基于前摄性调度的电动汽车路径问题研究

针对车辆调度过程中资源不均衡的问题,利用需求的不确定性,将配送周期划分为初始配送阶段和补货阶段,建立多阶段电动汽车的两级车辆路径优化模型.根据需求的动态程度对配送区域进行划分,结合前摄性调度和反应性调度策略,提出了一种混合禁忌搜索算法(HTSA)来求解该模型.在真实的案例和多个基准评估算例上的实验结果表明:模型和算法的性能优于传统的启发式算法,具有一定的实用价值.     

突发事件情景下串联式需求系统应急物资协同调度研究

针对突发事件情景下串联式需求系统遭受破坏问题,分析了突发事件情景下串联式需求系统应急物资协同调度的特征。在对系统提供应急物资进行修复的基础上,以串联式需求系统修复的时间最短及成本最小为目标,分别构建了纵向配送的应急物资调度模型和纵向配送与横向转运相结合的应急物资协同调度模型,并设计一种遗传算法对两种模型进行求解。最后通过算例分析,求解得到两种模式下串联式需求系统应急物资调度的最优配送方案,比较解的结果,得出纵向配送与横向转运相结合的应急物资协同调度模式优于一般的应急物资纵向配送模式的结论,验证了该应急物资协同调度模式的有效性和可行性。     

多行程带时间窗口的车辆调度问题研究北大核心

为了提高车辆的使用率,企业往往会安排车辆在单位周期内,执行多次配送任务.为了研究多行程带时间窗口的车辆配送(VRPTW)中的车辆调度问题.模型以车辆的固定费用、车辆行驶过程中的等待费用、司机的工作小时费最小为目标,同时也融合了司机在执行不同路线时,由于熟悉的过程所弓I起的费用.通过对路线的时间窗口性质的分析,建立了调度问题的模型.     

多行程带时间窗口的车辆调度问题研究

为了提高车辆的使用率,企业往往会安排车辆在单位周期内,执行多次配送任务.为了研究多行程带时间窗口的车辆配送(VRPTW)中的车辆调度问题.模型以车辆的固定费用、车辆行驶过程中的等待费用、司机的工作小时费最小为目标,同时也融合了司机在执行不同路线时,由于熟悉的过程所弓I起的费用.通过对路线的时间窗口性质的分析,建立了调度问题的模型.     

物流配送车辆路径问题的鲁棒优化方法

针对物流配送中的不确定性因素,构建车辆路径间题的鲁棒性度量与优化方法,目的是降低不确定性因素对物流配送系统的影响.首先,提出车辆路径问题的鲁棒性度量指标,利用算例对各指标的效果进行分析,选择适用于度量车辆路径方案鲁棒性的指标.在此基础上,设计物流配送车辆路径规划的两阶段优化算法.算法的第一阶段不考虑车辆路径的鲁棒性,以总配送成本最小为目标函数优化配送方案;算法的第二阶段以鲁棒性度量指标最大为目标函数,以第一阶段获得的总成本与车辆数为约束条件,优化鲁棒调度方案.文章为车辆路径问题的鲁棒性度量提供了一种有效方法,同时为如何平衡供应链中的物流配送环节的服务作业成本与调度方案鲁棒性提供了思路.     

基于改进模拟退火算法的推动式生产-配送协调优化

为减小物资生产与配送不协调造成的成本及生产资源浪费,建立了考虑推动式生产调度的物资配送优化模型,并针对标准模拟退火算法受随机因素影响易陷入局部最优的缺点,设计带有回火与缓冷操作的改进模拟退火算法对模型求解,确定了优化的车辆配送路线以及物资生产计划。对比实验结果表明：相对于单纯的物资配送优化模型,考虑推动式生产调度的配送优化模型,能够有效减小物资滞留时间以及配送延误成本;相较于标准模拟退火算法,改进算法搜索到了更优解,且计算结果的标准差减小了93.42%,稳定性更好;同时,改进模拟退火算法具有较低的偏差率,在中小规模算例中求解质量较高,平均偏差率在0.5%以内。     

基于模糊DEA的ERP系统选型方法

提出了一种基于模糊数据包络分析的企业资源计划系统选型方法.以系统的实施复杂性、预计实施成本、功能匹配度和系统供应商的企业形象等作为主要评价指标,运用模糊集相关理论争方法对系统选型过程中的不确定性进行刻画和描述,对候选系统的相对有效性进行客观评价,解决了系统选型过程中的不确定性和模糊性问题.并通过实证研究说明了该方法的应用.     

