跨区域远洋航线空集装箱动态调运优化模型

由于海运业受世界经济不景气的影响,班轮企业竞争日益加剧。尽可能地降低营运成本是班轮企业生存和发展的关键因素。提出了跨区域远洋空集装箱动态调运优化模型（ECR-RDM）,该模型一方面针对传统模型（ECR-TM）基于港口间点到点空箱调运策略的缺陷,采用了基于港口集合到集合的空箱调运策略。另一方面,不同于传统模型解决空箱调运的静态分配特征,提出的模型可以根据实时需要动态分配空箱,一定程度上减少了由于调运计划和实际业务需求不同步性产生的各项成本。通过抽取实际业务中可能发生的六种情况,依次进行分析比较,验证了文中提出模型的有效性和可行性。同时,通过分析在可能的六种情况下租赁成本、存储成本、装卸成本在总调运成本中所占的比例,为班轮企业制定高效的运营策略提供决策依据。     

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。     

基于成长算子的改进遗传算法及仿真

模拟生物界成长发育过程,加入成长算子对遗传算法框架进行改进,形成新的算法框架-成长遗传算法(growth GA)．该算法能够克服简单遗传算法寻优速度较慢、局部搜索能力较弱的缺点．利用爬山法局部搜索能力强的特点,给出成长算子的一种具体实现,并证明加入成长算子不改变算法收敛性．与简单遗传算法和确定性拥挤遗传算法的对比函数优化实验证明:成长遗传算法有利于兼顾寻优速度和收敛精度．     

利用改进的自适应遗传算法确定有机化合物分子式

针对简单遗传算法存在早熟和收敛速度慢的缺点,提出了一种改进的自适应遗传算法用来确定有机化合物分子式.在现有自适应遗传算法的基础上,对编码方式和遗传算子操作等几方面进行了改进.改进后的自适应遗传算法在防止早熟和加快收敛方面优于简单遗传算法.该算法应用到确定有机化合物分子式时,取得很好的效果.     

知识引导遗传算法实现机器人路径规划

针对传统遗传算法求解机器人路径规划问题存在的收敛速度较慢的缺陷,设计一种知识引导遗传算法,在染色体的编码、初始种群的产生、各种遗传算子和优化算子中加入相关的领域知识.综合考虑机器人路径的长度、安全度和平滑度等性能指标,在对机器人进行路径规划的同时,利用删除、简化、修正和平滑4种优化算子进行路径优化操作.仿真结果表明,所提方法能够有效提高遗传算法求解实际路径规划问题的能力和效率.     

基于遗传算法的图像聚类设计

遗传算法是模拟达尔文的遗传选择和自然淘汰的生物进化过程的搜索最优解方法。作为一种新的全局优化搜索算法,遗传算法以其简单通用、适于并行处理以及高效、实用等显著特点,在各个领域得到了广泛应用。遗传算子的实现方法如选择算子、交叉算子和变异算子以及评估函数、适应度函数的设定方法等是遗传算法的重要组成部分,运用遗传算法可以进行问题求解,从而可以运用遗传算法解决聚类问题。因此,在图像处理、自动控制等方面可以充分利用遗传算法有效地解决图像聚类设计问题。     

具有自适应选择策略的改进遗传算法

遗传算法是一种进行全局寻优的有效方法,是进化计算中最受关注的算法之一.基本遗传算法采用选择、交叉、变异等算子对问题的解空间进行搜索,具有结构简单、实现方便、鲁棒性强等特点,在许多领域得到了应用.介绍了遗传算法的原理与实现方法,讨论了基本遗传算法的遗传算子以及具有重要意义的模式定理.在基本遗传算法的基础上,提出了对选择算子的改进方法,即在选择操作时增加了自适应的优选和淘汰机制,以便提高进入交配池个体的适应值.仿真结果表明,改进后的遗传算法能有效地提高群体的平均适应值,加快最佳个体的进化速度.     

