, et al. MamMoeT:an intelligent agent-based communication support platform for multimodal transport. Expert Systems with Applications, 2009,36(7):10280-10287.
[69]马彩雯,孙光圻.基于多Agent的国际多式联运虚拟企业信息集成框架.大连海事大学学报,2006,32(2)：71-74.
[70]张戎,闫攀余.基于多Agent的集装箱海铁联运信息系统模型.同济大学学报,2007,35(1)：72-76.
[71]马彩雯.基于多Agent的多式联运各区段分运承运人选择.哈尔滨工业大学学报,2007,39(12)：1989-1992.
[72]Xiong G W, Wang Y. Research on job integration of multi-agent in multimodal transportation with time windows. International Journal of Services, Economics and Management,2010, 2(3):307-321.
[73]Monteiro T, Roy D, Anciaux D. Multi-site coordination using a multi-agent system. Computers in Industry,2007,58(4):367-377.
[74]Kim H S, Cho J H. Supply chain formation using agent negotiation. Decision Support Systems,2010,49(1):77-90.
[75]Wang M H, Liu J M, Wang H Q, et al. On-demand e-supply chain integration:a multi-agent constraint-based approach. Expert Systems with Applications,2008,34(4):2683-2692.
[76]Dong J X, Song D P. Container fleet sizing and empty repositioning in liner shipping systems. Transportation Research Part E,2009,45(6):860-877.
[77]Song D P, Carter J. Empty container repositioning in shipping industry. Maritime Policy & Management,2009,36(4):291-307.
[78]王琳,陈秋双,张瑞玲.面向不确定环境的集装箱空箱鲁棒优化调度.第29届中国控制会议,2010,1791-1796.
[79]王斌,唐国春.海运集装箱调运随机优化模型.交通运输系统工程与信息,2010,10(3)：58-63.
[80]Sumalee A, Uchida K, Lam W H K. Stochastic multi-modal transport network under demand uncertainties and adverse weather condition. Transportation Research Part C,2011,19(2): 338-350.
[81]Berit D B, Jakob D, David P, et al. The vessel schedule recovery problem(VSPR)-a mip model for handling disruptions in liner shipping. European Journal of Operational Research, 2013,224(2):362-374.
[82]Notteboom T E. The time factor in liner shipping services. Maritime Economics & Logistics, 2006,8:19-39.
[83]Hendriks M, Laumanns M, Lefeber E, et al. Robust cyclic berth planning of container vessels. OR Spectrum.2010,32(3):501-517.
[84]Han X L, Lu Z Q, Xi L F. A proactive approach for simultaneous berth and quay crane scheduling problem with stochastic arrival and handling time. European Journal of Operational Research,2010,207(3):1327-1340.
[85]李延晖,马士华,刘黎明.基于时间约束的供应链配送系统随机模型.预测,2004,23(4)：45-48.
[86]程赐胜,陈宝星,关仕罡.商品配送中车辆调度随机模型的建立及其求解.系统工程,2004,22(4)：66-70.
[87]Psaraftis H N, Kontovas C A. CO2 emission statistics for the world commercial fleet. WMU Journal of Maritime affairs,2009,8(1):1-25.
[88]Alvarez J F. Joint routing and deployment of a fleet of container vessel. Maritime Economists & Logistics,2009,11(2):186-208.
[89]Fagerholt K, Laporte G, Norstad I. Reducing fuel emissions by optimizing speed on shipping routes. Journal of the Operational Research Society,2010,61(3):523-529.
[90]Norstad I, Fagerholt K, Laporte G. Tramp ship routing and scheduling with speed optimization. Transportation Research Part C:Emerging Technologies,2011,19(5):853-865.
[91]Ronen D. The effect of oil price on containership speed and fleet size. Journal of the Operational Research Society,2011,62(1):211-216.
[92]Qi X T, Song D P. Minimizing fuel emissions by optimizing vessel schedules in liner shipping with uncertain port times. Transportation Research Part E,2012,48(4):863-880.
[93]Golias M M, Saharidis G K, Boile M, et al. The berth allocation problem:optimizing vessel arrival time. Maritime Economics & Logistics,2009,11(4):358-377.
[94]Lang N, Veenstra A. A quantitative analysis of container vessel arrival planning strategies. OR Spectrurn,2010,32(3):477-499.
[95]Du Y Q, Chen Q S, Quan X W, et al. Berth allocation considering fuel consumption and vessel emissions. Transportation Research Part E:Logistics and Transportation Review,2011, 47(6):1021-1037.
