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。     

铁路集装箱物流中心系统建模与仿真分析

铁路集装箱物流中心作业系统是复杂的随机系统,难以用数学模型准确描述。采用系统仿真的方法,依据集装箱物流中心的作业流程,构建基于eM-Plant的铁路集装箱物流中心整体作业模型。通过仿真实验,研究到站集装箱列车所载箱箱型比例、外卡到站时间间隔和内卡数量等模型参数对集装箱物流中心作业性能的影响。仿真结果表明集装箱列车所载箱箱型比例为6∶1∶3时装卸设备利用率最高,外卡在到站时间间隔超过8 min时无需等待,内卡数为5~7时辅助中转箱转运作业时间最少。     

考虑箱区作业均衡的ACT船舶配载鲁棒优化研究

基于自动化集装箱码头堆场与传统堆场在布局与机械配置方面的区别，将箱区作业均衡因素引入船舶配载问题中，以最小化装船作业时间和箱区作业均衡为目标建立考虑箱区作业均衡的自动化集装箱码头船舶配载混合整数规划模型；同时引入解的保守性参数，将确定性模型转化为考虑桥吊作业时间不确定性的自动化集装箱码头船舶配载鲁棒优化模型，再设计基于编号与排序的禁忌搜索算法对算例进行求解。结果证明了模型的正确性与算法的有效性，且各案例在不考虑箱区作业均衡因素时得到的箱区作业不均衡箱量比考虑该因素时得到的箱区作业不均衡箱量平均高出17.8%，不确定性情况下，“集中配载”现象更加显著。     

基于突破性局部搜索的集装箱班列同步转运作业调度优化

铁路集装箱中心站作为内陆腹地运输网络中的重要节点.转运作为主要的站内作业活动,对其开展调度研究,能够有效缩短不同运输方式之间的转运周期,从而保证铁路集装箱多式联运的整体运作效率.为了快速制定堆场作业计划,现有铁路集装箱站场转运作业均要求班列间的集装箱转运须经堆场方可实现.在该模式下,必然发生两次装卸和一次暂存作业.为了避免暂存中转作业,采用集装箱班列同步转运作业模式构建了以同步转运集装箱箱数最大化为目标的数学规划模型,提出突破性局部搜索框架实现对大规模问题的求解.最后通过算例分析验证了所提出的启发式算法效率.     

铁路集装箱中心站内部集卡调度和箱位配置

针对铁路集装箱中心站集装箱专列卸箱作业的效率问题,将内部集卡的任务调度和集装箱箱位分配进行集成,建立集装箱列车卸车作业的多目标优化模型,包括内卡在轨道门吊和辅助箱场等待时间,以及走行时间对作业效率的影响。采用混合遗传算法进行求解。将模拟结果与实际操作数据作比较,并对20次计算模拟进行平均时间和方差分析,验证了该方法的有效性。     

速度不确定下作业时间最短的集卡调度优化

在集装箱码头装卸作业中,提高集卡调度对作业效率有非常大的影响。而集装箱卡车重车和空车行驶速度的不确定性,增加了集卡调度的难度。为此提出建立不确定环境下作业时间最短的集卡调度优化模型。在完成固定装卸任务的前提下,通过对集卡的合理调度,达到不确定情况下集卡总作业时间最短。针对不确定模型的特点,采用粒子群算法快速求解,同时保证了解的有效性。算例表明上述模型和算法合理解决了不确定环境下集卡调度优化问题,得到较好的计算结果,并且降低了运算复杂度,提高了调度效率。     

基于多Agent和进化算法的作业线调度研究

作业线调度是集装箱码头生产调度中最为关键的一个环节。文中将在充分分析与研究集装箱码头装卸作业信息流动的基础上,针对岸边作业线调度问题以及国内作业线系统的不足,建立模型辅助决策,帮助调度员快速、准确、安全、合理地进行岸桥调度的决策。在文中,充分地考虑了泊位、船舶、岸桥等集装箱设备及其协调,利用并合理地配置了这些码头资源,将引用多Agent技术协调作业线调度,并使用进化算法计算并安排岸桥来进行作业调度,建立基于多Agent和进化算法的集装箱码头作业线调度智能化系统。     

基于作业链的泊位与岸桥协同调度研究

针对泊位与岸桥协同调度问题,引入"链式优化"思路,用作业链的方法分析集装箱装卸作业过程,首先将泊位计划作为开始链单元,采用资源节点优化策略进行分析,以最小化船舶在港总成本为目标建立模型;然后将岸桥卸船作业作为结束链单元,采用任务节点优化策略进行分析,以最小化岸桥最大完工时间为目标建立模型.考虑到作业链的整体性能,设计嵌...     

