基于($D,U$)控制策略的港口群空箱存储与调运联合优化

针对区域港口群内陆公共腹地的空箱供需状况,构建多港口、多周期空箱存储与调运联合优化混合整数规划模型,利用马尔科夫决策过程与动态规划相结合的方法,在(D,U)库存控制策略下对港口群内各港口的空箱库存保有量区间进行动态优化,以此为基础对港口群公共腹地和各港口间的空箱调运方案进行优化,并以辽宁沿海港口群-----东北腹地为对象进行实证分析.实验结果表明,(D,U)控制策略下的空箱调运能够为船公司节省15.22%的总成本.选取公共腹地数量、公共腹地和港口空箱需求量、单位租箱成本以及单位库存成本进行灵敏度分析,进而验证(D,U)控制策略能够削弱船公司在外界环境发生变化时受到的影响.     

集装箱海运收益管理超订模型研究

分别建立了不考虑空箱调运和考虑空箱调运的超订模型,通过比较说明了空箱调运对超订运作策略的影响,最后给出了模型的算例。     

基于遗传算法的海运集装箱空箱调运成本优化

根据港口集装箱调运的特点,对空箱调运的问题进行分析,考虑租箱成本、运输成本、装卸成本和调运约束等条件,以最小化调运成本为目标,建立在保证重箱运输前提下的空箱调运的非线性整数模型.文中使用简单遗传算法和加入遗传算子的遗传算法分别对模型实例进行求解,实验表明,当采用加入遗传算子的遗传算法时获得的结果比采用简单遗传算法的更好.     

考虑潮汐影响的班轮多船型船舶调度

针对班轮企业由于提前公布船期表，但受货运需求的波动和潮汐的影响引起的多船型船舶调度问题进行研究。首先系统分析了一家班轮企业近洋运输航线结构；然后考虑大型船舶需乘潮进出港口，以及适当条件下允许租船的实际情况，兼顾班轮船期表的限制，构建了以运输总成本最小为目标的班轮多船型船舶调度非线性规划模型；最后考虑模型的特点，设计了嵌入基因修复的改进遗传算法（IGA）用于模型求解。实验结果表明，与传统的经验调度方案相比，得到的船舶调度方案在船舶利用率上能提高25%~35%；中规模算例下与CPLEX相比，IGA的CPU处理时间平均降低77%；中、大规模算例下与蚁群算法相比，IGA计算的运输费用平均降低15%。实验结果验证了所提模型和算法的有效性，可为班轮企业船舶调度提供参考。     

集装箱海运空箱分派随机规划模型研究*

通过对海运集装箱空箱分派过程的仔细分析,在考虑需求不确定性的基础上引入了空箱运输能力的不确定性这一重要因素,建立了同时考虑供需平衡约束以及需求和空箱运输能力不确定性的空箱分派随机规划模型,并应用机会约束规划方法对模型求解.最后通过数值仿真,揭示了运输成本、租箱成本和存储成本等参数和空箱运输能力的不确定性对集装箱空箱分派策略的影响机制.     

移动边缘计算中基于粒子群优化的计算卸载策略

计算卸载作为移动边缘计算（MEC）中降低时延与能耗的手段之一,通过合理的卸载决策能够降低工业成本。针对工业生产线中部署MEC服务器后时延变长和能耗增高的问题,提出了一种基于粒子群优化（PSO）算法的计算卸载策略PSAO。首先,将实际问题建模为时延模型与能耗模型。由于是针对时延敏感型的应用,因此将模型转化为在能耗约束条件下的最小化时延问题,使用惩罚函数来平衡时延与能耗。其次,根据PSO算法优化后得到计算卸载决策向量,通过集中控制的方式使每一个计算任务合理分配到对应的MEC服务器。最后,通过仿真实验,对比分析了本地卸载策略、MEC基准卸载策略、基于人工鱼群算法（AFSA）的卸载策略以及PSAO的时延数据,PSAO的平均总时延远远低于其他三种卸载策略,PSAO比原来系统总代价降低了20%。实验结果表明,PSAO策略能够降低MEC中的时延,均衡MEC服务器的负载。     

基于CVaR的电网企业代理购电交易模型的研究

为推进电网企业代理购电工作,本文提出了一种基于条件风险价值（Conditonl Value at Risk,CVaR）的电网企业代理购电交易策略风险评价和控制模型。分析了电网企业代理购电业务的开展形式和服务,并以江苏省电力市场为例分析了电力交易的多年（年度）、月度、月内市场的价格波动风险,考虑到电力市场价格波动剧烈和明显的尖峰厚尾特征,建立了代理购电业务交易策略CVaR模型,并利用蒙特卡罗模拟法实现求解最优策略。利用数值算例验证了该模型的有效性,结果表明其可以降低CVaR和在险价值（Value at Risk,VaR）两个风险指标,有效降低了风险。     

支持业务过程重组和信息系统重构的企业建模

本文首先阐述了企业建模,业务过程重组（BRP）和信息系统重构的概念,并提出了企业建模与BPR以及信息系统重构之间的关系。然后通过详细分析传统企业建模方法的不足提出了“支持业务过程重组和信息系统重构的企业建模”的概念,并详细阐述了支持业务过程重组和信息系统重构的企业模型的主要内容及其建模体系,最后还介绍了基于上述模型的企业业务过程重组及信息系统重构的过程和步骤。     

