公交系统随机平衡网络设计模型及求解算法

本文根据公交网络的具体特点,对公交网络进行了系统的描述,提出一个双层规划模型来描述随机平衡公交网络设计问题。在双层规划模型中,上层模型为一个标准的公交网络设计模型,下层模型是一个公交网络随机平衡配流模型。针对所提出的模型,设计了基于灵敏度分析的求解算法。最后,给出一个简单算例加以证明。     

智能电网实时电价社会福利最大化模型的研究

社会福利最大化模型及其推广、改进的模型是目前智能电网实时定价的有效方法之一。通过计算社会福利最大化模型的拉格朗日乘子,即影子价格,确定电力电价。本文对社会福利最大化中供电量下限（即最小发电量）在模型中的作用进行了研究,在一个符合通常实际情况的假设下,得到了去掉供电量下限的等价模型。所获得的模型减少了一个区间约束和一个变量。最后说明了简化的模型仍然适用于目前使用的在线对偶方法求解。     

基于有限理性的股票灰色定价与股票市场非有效性现象

本文从有限理性的角度,对折现现金流定价模型给出了一个灰色数学描述,引进了信心度的概念,建立了一个含有投资人行为心理为参数的模型,提出了灰色有效市场假说;最后利用该模型和灰色有效市场假说,把股票市场的非有效性现象纳入了一个解释框架.     

设备维修决策的仿真模型

本文就设备计划维修周期决策问题进行了探讨,建立了一个以设备的综合效益为目标的计算机仿真模型。讨论了模型中随机变量的生产算法及参数估计,通过一个实例,给出了该模型的逻辑框架及运行机制。     

基于专家调查的组织信息化成熟度模型研究

在对国内外现有信息化成熟度模型分析的基础上,提出了一个信息化成熟度模型.采用背靠背的方法征集了国内15位知名信息化专家对该模型的意见,统计分析发现,专家对该模型的认可度比较高.根据专家的针对性意见,修改得到了一个5阶段的信息化成熟度模型技术支撑级、资源集成级、管理优化级、战略支持级和持续改善级.最后,对如何进行信息化成熟度的评价提出了一个初步的框架.     

基于可控提前期的两供应商-单制造商协同模型

本文建立了一个基于BOM表关系的两供应商-单制造商供应链模型,在需求服从正态分布和给定服务水平约束条件下,决策制造商分别向两供应商的订货提前期和订货批量,使得供应链成本最优.文章通过模型分析得出所建立的模型是一个凸规划问题,存在唯一的最优解,并且给出了模型的求解算法,最后通过一个算例说明了模型和算法的有效性.     

物流配送中心选址的随机数学模型

本文对照传统的物流配送中心选址问题提出了一个随机化的模型,并从数学角度对该模型进行了的一些分析,给出了单配送中心选址问题的一个量化的处理方法。     

多阶段供应商的选择和协商策略研究

本文在现有文献的基础上对供应商选择模型进行了改进,建立一个多阶段供应商选择混合整数规划模型,在模型中增加考虑了供应商的供应商的绩效水平.为了保持对被选供应商的竞争力,文章建立了一个协商策略模型,来确定那些未被选中的供应商若想被选中所需要达到的业绩水平.     

证券组合优化Konno—Suzuki模型的一个新的线性规划近似模型

Ｋｏｎｎｏ－Ｓｕｚｕｋｉ模型是证券组合优化的新进展。在Ｈ．Ｋｏｎｎｏ和Ｋ．Ｓｕｚｕｋｉ的论文中提出了Ｋｏｎｎｏ－Ｓｕｚｕｋｉ模型的一个线性规划近似模型，用以求解大规模证券组合优化问题。本文提出了与该近似模型等价的一个新的线性规划近似模型，初步比较，显示新模型更便于计算。     

考虑产品绿色度的绿色供应链博弈模型及收益共享契约

本文以一个制造商和一个零售商组成的二级绿色供应链为背景,建立了考虑产品绿色度的四种绿色供应链博弈模型:以制造商为主导的Stackelberg博弈模型、以零售商为主导的Stackelberg博弈模型、制造商和零售商的Nash均衡博弈模型以及集中控制模型。对四种博弈模型在产品绿色度、产品价格以及批发价格等方面进行了比较分析,并进一步建立了收益共享契约下的博弈模型。通过数值仿真探讨了收益共享系数对产品绿色度、制造商利润和零售商利润的影响。     