带有缓存区的集装箱码头AGV和场桥的联合调度

为提高自动化集装箱港口设备AGV（Automated Guided Vehicle）和堆场场桥的工作效率,减少它们之间衔接作业的相互等待时间,建立了带有缓存区设置的集装箱码头AGV和堆场场桥的联合调度模型。利用遗传算法进行算例求解,得到相应的调度方案和以场桥的作业延迟时间、AGV的总行驶时间及场桥等待AGV时间之和最小为目标的完工时间。再针对不同的缓存区容量的设置进行调度方案完工时间的结果比较。实验结果表明,缓存的设置可以有效减少AGV和堆场场桥衔接作业中相互等待的时间,而缓存区容量在一定范围内对于完成作业时间有较大影响。     

考虑箱区作业均衡的自动化码头集装箱船舶配载计划

在考虑箱区作业均衡的基础上,研究自动化码头集装箱船舶的配载计划问题.以堆场翻箱时间最小、集装箱水平运输时间最短以及堆场箱区作业不均衡水平最小为目标建立0-1整数规划模型.使用CPLEX工具精确求解5个不同规模的算例得到配载结果,并对比是否考虑箱区作业均衡的配载方案.结果显示5组算例的翻箱量均为0,有效地满足翻箱时间最小的目标.在配载计划中考虑箱区作业均衡,所得的箱区作业不均衡指数较不考虑箱区作业均衡有一定程度的降低,最高降低19.0个百分点,平均降低7.2个百分点.结果表明所提模型可有效解决自动化码头配载计划问题,保证箱区作业的均衡性,提升码头的装船作业效率.     

铁路集装箱中心站资源分配与作业调度联合优化

铁路集装箱中心站主作业区资源分配与作业调度联合优化对其经营效益和运作效率有重要影响.基于“轨道吊-集卡”协同装卸方案,引入轨道吊动态配置原则,以最大化作业均衡率和最小化作业成本为目标,构建多目标非线性混合整数规划模型,综合研究作业区域动态划分、贝位分配以及多轨道吊调度多层次联合优化问题. 根据问题特点,融合启发式规则、遗传算法和模拟退火算法,设计3层混合启发式算法求解模型.通过不同规模算例,对比Cplex与所设计算法的实验结果,验证模型的正确性和算法的有效性,并借助于不同划分原则、不同优化策略与不同间隔约束下的对比实验,验证优化模型与算法普适性.结果表明,新型装卸方案、动态作业区域以及联合优化策略可大幅度均衡轨道吊作业量,避免资源过度负载、降低中心站作业成本,为中心站的实际运营管理提供决策支持.     

Advanced resource planning as a decision support module for ERP

The planning and decision support capabilities of the manufacturing planning and control system, which provides the core of any enterprise resource planning package, can be enhanced substantively by the inclusion of a decision support module as an add-on at the midterm planning level. This module, called advanced resource planning (ARP), provides a parameter-setting process, with the ultimate goal of yielding realistic information about production lead times for scheduling purposes, sales and marketing, strategic and operational decision making, and suppliers and customers. This article illustrates the ARP approach with reports from several real-life implementations by large industrial companies.     

基于深度强化学习的雾计算容器整合

在雾计算系统架构基础上, 针对数据中心高能耗、应用任务负载的随机动态性以及用户对应用的低时延要求, 提出一种基于A2C (advantage actor-critic)算法的以最小化能源消耗和平均响应时间为目标的容器整合方法, 利用检查点/恢复技术实时迁移容器, 实现资源整合. 构建从数据中心系统状态到容器整合的端到端决策模型, 提出自适应多目标奖励函数, 利用基于梯度的反向传播算法加快决策模型的收敛速度. 基于真实任务负载数据集的仿真实验结果表明, 该方法能够在保证服务质量的同时有效降低能耗.     

An enriched model for the integrated berth allocation and quay crane assignment problem

Given the increasing pressure to improve the efficiency of container terminals, a lot of research efforts have been devoted to optimizing container terminal operations. Most papers deal with either the berth allocation problem (BAP) or the (quay) crane assignment problem (CAP). In the literature on the BAP, handling times are often simplified to be berth dependent or proportional to vessel size, so the CAP can be ignored when scheduling vessels. This is unsatisfactory for real-life applications because the handling time primarily depends on the number of containers to be handled and the number of cranes deployed. Only a limited number of papers deals with the combination of berth allocation and crane assignment. In these papers however, authors often have resorted to algorithmic simplifications that limit the practical use of the models. This paper presents a MILP model for the integrated BAP–CAP taking into account vessel priorities, preferred berthing locations and handling time considerations. The model is used in a hybrid heuristic solution procedure that is validated on real-life data illustrating the potential to support operational and tactical decision-making.     