基于主动推送技术的网络流媒体业务承载模型

在分析网络流媒体业务特征和传统网络互联网络优缺点的基础上,通过引入主动网络技术与DiffServ技术,提出了一种基于主动推送技术的网络流媒体业务承载模型,并给出了该模型在国家“863”高性能宽带信息网（3TNet）中的方案。     

基于WNegNodeset结构的加权频繁项集挖掘算法

针对基于WN-list 加权频繁项集挖掘算法（NFWI）中挖掘加权频繁项集（FWI）效率低的问题,提出了一种基于WNegNodeset结构的加权频繁项集挖掘算法（NegNFWI）。该算法首先采用了新的数据结构WNegNodeset,它是NegNodeset的扩展,该数据结构采用了一种新的基于集合位图表示的位图加权树（BMW-tree）节点编码模型,通过按位运算符快速提取WNegNodeset的节点集,避免了大量的交集运算;其次采用了差集策略快速计算项集的加权支持度,从而减少了计算量;最后通过仿真实验验证了算法的有效性和可行性。     

Application of a mixed fuzzy decision making and optimization programming model to the empty container allocation

Containerization transportation has been growing fast in the past few decades. International trades have been growing fast since the globalization of world economies intensified in the early 1990s. However, these international trades are typically imbalanced in terms of the numbers of import and export containers. As a result, the relocation of empty containers has become one of the important problems faced by liner shipping companies. In this paper, we consider the empty container allocation problem where we need to determine the optimal volume of empty containers at a port and to reposition empty containers between ports to meet exporters’ demand over time. We formulate this empty container allocation problem as a two-stage model: in stage one, we propose a fuzzy backorder quantity inventory decision making model for determining the optimal quantity of empty container at a port; whereas in stage two, an optimization mathematical programming network model is proposed for determining the optimal number of empty containers to be allocated between ports. The parameters such as the cost of loading container, cost of unloading container, leasing cost of empty container, cost of storing container, supplies, demands and ship capacities for empty containers are considered in this model. By taking advantages of the fuzzy decision making and the network structure, we show how a mixed fuzzy decision making and optimization programming model can be applied to solve the empty container allocation problem. The utilization of the proposed model is demonstrated with a case of trans-Pacific liner route in the real world. Six major container ports on the trans-Pacific route are considered in the case study, including the Port of Kaohsiung, the Port of Hong Kong, the Port of Keelung, the Port of Kobe, the Port of Yokohama and the Port of Los Angles. The results show that the proposed mixed fuzzy decision making and optimization programming model can be used to solve the empty container allocation problem well.     

Understanding Port Choice Behavior—A Network Perspective

A novel Network-based Integrated Choice Evaluation (NICE) model is developed to enhance the multinomial logit preference (MNL) model that is widely employed in the existing port choice literature. The NICE model integrates the element of port service network with observational port attributes to identify important quality characteristics on which liner shipping companies base their port choices. An empirical study of the proposed model is conducted through the service schedules of three established liner shipping companies. Results show that port efficiency and scale economies are the more important dimensions influencing liner shipping companies’ selection of major Asian ports. Nevertheless, it is important for a competitive port to balance its efforts among all the dimensions.     

Pricing and balancing of the sea–cargo service chain with empty equipment repositioning

To sustain a business, firms have to reposition empty containers from a surplus port to a port with a shortage and incur repositioning costs if the realized demands are unbalanced in the sea–cargo service chain. In this paper, we study a sea cargo service chain with one carrier and two forwarders providing transportation service between two ports, and there are potential demands for transportation services in both directions. We built a mathematical model to study how the carrier and forwarders determine pricing and empty equipment repositioning (hereafter EER) decisions. We find that whether or not the carrier and forwarders use pricing policies to balance the cargo demands depends on the potential demand imbalance volume between two ports. We also investigate the EER sharing strategy on whether to share the EER cost or undertake it solely. It is found that there exists a threshold, and when the EER cost is beyond the threshold value, the carrier assumes all of the repositioning costs; otherwise, the forwarder assumes all of the repositioning costs. Lastly, we study a subsidy contract between both forwarders and expand to study an EER sharing mode between the carrier and both forwarders.     

Application of a combined fuzzy multiple criteria decision making and optimization programming model to the container transportation demand split

The container transportation demand split is one of the most important decision issues for government transportation departments and port organizations. In previous studies, many researchers assumed that the shipping carrier would aim to minimize the total operation cost by selecting an appropriate port as the most favorable one to call, and the shipper would aim to minimize the inland freight cost by selecting the nearest port as the most favorable one to import and export international trade containers. Thus, a number of mathematical programming models have been developed. But in practice, the shipping carrier not only aims to minimize the total operation cost but also takes into account other criteria such as the volume of containers and port facility conditions when choosing an appropriate port as the most favorable one to call. The shipper not only aims to minimize the inland freight cost but also takes into account the frequency of ship callings when choosing an appropriate port as the most favorable one to import and export international trade containers.Thus, the purpose of this paper is to formulate a combined fuzzy multiple criteria decision making and optimization programming model for solving the container transportation demand split problem. There are two stages in this combined model: in stage one, we first compute the container transportation demand split rate by using fuzzy multiple criteria decision making (MCDM) method; whereas in stage two, an optimization mathematical programming network model is proposed for determining the inland origin destination (O-D) of import/export containers. The utilization of the proposed model is demonstrated with a case of Taiwanese ports. The results show that the proposed combined fuzzy MCDM and optimization programming model can be used to explain the container transportation demand split practice.     