突发事件下应急物资多目标优化调度模型与算法研究：以新冠肺炎疫情为例

突发事件下应急物资调度具有高度不确定性与动态性,应急物资跨区域调度计划能否科学合理制定对应急救援的有效开展具有重要影响。以武汉市新冠肺炎疫情为例,考虑应急物资跨区域调配的时效差异、地域分散以及资金消耗等要素,建立多周期下包含软硬需求时间窗约束的多物资品种、运输方式的跨区域三级动态物资调度网络模型,并设计一种带变异操作的动态变惯性权重自适应粒子群算法(VDCWPSO),利用疫情算例验证该算法的有效性和可行性。研究结果表明,在保障应急物资供应时效性、公平性、经济性原则下,物资跨区域调度可以缓解资源严重短缺状况、提高医用物资利用率,研究可为突发事件下应急物资调度计划的制定与动态调整提供决策支持。     

不确定环境下快速消费品生产配送鲁棒优化模型

在具有生产准备决策的生产环节,考虑生产成本、加班成本及客户需求的不确定性,建立一个鲁棒优化模型,来解决多周期、多产品、多工厂、多配送中心的快速消费品生产配送集成调度问题。模型目标是最小化生产准备成本、生产成本、库存成本及配送成本。最后提出一个算例来说明所提出的模型和方法的适用性和实用性,并分析解鲁棒性和模型鲁棒性。     

带客户协同分仓的动态车辆路径问题研究

随着互联网商业迭代的不断深化,越来越多的企业倾向于从商品前置视角解决配送距离与配送时效性的矛盾。为此,本文研究基于客户协同分仓备货的动态车辆调度问题(Dynamic Vehicle Routing Problem with Inventory Synergetic Customer, DVRP-ISC),设计考虑区域分异特征的协同分仓客户选择方法,建立多阶段动态配送网络优化模型。鉴于研究问题的特殊性,设计多阶段两级网络协同配送路径优化算法;最后,以仿真算例、自定义算例集和基准算例,验证所提模型和算法性能及其拓展性。     

Simulation and optimization for crop water allocation based on crop water production functions and climate factor under uncertainty

In this paper, the uncertainty methods of interval and functional interval are introduced in the research of the uncertainty of crop water production function itself and optimal allocation of water resources in the irrigation area. The crop water production functions in the whole growth period under uncertainty and the optimal allocation of water resources model in the irrigation area under uncertainty are established, and the meteorological factor is considered in the model. It can promote the practical application of the uncertain methods, reflect the complexity and uncertainty of the actual situation, and provide more reliable scientific basis for using water resources economically, fully improving irrigation efficiency, and keeping the sustainable development of the irrigated area. This approach has important value on theoretical and practical for the optimal irrigation schedule, and has very broad prospects for research and development to other related agriculture water management.     

油田开发指标系统的概率模拟预测

基于油田开发指标系统的复杂性和其影响因素带有明显的随机特征,从理论和实用上研究了油田开发指标系统的不确定性预测方法.方法提出采用神经网络建立开发指标和影响因素的关联关系;利用微分模拟研究开发指标的影响因素的概率分布;使用蒙特卡罗方法对影响因素进行随机抽样;依据得到的关联关系和影响因素的随机样本建立油田开发指标的概率预测模型.此外,设计了概率模拟器.实例研究表明开发指标的预测值以某概率落在某个区间内,这可为决策者做出符合实际的决策和准备应急决策方案提供依据.     

An analysis of the structural complexity of supply chain networks

Each enterprise in a supply chain network needs quantitative indicators to analyze and manage its interactions with different business partners in the network. Supply chains exhibit the characteristics of complex systems. In a supply chain network, a large number of firms cooperate simultaneously with many suppliers and customers, and interact through a variety of information and material flows to achieve a balance between supply and demand. However, the complexity of a supply chain is not a simple linear structure where a small change often results in a chain reaction. When supply chain complexity increases, monitoring and managing the interaction between different elements of the chain becomes more difficult. An entropy model based on information theory provides an appropriate means of quantifying the complexity of a supply chain system by delivering information required to describe the state of the system. The entropy measure links uncertainty and complexity so that, as a system grows in uncertainty, it becomes more complex and more information is required to describe and monitor it. In this paper, we propose an entropy-based measure for analyzing the structural complexity in relation to the structure and system uncertainty. The method provides guidelines for estimating the complexity throughout the supply chain structure.     

基于贝叶斯网络的工程项目进度完工概率分析

针对传统计划评审技术(Program Evaluation and Review Technique,PERT)在计算完工概率时假设条件的局限性(假设条件与工程实际存在偏差,导致完工概率偏大),提出了基于贝叶斯网络的施工进度完工概率分析方法.首先,分析了贝叶斯网络与进度计划网络之间的相似性,将两者结合起来构建了贝叶斯进度网络;在此基础上,综合考虑贝叶斯网络在节点取值及概率计算方面的优越性,并结合工程项目的不确定性及复杂性特点,建立了基于贝叶斯网络的施工进度完工概率分析模型.最后,将该模型应用于具体工程进行实例分析,验证了模型的可行性与有效性.研究结果表明:基于贝叶斯网络的进度完工概率模型充分考虑了工程施工中的风险因素,其结果能更客观地反映工程实际,可为工程项目决策者提供可靠的依据.     