[96]Bektas T, Laporte G. The pollution-routing problem. Transportation Research Part B,2011, 45(8):1232-1250.
[97]Suzuki Y. A new truck-routing approach for reducing fuel consumption and pollutants emission. Transportation Research Part D,2011,16(1):73-77.
[98]Figliozzi M A. The impacts of congestion on time-definitive urban freight distribution networks CO2 emission levels:results from a case study in Portland, Oregon. Transportation Research Part C,2011,19 (5):766-778.
[99]Jabali O, Woensel T, Kok A G. Analysis of travel times and CO2 emissions in time-dependent vehicle routing. Production and Operations Management,2012,21(6):1060-1074.
[100]Xiao Y, Zhao Q, Kaku I, et al. Development of a fuel consumption optimization model for the capacitated vehicle routing problem. Computers and Operations Research,2012,39(7): 1419-1431.
[101]Huang Y, Shi C, Zhao L, et al. A study on carbon reduction in the vehicle routing problem with simultaneous pickups and deliveries.2012 IEEE International Conference on Service Operations and Logistics and Informatics,2012,302-307.
[102]Kim K H, Park Y M. A crane scheduling method for port container terminals. European Journal of Operational Research,2004,156(3):752-768.
[103]Moccia L, Cordeau J F, Gaudioso M, et al. A branch-and-cut algorithm for the quay crane scheduling problem in a container terminal. Naval Research Logistics,2006,53(1):45-59.
[104]Li C L, Cai X Q, Lee C Y. Scheduling with multiple-job-on-one-processor pattern. HE Transactions,1998,30(5):433-445.
[105]Guan Y, Xiao W Q, Cheung R K, et al. A multiprocessor task scheduling model for berth allocation:heuristic and worst-case analysis. Operations Research Letters,2002,30(5): 343-350.
[106]Song L, Cherrett T, Guan W. Study on berth planning problem in a container seaport:using an integrated programming approach. Computers & Industrial Engineering,2012,62(1): 119-128.
[107]Monaco M F, Sammarra M. The berth allocation problem:a strong formulation solved by a lagrangean approach. Transportation Science,2007,41(2):265-280.
[108]Lai K K, Shih K. A study of container berth allocation. Journal of Advanced Transportation, 1992,26(1):45-60.
[109]Imai A, Nishimura E, Papadimitriou S. The dynamic berth allocation problem for a container port. Transportation Research Part B,2001,35(4):401-417.
[110]Nishimura E, Imai A, Papadimitriou S. Berth allocation planning in the public berth system by genetic algorithms. European Journal of Operational Research,2001,131(2):282-292.
[111]Imai A, Nishimura E, Papadimitriou S. Berth allocation with service priority. Transportation Research Part B,2003,37(5):437-457.
[112]韩笑乐,陆志强,奚立峰.具有服务优先级别的动态离散泊位调度优化.上海交通大学学报,2009,43(6)：902-905.
[113]Cordeau J F, Laporte G, Legato P, et al. Models and tabu search heuristics for the berth-allocation problem. Transportation Science,2005,39(4):526-538.
[114]Cheong C, Tan K, Liu D, et al. Multi-objective and prioritized berth allocation in container ports. Annals of Operations Research,2010,180(1):63-103.
[115]Lim A. The Berth planning problem. Operations Research Letters,1998,22(2-3):105-110.
[116]Park K T, Kim K H. Berth scheduling for container terminals by using a sub-gradient optimization technique. Journal of the Operational Research Society,2002,53(9): 1054-1062.
[117]Kim K H, Moon K C. Berth scheduling by simulated annealing. Transportation Research Part B,2003,37(6):541-560.
[118]Wang F, Lim A. A stochastic beam search for the berth allocation problem. Decision Support Systems,2007,42(4):2186-2196.
[119]Lee D H, Chen J H, Cao J X. The continuous berth allocation problem:a greedy randomized adaptive search solution. Transportation Research Part E,2010,46(6): 1017-1029.
[120]Park Y M, Kim K H. A scheduling method for berth and quay cranes. OR Spectrum,2003, 25(1):1-23.
[121]韩骏,孙晓娜,靳志宏.集装箱码头泊位与岸桥协调调度优化.大连海事大学学报,2008,34(3)：117-121.
[122]杜玉泉,陈秋双,姬晓涛.面向服务的泊位和岸桥联合调度.计算机集成制造系统,2011,17(9)：2051-2060.