基于协同ERP的群体决策支持系统集成化研究模型

首先系统地分析了ERP软件的研究发展现状,明确定义了协同商务时代ERP(Collaboration Business Enterprise Resource Planning,简称CERP或协同ERP)概念模式,其次,对群体决策支持系统(Group Decision Support System, 简称GDSS)研究模进行系统研究,最后,集成协同ERP概念模式与GDSS研究模型,首次提出了基于协同 ERP的 GDSS集 成化研究模型。     

基于双层规划的容器云资源动态配置算法

本文分析容器云资源动态配置决策问题,通过定义容器云资源的调度任务,求解得到容器云资源调度时间;利用容器云资源调度任务的最短时间矩阵,获取容器云资源调度所需的条件。在双层规划条件下,求解容器云资源调度的目标函数和约束函数;考虑到用户的任务情况和云数据中心的云资源状况,在虚拟机上构建一个到物理主机的矩阵,通过构建容器云资源动态配置结果在优化时的目标函数,结合约束条件,实现容器云资源的动态配置。实验结果表明,资源动态配置算法不仅可以提高容器云资源的利用率,还可以减少配置完成时间,具有更好的动态配置性能。     

Integrating tactical and operational berth allocation decisions via Simulation–Optimization

The berth allocation problem (BAP) arising in maritime container terminals has received great attention in the literature over recent years. It has been largely modeled as an integer mathematical programming formulation to be adopted at a tactical level, where detailed equipment and manpower schedules, as well as real-time operational conditions are not explicitly modeled. In this paper, decision making for the BAP is supported by integrating two separate models into a Simulation–Optimization framework: a mathematical programming model at the tactical level and a simulation model at the operational level. Specifically, the framework uses a beam search heuristics to obtain a weekly plan at the tactical level, followed by a simulated annealing based search process to adjust allocation decisions at the operational level. At this level, randomness in discharge/loading operations is taken into account and modeled by an event-based Monte Carlo simulator. A non-standard ranking and selection procedure is used to compare alternative BAP solutions, within the Simulation–Optimization procedure, in order to reduce the related number of simulation runs required. Numerical experiments performed on real instances show how, under conditions of uncertainty and variability, the tactical solution returned for the BAP requires tuning at the operational level.     

Workflow simulation for operational decision support using event graph through process mining

It is increasingly common to see computer-based simulation being used as a vehicle to model and analyze business processes in relation to process management and improvement. While there are a number of business process management (BPM) and business process simulation (BPS) methodologies, approaches and tools available, it is more desirable to have a systemic BPS approach for operational decision support, from constructing process models based on historical data to simulating processes for typical and common problems. In this paper, we have proposed a generic approach of BPS for operational decision support which includes business processes modeling and workflow simulation with the models generated. Processes are modeled with event graphs through process mining from workflow logs that have integrated comprehensive information about the control-flow, data and resource aspects of a business process. A case study of a credit card application is presented to illustrate the steps involved in constructing an event graph. The evaluation detail is also given in terms of precision, generalization and robustness. Based on the event graph model constructed, we simulate the process under different scenarios and analyze the simulation logs for three generic problems in the case study: 1) suitable resource allocation plan for different case arrival rates; 2) teamwork performance under different case arrival rates; and 3) evaluation and prediction for personal performances. Our experimental results show that the proposed approach is able to model business processes using event graphs and simulate the processes for common operational decision support which collectively play an important role in process management and improvement.     

Agent based simulation architecture for evaluating operational policies in transshipping containers

An agent based simulator for evaluating operational policies in the transshipment of containers in a container terminal is described. The simulation tool, called SimPort, is a decentralized approach to simulating managers and entities in a container terminal. Real data from two container terminals are used as input for evaluating eight transshipment policies. The policies concern the sequencing of ships, berth allocation, and stacking rule. They are evaluated with respect to a number of aspects, such as, turn-around time for ships and traveled distance of straddle carriers. The simulation results indicate that a good choice in yard stacking and berthing position policies can lead to faster ship turn-around times. For instance, in the terminal studied the Overall-Time-Shortening policy offers fast turn-around times when combined with a Shortest-Job-First sequencing of arriving ships.     

铁路集装箱中心站最佳通道数设计问题研究

铁路集装箱中心站大门通道数量优化设计问题,直接关系到整个系统的运作效率,传统的研究多采用数学建模方法,而基于运作成本角度的系统细节忽略较多,造成系统不全面。根据排队系统理论(M/M/1和M/M/S),分析了铁路集装箱中心站大门集装箱卡车服务规则与到达时间分布,考虑排队等待时间,建立基于随机服务系统(排队论)的铁路集装箱中心站大门最佳通道数计算仿真模型,并利用eM-Plant仿真软件系统对模型的实体、事件、活动和进程四个要素进行仿真。通过一个算例仿真了系统运行情况,分析了不同数量通道数对中心站运作成本的影响。仿真结果表明,可为铁路集装箱中心站大门通道数优化设计提供理论依据。