Collaborative mechanisms for berth allocation

This paper proposes two collaborative mechanisms between container shipping lines and port operators to facilitate port operators to make proper berth allocation decisions. In the first mechanism, assuming no transshipment, a shipping line needs to provide the port operator with the utilities associated with the start operation days of each liner route. The total utilities for all start operation days must be 0. A higher bunker and inventory cost for the shipping line means a lower utility. The port operator compensates the shipping line if its ship is scheduled on a day with negative utility and charges additional fees if the ship is scheduled on a day with positive utility. The second mechanism accounts for the utilities related to the inventory cost of transshipment containers. These two mechanisms ensure that shipping lines have no incentive to overstate or undervalue the utilities. The utilities estimated by shipping lines are much more accurate than those estimated by port operators. Hence, models for the tactical berth allocation problem incorporating the utilities provided by shipping lines lead to more efficient and equitable berth allocation plans. The utilities provided by shipping lines can also guide the decisions on operational berth allocation.     

Positioning empty containers under dependent demand process

Owing to trade imbalance, shipping companies position empty containers between ports or depots periodically. The most difficult problem for positioning is that it is not possible to know the exact amounts of empty containers required in the future. The paper deals with the problem of positioning empty containers in a port area with multiple depots. Customer demands and returning containers in depots per unit time period are assumed to be serially-correlated and dependent random variables. Three options are considered to prepare the required extent of positioning: positioning from other overseas ports, inland positioning between depots, and leasing. The policies for empty-container management consist of three parts as follows: a coordinated, (s, S) inventory policy for positioning from other ports, (r_i, R_i) policy for inland positioning between depots; and a simple leasing policy with zero lead-time. For inland positioning policy, four heuristic methods are proposed to reposition empty containers between depots. The objective is to obtain optimal policies corresponding to different methods of inland positioning in order to minimize the expected total costs. A genetic-based optimization procedure is developed to find the optimal parameters (s, S) and (r_i, R_i). Some numerical examples and sensitivity analyses are given to demonstrate the results.     

Schedule design for sustainable container supply chain networks with port time windows

This paper studies a practical liner shipping schedule design problem with port time windows for container supply chain networks. A mixed-integer nonlinear non-convex model that incorporates the availability of ports is proposed to minimize the sum of ship cost and fuel cost (and thereby pollutant emission). In view of the structure of the problem, we reformulate it as an integer linear optimization model and propose an iterative optimization approach. The proposed solution method is applied to two liner networks operated by a global shipping line.     

考虑港口拥堵因素的沿海海运网络优化

沿海运输权制度是沿海运输是否保留给本国船舶或者向外国籍船舶开放的制度。港口拥堵主要发生在海运网络的枢纽港上。通过考虑沿海运输权和港口拥堵对轴辐式海运网络加以优化,以海运网络内货物运输总成本最小化为目标函数,构建一个小规模整数规划模型。从欧亚航线覆盖的主要地区中选取10个港口组成海运网络进行算例分析,并使用CPLEX软件进行求解,研究结果表明,通过考虑枢纽港间货物运输的规模经济效应和产生的拥堵成本,可合理地确定枢纽港的位置;若允许沿海捎带,外资航运企业将会改变货物中转的港口。     

考虑恶劣天气的班轮多阶段重调度方法

受恶劣天气影响的船舶调度是一个非常复杂的优化问题,也是班轮公司重点关注的问题之一。为此,针对某航运网络上的一家班轮公司的所有营运船舶,以获知设计的多阶段重调度机制时段内最新预报的天气信息和这些船舶的实时位置为前提,重点考虑班轮船期表的限制并兼顾港口间航速变化和船舶容量等现实约束,构建了以固定计划期内所有船舶的航运总成本最小为优化目标的非线性数学模型,并设计了嵌入基因修复算子的改进遗传算法用于求解该模型。由此,可以给出集成租船直运、跨航线调船、反挂和货物中转等解决策略的最佳多阶段重调度方案。通过对大、中、小规模的算例进行实验,实验结果表明,可知与传统等待办法相比,多阶段重调度节约了总航运成本的15%以上,验证了所提模型和方案的有效性;与Cplex相比,改进遗传算法的运算效率大大提高,且偏差值均在5%以内,而与蚁群优化（ACO）算法、禁忌搜索（TS）算法、量子差分进化（QDE）算法相比,改进遗传算法能在有效时间内降低10%左右的成本,验证了算法的科学性。所提方法可为班轮公司的实际船舶调度提供参考。