[123]Liang C, Guo J, Yang Y. Multi-objective hybrid genetic algorithm for quay crane dynamic assignment in berth allocation planning. Journal of Intelligent Manufacturing,2011,22(3): 471-479.
[124]Ma H, Chan F, Chung S, et al. Maximizing the reliability of terminal service by vessel scheduling and quay crane assignment.2011 IEEE International Conference on Quality and Reliability,2011,85-89.
[125]Beyer H G, Sendhoff B. Robust optimization-a comprehensive survey. Computer Methods in Applied Mechanics and Engineering,2007,196(33-34):3190-3218.
[126]Gao H. Building robust schedules using temporal protection:an empirical study of constraint based scheduling under machine failure uncertainty:[Master's Thesis]. Toronto: University of Toronto,1996.
[127]丁然.不确定条件下鲁棒性生产调度的研究：[博士学位论文].济南：山东大学,2006.
[128]徐亚.集装箱码头作业调度优化模型与算法研究：[博士学位论文].天津：南开大学,2009.
[129]Du Y Q, Xu Y, Chen Q S. A feedback procedure for robust berth allocation with stochastic vessel delays.8th World Congress on Intelligent Control and Automation,2010,2210-2215.
[130]Zhen L, Lee L H, Chew E P. A decision model for berth allocation under uncertainty. European Journal of Operational Research,2011,212(1):54-68.
[131]周鹏飞,康海贵.面向随机环境的集装箱码头泊位-岸桥分配方法.系统工程理论与实践,2008,28(1)：168-169.
[132]曾庆成,胡祥培,杨忠振.集装箱码头泊位分配-装卸桥调度干扰管理模型.系统工程理论与实践,2010,30(11)：2026-2035.
[133]于蒙.基于多Agent的集装箱码头生产调度系统的研究：[博士学位论文].武汉：武汉理工大学,2007.
[134]魏众.集装箱码头物流作业系统集成优化调度研究：[博士学位论文].北京：北京交通大学,2007.
[135]孙彬,孙俊清,陈秋双.基于MAS的集装箱码头物流运作调度管理系统.第30届中国控制会议,2011,5484-5489.
[136]Li B, Li W F, Zhang Y. Study on modeling of container terminal logistics system using agent-based computing and knowledge discovery. International Journal of Distributed Sensor Networks,2009,5(1):36-36.
[137]Ghosh S, Lee T. Modeling and asynchronous distributed simulation:analyzing complex systems. New York:Wiley IEEE Press,2000.
[138]Thurston T, Hu H S. Distributed agent architecture for port automation.26th International Computer Software and Applications Conference,2002,81-87.
[139]Sun B, Sun J Q, Yang P. The design and implementation of berth allocation management system based on MAS.5th International Conference on Natural Computation,2009,5: 593-597.
[140]王刚,陈秋双,杜玉泉.基于组合拍卖的多主体单机调度问题.计算机集成制造系统,2013,19(1)：106-113.
[141]邢文训,谢金星.现代优化计算方法.北京：清华大学出版社,2003.
[142]Kutanoglu E, Wu S D. On combinatorial auction and lagrangean relaxation for distributed resource scheduling. HE Transaction,1999,31(9):813-826.
[143]杜玉泉.绿色背景下的泊位分配问题研究：[博士学位论文].大津：南开大学,2012.
[144]雷桂媛.关于蒙特卡罗及拟蒙特卡罗方法的若干研究：[博士学位论文].杭州：浙江大学，2003.
[145]王刊良,徐寅峰.相异路径选线问题的模型与算法.系统工程理论方法应用,2001,10(1)：8-12.
[146]陈秋双,杜玉泉,徐亚.不确定环境下集装箱码头的扰动管理.物流技术,2010,29(3-4)：1-5.
[2]刘竹芃.天津港集装箱海铁联运研究.中国铁路,2012,(7)：26-28.
[3]港口经理人.http://www.thechoice.com.cn/resource_center/newsletter_display.php.2013(1).
[4]Taylor J C, Jackson G C. Conflict, power, and evolution in the intermodal transportation industry's channel of distribution. Transportation Journal,2000,39 (3):5-17.
[5]Vernimmen B, Dullaert W, Engelen S. Schedule unreliability in liner shipping:origin and consequence for hinterland supply chain. Maritime Economics & Logistics,2007,9(3): 193-213
[6]BunkerWorld Rotterdam. Monthly Bunker Prices,2012. http://www.bunkerworld.com/ prices/port/nl/rtm/.