基于多智能体的新型遗传算法及其在复杂系统中的应用研究

通过对简单遗传算法及其研究现状的分析研究，及对复杂系统与简单遗传 算法特点的比较分析，针对简单遗传算法的不足，提出了一种基于多智能体的新型遗传算法，定义了新型遗传算法中的环境、智能体结构、遗传算子、目标/评估函数和流程图，最后用一个测试函数和复杂环境下的多峰函数对它进行了验证分析. 结果表明，新型遗传算法具有明显的优点和优势，特别适合于复杂系统中的问题求解．     

约束优化问题的改进遗传算法设计

遗传算子是影响遗传算法优化效果的重要因素,针对目前遗传算法研究中对约束优化问题求解的不足,提出基于退火思想的退火选择算子和加权适应度算子,并给出了退火选择算子和加权适应度算子设计方法及其计算过程.在此基础上与现有的遗传算子结合,提出一种新的改进遗传算法,分析了改进遗传算法与基于罚函数遗传算法之间在原理上的区别.最后以两个测试函数为算例对算法进行了性能测试,结果表明改进的遗传算法具有良好的优化性能,能获得更好的优化结果.     

Empty container repositioning strategy in intermodal transport with demand switching

In this paper, we study the empty container inventory repositioning problem with customer demand switching in intermodal transport. The objective of this article is to solve the empty container repositioning problem by contract coordination theory, and to improve the coordination of empty container management and the profit of each participant. We consider an intermodal transport system composed of the rail firm and the liner firm. First, we have considered the situation of no cooperation between the dry port and the seaport, and established a model where there is only the customer demand switching without the occurrence of empty container repositioning. Next, we consider the cooperation between the dry port and the seaport in the decentralized model and the centralized model, and set up the empty container repositioning models from the seaport to the dry port respectively. We analyse the optimal inventory level for the dry port and the seaport under different models, and the effect of the repositioning price on the optimal inventory level. We then apply the contract coordination theory to the empty container inventory repositioning problem. We propose an inventory coordination strategy based on a revenue sharing contract and coordinate the intermodal transport system by choosing the appropriate contract parameters. The results of the study show that under the guidance of the seaport, the revenue sharing contract can achieve a win-win situation for the dry port and the seaport.     

A recursive model for static empty container allocation

Backlogged empty containers have gradually turned into a serious burden to shipping networks. Empty container allocation has become an urgent settlement issue for the container shipping industry on a global scale. Therefore, this paper proposes an improved immune algorithm based recursive model for optimizing static empty container allocation which integrates with the global maritime container shipping network. This model minimizes the operating and capital costs during container shipping considering 0–1 mixed-integer programming. So an immune algorithm procedure based on a special twodimensional chromosome encoding is proposed. Finally, computational experiments are performed to optimize a 10-port static empty container shipping system. The results indicate that the proposed recursive model for static empty container allocation is effective in making an optimal strategy for empty container allocation.     

基于遗传算法的容器云资源配置优化

提出一种基于遗传算法的容器云资源配置优化方法。充分考虑虚拟机配置于物理主机以及容器配置于虚拟机的资源分配情况,将容器云平台数据中心整体能耗最低作为目标函数,设置物理主机与虚拟机对应、虚拟机与容器对应等约束条件,利用遗传算法通过染色体表达、初始化、交叉操作、变异操作以及设置适应度函数5个步骤求解目标函数,获取最优容器云环境资源配置结果。实验结果表明,本文方法可实现容器云资源的合理配置,提高物理资源的利用效率,实现数据中心节能的目标。     

A Tabu Search Heuristic for the Inland Container Transportation Problem

The Inland Container Transportation Problem describes the movement of full and empty containers among a number of terminals, depots and customers in a hinterland region. A trucking company with a homogeneous fleet of trucks has to serve customers which either receive goods by inbound containers or ship goods by outbound containers. While keeping given hard time constraints the total operating time of all trucks has to be minimized. A comprehensive mathematical formulation which considers vehicle routing and scheduling and empty container repositioning simultaneously is defined. The problem is solved by an efficient Tabu Search Heuristic. Computational experiments carried out on small- and large-sized instances indicate that the proposed algorithm performs well with respect to effectiveness and efficiency.     