Advances on measuring the operational complexity of supplier–customer systems

Even structurally simple supplier–customer systems can be operationally complex. This operational complexity can be colloquially defined as the uncertainty associated with managing the dynamic variations, in time or quantity, across information and material flows at the supplier–customer interface. This paper proposes a means of measuring the information demands placed on supplier–customer systems, as a result of this uncertainty.This paper mathematically models the operational complexity of supplier–customer systems from an information-theoretic perspective. A unique feature of this measure is that it captures, in relative terms, the expected amount of information required to describe the state of the system. The measure provides flexibility in the scope and detail of analysis, while at the same time allowing a systematic hierarchical approach.The application of the measure allows valuable insights to be obtained in terms of the degree of uncertainty, level of control and the detail of monitoring required to manage the operational complexity of supplier–customer systems.     

系统的最佳随机维护策略

系统进行有计划的预防性维护时,要求设计一个具有周期T的维护时间表.有一个重要的问题就是这种维护时间表是否具有最佳周期.本文给出最佳随机维护策略问题有解存在的必要条件,并得到结论:当失效时刻Y服从指数分布时,对维护次数N的任意概率分布,最佳随机维护策略问题都无解.     

考虑路径风险的需求不确定应急物流定位-路径问题

针对自然灾害对路网造成的破坏性和受灾点物资需求量的不确定性,综合考虑应急物流路径风险和物资分配的时间公平性,将路径风险定量化为路径通行能力风险、路径损毁风险和路径复杂性,基于鲁棒优化理论,以最小化车辆最长配送时间和应急物流系统总成本为目标,建立了双目标多物资定位-路径优化模型,采用遗传算法对其求解,以汶川地震应急救援相关数据为例进行了数据仿真实验。实验结果的对比分析表明:在考虑路径风险下,随着控制系数的增加,系统总成本和最长配送时间目标值均呈现增大的趋势;受灾点的物资需求扰动越大,构建应急物流系统的总成本越高。算例结果也验证了鲁棒优化方法在处理不确定需求方面的有效性,为解决自然灾害后应急设施点的开设和救援物资的安全及时准确配送提供了有效的方法。     

可再生能源系统的不确定性优化模型及求解

结合新提出的满意度方法和混合整数规划方法,给出了多态不确定性环境下可再生能源规划模型.该模型综合描述了多地区、多时期、多品种、多部门之间的可再生能源管理系统的复杂性、系统性、动态性和不确定性.最后的实例求解结果说明该模型能很好地反映能源安全性、系统可靠性与系统成本之间的关系,并能给出不同系统违反水平下的能源配置方式及增容计划,为决策者提供决策参考.     

Uncertain insurance risk process with multiple classes of claims

Traditionally, an insurance risk process describes an insurance company’s risk through some criteria using the historical data under the framework of probability theory with the prerequisite that the estimated distribution function is close enough to the true frequency. However, because of the complexity and changeability of the world, economical and technological reasons in many cases enough historical data are unavailable and we have to base on belief degrees given by some domain experts, which motivates us to include the human uncertainty in the insurance risk process by regarding interarrival times and claim amounts as uncertain variables using uncertainty theory. Noting the expansion of insurance companies’ operation scale and the increase of businesses with different risk nature, in this paper we extend the uncertain insurance risk process with a single class of claims to that with multiple classes of claims, and derive expressions for the ruin index and the uncertainty distribution of ruin time respectively. As the ruin time can be infinite, we propose a proper uncertain variable and the corresponding proper uncertainty distribution of that. Some numerical examples are documented to illustrate our results. Finally our method is applied to a real-world problem with some satellite insurance data provided by global insurance brokerage MARSH.     

Using entropy for quantitative measurement of operational complexity of supplier–customer system: case studies

The paper concerns with analysis of operational complexity of company supplier–customer relations. Well-known approach for measuring of operational complexity is based upon entropy. However, there are several approaches thereon. In the first part, we discuss various general measures of uncertainty of states, the power entropies in particular. In the second part, we use Shannon entropy as a base framework for our two case studies—the first, a supplier–customer system which implements managerial thresholds for processing product delivery term deviations, the second, a supplier system of the most important commodity in brewery industry, the malted barley. In both cases, we assume an existence of problem-oriented databases, which contain detailed records of all product orders, deliveries and forecasts in quantity and time having been scheduled and realized. Our general procedure elaborated consists of three basic steps—pre-processing of data with consistency checks in Java, calculation of histograms and empirical distribution functions, and finally, evaluation of conditional entropy. The last two steps are realized by Mathematica modules. Illustrative results of operational complexity measurement using entropy are provided for both case studies.