[7]Olivier J G J, Greet J M, Jeroen A H W P. Trends in global CO2 emissions.2012 Report, PBL Netherlands Environmental Assessment Agency,2012.
[8]梁晓强.航运企业第三方物流绿色供应链实施现状与策略探讨.现代商贸工业,2009,21(023)：8-9.
[9]黄小兵,唐文胜.基于Agent系统的概念,方法和应用.计算机与现代化,2000,4：6-11.
[10]刘宝碇,赵瑞清,王纲.不确定规划及应用.北京：清华大学出版社,2004.
[11]吴平,韩才元.船舶和机车用高速柴油机废气再循环试验.国外内燃机车,2007,(2)：25-31.
[12]张云鹏,李庆祥.轮胎式集装箱门式起重机节能技术研究.交通节能与环保,2008,(2)：9-11.
[13]刘洪波,汪锋,张志平.集装箱轮胎吊”油改电”技术在港口节能减排中的应用.水运工程,2011,(9)：123-125.
[14]鄙克存,戴瑜兴,李加升.一种新型电子静止式岸电电源.电力电子技术,2011,45(2)：86-88.
[15]COSCO. COSCO sustainability report 2010. Beijing:COSCO,2011. http://www.cosco. com/GC_report/GC_report2011/index-cn.html.
[16]Notteboom T E, Vernimmen B. The effect of high fuel costs on liner service configuration in container shipping. Journal of Transport Geography,2009,17(5):325-337
[17]Corbett J J, Wang H F, Winebrake'J J. The effectiveness and costs of speed reductions on emissions from international shipping. Transportation Research Part D:Transport and Environment,2009,14(8):593-598.
[18]Psaraftis H N, Kontovas C A. Balancing the economic and environmental performance of maritime transportation. Transportation Research Part D:Transport and Environment,2010, 15(8):458-462.
[19]Cariou P. Is slow steaming a sustainable means of reducing CO2 emissions from container shipping? Transportation Research Part D:Transport and Environment,2011,16(3): 260-264.
[20]朱小宁.集装箱多式联运通道的规划问题：[博士学位论文].北京：北京交通大学,2000.
[21]马彩雯.多式联运的虚拟企业运作模式研究：[博士学位论文].大连：大连海事大学,2007.
[22]Maurizio B, Azedine B, Mohamed R. Object oriented model for container terminal distributed simulation. European Journal of Operational Research,2006,175(3):1731-1751.
[23]于汝民.现代集装箱码头经营管理.北京：人民交通出版社,2007.8(1).
[24]Agarwal R, Ergun O. Shipping scheduling and network design for cargo routing in liner shipping. Transportation Science,2008,42(2):175-196.
[25]Shintani K, Imai A, Nishimura E, et al. The container shipping network design problem with empty container repositioning. Transportation Research Part C,2007,43(1):39-59.
[26]王琳.不确定环境下的班轮企业资源管理研究：[博士学位论文].天津：南开大学,2010.
[27]Sandra U N, Christian P, Roberto W C. An effective memtic algorithm for the cumulative capacitated vehicle routing problem. Computers & Operations Research,2010,37(11): 1877-1885.
[28]Agostinho A, Marielle C, Rosa F, et al. The robust vehicle routing problem with time windows. Computers & Operations Research,2013,40(3):856-866.
[29]李广儒,杨大奔,任大伟.集卡动态调度路径优化算法.交通运输工程学报,2012,12(3)：86-91.
[30]Heaver T D. The Evolving Roles of Shipping Lines in International Logistics. International Journal of Maritime Economics,2002,4(3):210-230.
[31]孙光圻,闵德权.港航合作的战略价值与策略模式.中国港口,2000,(7)：43-44.
[32]周甫宾,曹蕾.港航合作模式的评述.中国水运,2009,9(5)：53-54.
[33]Yevdokimov Y V. Measuring economic benefits of intermodal transportation. Transportation Law Journal,2000,27(3):439-452.
[34]Handman A L. Intermodalism-a solution for congestion at the millennium. Review of Policy Research,2002,19(2):51-61.
[35]Szyliowicz J S. Decision-making, intermodal transportation, and sustainable mobility: towards a new paradigm. International Social Science Journal,2003,55(2):185-197.
[36]Janic M. Modelling the full costs of an intermodal and road freight transport network. Transportation Research Part D,2007,12(1):33-44.