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.     

考虑时间窗的集装箱港口场桥全局调度优化

合理高效的场桥调度计划有助于减少场桥与集卡相互等待的时间从而提高港口的运营效率,考虑到在实际操作中会出现多箱区多场桥同时工作、互相冲突等情况,建立了以场桥移动成本和延误成本最小化为目标的数学模型,利用计划时间段和时间窗的概念对场桥作业进行约束,通过遗传算法编码进行求解,并将计算结果与实际操作及其他算法的优化结果相对比,进而验证该模型和算法的有效性和稳定性。     

基于滚动窗策略的港口海铁联运集装箱转运优化

为了解决港口海铁联运转运集装箱作业规模过大的问题,采用滚动窗策略方法研究港口船舶与列车之间转运进口集装箱作业问题,在每个窗口内建立以列车在港停留时间和集装箱在堆场的堆存时间总时间最小为目标的整数规划模型,设计双层遗传算法进行求解。在此基础上,分别讨论了以固定任务数量和固定时间长度为滚动窗口的情况,对比发现以固定时间长度为窗口的滚动窗策略更适用,并将其与已有的调度策略研究成果作比较分析。最后,设置实验比较双层遗传算法和单层遗传算法,并对设备的工作能力进行灵敏度分析。结果表明,滚动调度策略可以灵活解决大规模集装箱转运问题,双层遗传算法的解优于单层遗传算法的解,增加装卸线数和轨道起重机工作能力可以提高集装箱转运效率。     

Immunity-based hybrid evolutionary algorithm for multi-objective optimization in global container repositioning

The development of evolutionary algorithms for optimization has always been a stimulating and growing research area with an increasing demand in using them to solve complex industrial optimization problems. A novel immunity-based hybrid evolutionary algorithm known as Hybrid Artificial Immune Systems (HAIS) for solving both unconstrained and constrained multi-objective optimization problems is developed in this research. The algorithm adopts the clonal selection and immune suppression theories, with a sorting scheme featuring uniform crossover, multi-point mutation, non-dominance and crowding distance sorting to attain the Pareto optimal front in an efficient manner. The proposed algorithm was verified with nine benchmarking functions on its global optimal search ability as well as compared with four optimization algorithms to assess its diversity and spread. Sensitivity analysis was also carried out to investigate the selection of key parameters of the algorithm. It is found that the developed immunity-based hybrid evolutionary algorithm provides a useful means for solving optimization problems and has successfully applied to the problem of global repositioning of containers, which is one of a constrained multi-objective optimization problem. The developed HAIS will assist shipping liners on timely decision making and planning of container repositioning operations in global container transportation business in an optimized and cost effective manner.     

一种基于服务选取的SBS云资源优化分配方法

获取满足全局优化目标的资源分配策略,是影响云环境中基于服务的软件系统(service-based software system,简称SBS)运行时优化效果的关键.然而,由于SBS内部复杂的业务逻辑关系和云环境中的资源约束,现有分配方法无法得到最优资源分配量.以满足SLA约束和最小化资源成本为目标,根据不同资源状态对应不同组件服务性能的特点,将组件服务可能的资源分配量、相应性能及成本转换为备选逻辑服务集,进而提出了一种云环境中基于服务选取的SBS资源优化分配模型,并设计了一种求解模型的混合遗传算法.算法采用整数编码以提高求解效率,并在选择算子中引入了精英保留策略,从而保证收敛到全局最优解.为提高遗传算法的局部搜索能力、加快收敛速度,以局部搜索策略改进了标准变异算子.实验验证了所提出的资源优化分配模型和求解算法的有效性,并表明:与分支定界法及精英保留策略遗传算法相比,混合遗传算法能够在较大规模的问题上快速获得具有较低资源成本的资源分配策略.