[37]魏际刚,荣朝和.中国集装箱多式联运发展的宏观经济因素分析.中国软科学,2000,(8)：39-44.
[38]肖平安.我国国际集装箱多式联运之探讨.交通企业管理,2006,21(8)：46-47.
[39]郭琴.我国多式联运发展的若干思考.物流工程与管理,2010,32(4)：3-4.
[40]Arnold P, Peeters D, Thomas I. Modelling a rail/road intennodal transportation system. Transportation Research Part E,2004,40(3):255-270.
[41]汪涛,朱晓宁,马金元.三级网络结构的铁路集装箱一体化服务体系设计.物流技术,2009,28(10)：45-47.
[42]洪雁.铁路集装箱运输系统规划若干问题研究：[博士学位论文].北京：北京交通大学,2008.
[43]Imai A, Nishimura E, Hattori M, et al. Berth allocation at indented berths for mega-containerships. European Journal of Operational Research,2007,179(2):579-593.
[44]Imai A, Chen H C, Nishimura E, Papadimitriou S. The simultaneous berth and quay crane allocation problem. Transportation Research Part E,2008,44(5):900-920.
[45]Xu D S, Li C L, Leung J Y T. Berth allocation with time-dependent physical limitations on vessels. European Journal of Operational Research,2012,216(1):47-56.
[46]Bazzazi M, Safael N, Javadian N. A genetic algorithm to solve the storage space allocation peoblem in a container terminal. Computer & Industrial Engineering,2009,56(1):44-52.
[47]Xu Y, Chen Q, Quan X. A robust integrated approach to yard space allocation and crane scheduling in container terminals.7th International Conference on Service Systems and Service Management,2010,28-30.
[48]Sharif O, Huynh N. Storage space allocation at marine container terminals using ant-based control. Expert Systems with Applications,2013,40(6):2323-2330.
[49]Choi H R, Park B K, Lee J W, et al. Dispatching of container trucks using genetic algorithm. 4th International Conference on Interaction Sciences,2011,146-151.
[50]Zheng X J, Shu F, Mi W J. Research on genetic algorithm-based simulation of dynamic container truck scheduling. IEEE International Conference on Computer Science and Automation Engineering,2012, (2):131-135.
[51]Angelica L, Giovanni S. Shortest viable path algorithm in multimodal networks. Transportation Research Part A,2001,35(3):225-241.
[52]Delling D, Pajor T, Wagner D. Accelerating multi-modal route planning by access-node. Proceedings of the 17th Annual European Symposium on Algorithms,2009,587-598.
[53]康凯,牛海姣,朱越杰.基于粒子群蚁群算法求解多式联运中运输方式与运输路径集成优化问题.物流工程与管理,2009,31(10)：61-65.
[54]Chang T S. Best routes selection in international intermodal networks. Computers & Operations Research,2008,35(9):2877-2891.
[55]佟璐,聂磊,付慧玲.多式联运路径优化模型与方法研究.物流技术,2010,29(3)：57-60.
[56]魏航,李军,蒲云.时变网络下多式联运的最短路径问题研究.系统工程学报,2007,22(2)：205-209.
[57]刘杰,何世伟,宋瑞.基于运输方式备选集的多式联运动态路径优化研究.铁道学报,2011,33(10)：1-6.
[58]张洁,高亮,李培根.多Agent技术在先进制造中的应用.北京：科技出版社,2004.
[59]Wang SL, Xia H, Liu F. Agent-based modeling and mapping of a manufacturing system. Journal of Materials Processing Technology,2002,129(3):518-523.
[60]李敬华,刘文剑,金天国.基于多Agent的多型号生产调度系统研究.计算机集成制造 系统,2006,12(4)：573-578.
[61]Goldsmith S Y, Phillips L R, Spires S V. A multi-agent system for coordinating international shipping. Agent Mediated Electronic Commerce,1999,91-104.
[62]Sinha-Ray P, Carter J, Field T, et al. Container World:Global agent-based modelling of the container transport business. Proceedings 4th Workshop on Agent-Based Simulation,2003.
[63]杨华龙,东方,郑斌.集装箱航运服务多属性组合拍卖模型与算法.交通运输工程学报,2009,9(5)：111-115.
[64]Robu V, Noot H, Poutre H L, et al. An interactive platform for auction-based allocation of loads in transportation logistics. Proceedings of the 7th international joint conference on Autonomous Agents and Multiagent Systems,2008,3-10.
[65]Robu V, Poutre H L. Designing bidding strategies in sequential auctions for risk averse agents. Multiagent and Grid Systems-Advances in Agent-mediated Automated Negotiations, 2010,6(5):437-457.
[66]Robu V, Noot H, Poutre H L, et al. A multi-agent platform for auction-based allocation of loads in transportation logistics. Expert Systems with Applications,2011,38(4):3483-3491.
[67]Dong J W, Li Y J. Agent-based design and organization of intermodal freight transportation systems. International Conference on Machine Learning and Cybernetics,2003,4: 2269-2274.
[68]Dullaert W, Neutens T, Berghe G V, et al. MamMoeT:an intelligent agent-based communication support platform for multimodal transport. Expert Systems with Applications, 2009,36(7):10280-10287.
[69]马彩雯,孙光圻.基于多Agent的国际多式联运虚拟企业信息集成框架.大连海事大学学报,2006,32(2)：71-74.
[70]张戎,闫攀余.基于多Agent的集装箱海铁联运信息系统模型.同济大学学报,2007,35(1)：72-76.
[71]马彩雯.基于多Agent的多式联运各区段分运承运人选择.哈尔滨工业大学学报,2007,39(12)：1989-1992.
[72]Xiong G W, Wang Y. Research on job integration of multi-agent in multimodal transportation with time windows. International Journal of Services, Economics and Management,2010, 2(3):307-321.
[73]Monteiro T, Roy D, Anciaux D. Multi-site coordination using a multi-agent system. Computers in Industry,2007,58(4):367-377.
[74]Kim H S, Cho J H. Supply chain formation using agent negotiation. Decision Support Systems,2010,49(1):77-90.
[75]Wang M H, Liu J M, Wang H Q, et al. On-demand e-supply chain integration:a multi-agent constraint-based approach. Expert Systems with Applications,2008,34(4):2683-2692.
[76]Dong J X, Song D P. Container fleet sizing and empty repositioning in liner shipping systems. Transportation Research Part E,2009,45(6):860-877.
[77]Song D P, Carter J. Empty container repositioning in shipping industry. Maritime Policy & Management,2009,36(4):291-307.
[78]王琳,陈秋双,张瑞玲.面向不确定环境的集装箱空箱鲁棒优化调度.第29届中国控制会议,2010,1791-1796.
[79]王斌,唐国春.海运集装箱调运随机优化模型.交通运输系统工程与信息,2010,10(3)：58-63.
[80]Sumalee A, Uchida K, Lam W H K. Stochastic multi-modal transport network under demand uncertainties and adverse weather condition. Transportation Research Part C,2011,19(2): 338-350.
[81]Berit D B, Jakob D, David P, et al. The vessel schedule recovery problem(VSPR)-a mip model for handling disruptions in liner shipping. European Journal of Operational Research, 2013,224(2):362-374.
[82]Notteboom T E. The time factor in liner shipping services. Maritime Economics & Logistics, 2006,8:19-39.
[83]Hendriks M, Laumanns M, Lefeber E, et al. Robust cyclic berth planning of container vessels. OR Spectrum.2010,32(3):501-517.
[84]Han X L, Lu Z Q, Xi L F. A proactive approach for simultaneous berth and quay crane scheduling problem with stochastic arrival and handling time. European Journal of Operational Research,2010,207(3):1327-1340.
[85]李延晖,马士华,刘黎明.基于时间约束的供应链配送系统随机模型.预测,2004,23(4)：45-48.
[86]程赐胜,陈宝星,关仕罡.商品配送中车辆调度随机模型的建立及其求解.系统工程,2004,22(4)：66-70.
[87]Psaraftis H N, Kontovas C A. CO2 emission statistics for the world commercial fleet. WMU Journal of Maritime affairs,2009,8(1):1-25.
[88]Alvarez J F. Joint routing and deployment of a fleet of container vessel. Maritime Economists & Logistics,2009,11(2):186-208.
[89]Fagerholt K, Laporte G, Norstad I. Reducing fuel emissions by optimizing speed on shipping routes. Journal of the Operational Research Society,2010,61(3):523-529.
[90]Norstad I, Fagerholt K, Laporte G. Tramp ship routing and scheduling with speed optimization. Transportation Research Part C:Emerging Technologies,2011,19(5):853-865.
[91]Ronen D. The effect of oil price on containership speed and fleet size. Journal of the Operational Research Society,2011,62(1):211-216.
[92]Qi X T, Song D P. Minimizing fuel emissions by optimizing vessel schedules in liner shipping with uncertain port times. Transportation Research Part E,2012,48(4):863-880.
[93]Golias M M, Saharidis G K, Boile M, et al. The berth allocation problem:optimizing vessel arrival time. Maritime Economics & Logistics,2009,11(4):358-377.
[94]Lang N, Veenstra A. A quantitative analysis of container vessel arrival planning strategies. OR Spectrurn,2010,32(3):477-499.
[95]Du Y Q, Chen Q S, Quan X W, et al. Berth allocation considering fuel consumption and vessel emissions. Transportation Research Part E:Logistics and Transportation Review,2011, 47(6):1021-1037.
[96]Bektas T, Laporte G. The pollution-routing problem. Transportation Research Part B,2011, 45(8):1232-1250.
[97]Suzuki Y. A new truck-routing approach for reducing fuel consumption and pollutants emission. Transportation Research Part D,2011,16(1):73-77.
[98]Figliozzi M A. The impacts of congestion on time-definitive urban freight distribution networks CO2 emission levels:results from a case study in Portland, Oregon. Transportation Research Part C,2011,19 (5):766-778.
[99]Jabali O, Woensel T, Kok A G. Analysis of travel times and CO2 emissions in time-dependent vehicle routing. Production and Operations Management,2012,21(6):1060-1074.
[100]Xiao Y, Zhao Q, Kaku I, et al. Development of a fuel consumption optimization model for the capacitated vehicle routing problem. Computers and Operations Research,2012,39(7): 1419-1431.
[101]Huang Y, Shi C, Zhao L, et al. A study on carbon reduction in the vehicle routing problem with simultaneous pickups and deliveries.2012 IEEE International Conference on Service Operations and Logistics and Informatics,2012,302-307.
[102]Kim K H, Park Y M. A crane scheduling method for port container terminals. European Journal of Operational Research,2004,156(3):752-768.
[103]Moccia L, Cordeau J F, Gaudioso M, et al. A branch-and-cut algorithm for the quay crane scheduling problem in a container terminal. Naval Research Logistics,2006,53(1):45-59.
[104]Li C L, Cai X Q, Lee C Y. Scheduling with multiple-job-on-one-processor pattern. HE Transactions,1998,30(5):433-445.
[105]Guan Y, Xiao W Q, Cheung R K, et al. A multiprocessor task scheduling model for berth allocation:heuristic and worst-case analysis. Operations Research Letters,2002,30(5): 343-350.
[106]Song L, Cherrett T, Guan W. Study on berth planning problem in a container seaport:using an integrated programming approach. Computers & Industrial Engineering,2012,62(1): 119-128.
[107]Monaco M F, Sammarra M. The berth allocation problem:a strong formulation solved by a lagrangean approach. Transportation Science,2007,41(2):265-280.
[108]Lai K K, Shih K. A study of container berth allocation. Journal of Advanced Transportation, 1992,26(1):45-60.
[109]Imai A, Nishimura E, Papadimitriou S. The dynamic berth allocation problem for a container port. Transportation Research Part B,2001,35(4):401-417.
[110]Nishimura E, Imai A, Papadimitriou S. Berth allocation planning in the public berth system by genetic algorithms. European Journal of Operational Research,2001,131(2):282-292.
[111]Imai A, Nishimura E, Papadimitriou S. Berth allocation with service priority. Transportation Research Part B,2003,37(5):437-457.
[112]韩笑乐,陆志强,奚立峰.具有服务优先级别的动态离散泊位调度优化.上海交通大学学报,2009,43(6)：902-905.
[113]Cordeau J F, Laporte G, Legato P, et al. Models and tabu search heuristics for the berth-allocation problem. Transportation Science,2005,39(4):526-538.
[114]Cheong C, Tan K, Liu D, et al. Multi-objective and prioritized berth allocation in container ports. Annals of Operations Research,2010,180(1):63-103.
[115]Lim A. The Berth planning problem. Operations Research Letters,1998,22(2-3):105-110.
[116]Park K T, Kim K H. Berth scheduling for container terminals by using a sub-gradient optimization technique. Journal of the Operational Research Society,2002,53(9): 1054-1062.
[117]Kim K H, Moon K C. Berth scheduling by simulated annealing. Transportation Research Part B,2003,37(6):541-560.
[118]Wang F, Lim A. A stochastic beam search for the berth allocation problem. Decision Support Systems,2007,42(4):2186-2196.
[119]Lee D H, Chen J H, Cao J X. The continuous berth allocation problem:a greedy randomized adaptive search solution. Transportation Research Part E,2010,46(6): 1017-1029.
[120]Park Y M, Kim K H. A scheduling method for berth and quay cranes. OR Spectrum,2003, 25(1):1-23.
[121]韩骏,孙晓娜,靳志宏.集装箱码头泊位与岸桥协调调度优化.大连海事大学学报,2008,34(3)：117-121.
[122]杜玉泉,陈秋双,姬晓涛.面向服务的泊位和岸桥联合调度.计算机集成制造系统,2011,17(9)：2051-2060.
[123]Liang C, Guo J, Yang Y. Multi-objective hybrid genetic algorithm for quay crane dynamic assignment in berth allocation planning. Journal of Intelligent Manufacturing,2011,22(3): 471-479.
[124]Ma H, Chan F, Chung S, et al. Maximizing the reliability of terminal service by vessel scheduling and quay crane assignment.2011 IEEE International Conference on Quality and Reliability,2011,85-89.
[125]Beyer H G, Sendhoff B. Robust optimization-a comprehensive survey. Computer Methods in Applied Mechanics and Engineering,2007,196(33-34):3190-3218.
[126]Gao H. Building robust schedules using temporal protection:an empirical study of constraint based scheduling under machine failure uncertainty:[Master's Thesis]. Toronto: University of Toronto,1996.
[127]丁然.不确定条件下鲁棒性生产调度的研究：[博士学位论文].济南：山东大学,2006.
[128]徐亚.集装箱码头作业调度优化模型与算法研究：[博士学位论文].天津：南开大学,2009.
[129]Du Y Q, Xu Y, Chen Q S. A feedback procedure for robust berth allocation with stochastic vessel delays.8th World Congress on Intelligent Control and Automation,2010,2210-2215.
[130]Zhen L, Lee L H, Chew E P. A decision model for berth allocation under uncertainty. European Journal of Operational Research,2011,212(1):54-68.
[131]周鹏飞,康海贵.面向随机环境的集装箱码头泊位-岸桥分配方法.系统工程理论与实践,2008,28(1)：168-169.
[132]曾庆成,胡祥培,杨忠振.集装箱码头泊位分配-装卸桥调度干扰管理模型.系统工程理论与实践,2010,30(11)：2026-2035.
[133]于蒙.基于多Agent的集装箱码头生产调度系统的研究：[博士学位论文].武汉：武汉理工大学,2007.
[134]魏众.集装箱码头物流作业系统集成优化调度研究：[博士学位论文].北京：北京交通大学,2007.
[135]孙彬,孙俊清,陈秋双.基于MAS的集装箱码头物流运作调度管理系统.第30届中国控制会议,2011,5484-5489.
[136]Li B, Li W F, Zhang Y. Study on modeling of container terminal logistics system using agent-based computing and knowledge discovery. International Journal of Distributed Sensor Networks,2009,5(1):36-36.
[137]Ghosh S, Lee T. Modeling and asynchronous distributed simulation:analyzing complex systems. New York:Wiley IEEE Press,2000.
[138]Thurston T, Hu H S. Distributed agent architecture for port automation.26th International Computer Software and Applications Conference,2002,81-87.
[139]Sun B, Sun J Q, Yang P. The design and implementation of berth allocation management system based on MAS.5th International Conference on Natural Computation,2009,5: 593-597.
[140]王刚,陈秋双,杜玉泉.基于组合拍卖的多主体单机调度问题.计算机集成制造系统,2013,19(1)：106-113.
[141]邢文训,谢金星.现代优化计算方法.北京：清华大学出版社,2003.
[142]Kutanoglu E, Wu S D. On combinatorial auction and lagrangean relaxation for distributed resource scheduling. HE Transaction,1999,31(9):813-826.
[143]杜玉泉.绿色背景下的泊位分配问题研究：[博士学位论文].大津：南开大学,2012.
[144]雷桂媛.关于蒙特卡罗及拟蒙特卡罗方法的若干研究：[博士学位论文].杭州：浙江大学，2003.
[145]王刊良,徐寅峰.相异路径选线问题的模型与算法.系统工程理论方法应用,2001,10(1)：8-12.
[146]陈秋双,杜玉泉,徐亚.不确定环境下集装箱码头的扰动管理.物流技术,2010,29(3-4)：1-5.