反向物流系统的优化调整模型与算法

本文提出反向物流系统优化调整问题,并分别研究配送不变和配送改变条件下无容量限制的反向物流系统优化调整模型,给出配送不变条件下的最优策略和配送改变条件下的求解算法。     

需求扰动下生产规模不经济闭环供应链的应急渠道决策

针对生产规模不经济闭环供应链，在突发事件干扰其产品的市场需求发生扰动的情况下，比较研究了各类回收渠道分散式决策系统和集中式决策系统的应急均衡决策及契约协调问题。结果表明：当需求的扰动程度不大时，各类系统正常运营环境下新产品的订购量和废旧品的回收量等均衡决策均具鲁棒性，当需求的扰动程度较大时，各类系统均应按其扰动方向调整正常运营环境下制定的均衡决策；制造商回收渠道分散式决策系统中其获得的利润和系统的总利润均高于零售商回收渠道分散式决策系统的，且制造商会利用更高比例的废旧品生产再造品，因此其应直接回收废旧品；各企业的利润和系统的总利润均随生产规模不经济弹性系数的增加而降低；设计的应急收益费用共享契约可协调解决各类回收渠道分散式决策系统中存在的"双重边际效应"问题，且各企业可通过讨价还价确定收益费用共享比例的取值来获得帕累托改进的利润。     

The Value of Quality Grading in Remanufacturing

In this paper we consider a tactical production‐planning problem for remanufacturing when returns have different quality levels. Remanufacturing cost increases as the quality level decreases, and any unused returns may be salvaged at a value that increases with their quality level. Decision variables include the amount to remanufacture each period for each return quality level and the amount of inventory to carry over for future periods for both returns (unremanufactured), and finished remanufactured products. Our model is grounded with data collected at Pitney‐Bowes from their mailing systems remanufacturing operations. We derive some analytic properties for the optimal solution in the general case, and provide a simple greedy heuristic to computing the optimal solution in the case of deterministic returns and demand. Under mild assumptions, we find that the firm always remanufactures the exact demand in each period. We also study the value of a nominal quality‐grading system in planning production. Based on common industry parameters, we analyze, via a numerical study, the increase in profits observed by the firm if it maintains separate inventories for each quality grade. The results show that a grading system increases profit by an average of 4% over a wide range of parameter values commonly found in the remanufacturing industry; this number increases as the returns volume increases. We also numerically explore the case where there are capacity constraints and find the average improvement of a grading system remains around 4%.     

On Core Sorting in RMTS and RMTO Systems: A Newsvendor Framework

Variation in core condition and uncertainty in market demand pose great challenges for remanufacturers to match supply with demand. This article investigates a firm that acquires and remanufactures cores of multiple quality conditions to satisfy demand. Both remanufacturing‐to‐stock (RMTS) and remanufacturing‐to‐order (RMTO) systems are considered. In each system, a sorting operation that resolves the core conditions before remanufacturing may or may not be adopted, leading to four possible sorting/remanufacturing strategies: (1) no sorting in RMTS; (2) sorting in RMTS; (3) no sorting in RMTO; and (4) sorting in RMTO. Under each strategy, we derive the optimal decisions on the acquisition and remanufacturing quantities, in two scenarios, respectively: (i) all acquired cores are remanufacturable and (ii) some cores are non‐remanufacturable. We find that sorting can be completely useless to RMTS system, and thus should never be adopted regardless of the sorting cost. We provide the analytical condition under which this ineffectiveness of sorting occurs. Nevertheless, sorting is always useful to RMTO system and should be adopted when the sorting cost is below a threshold value. We also conduct an extensive numerical study and show that the effects of sorting to RMTO system are more significant than that to RMTS system.     

Managing Retail Shelf and Backroom Inventories When Demand Depends on the Shelf‐Stock Level

Inventory displayed on the retail sales floor not only performs the classical supply function but also plays a role in affecting consumers’ buying behavior and hence the total demand. Empirical evidence from the retail industry shows that for some types of products, higher levels of on‐shelf inventory have a demand‐increasing effect (“billboard effect”) while for some other types of products, higher levels of on‐shelf inventory have a demand‐decreasing effect (“scarcity effect”). This suggests that retailers may use the amount of shelf stock on display as a tool to influence demand and operate a store backroom to hold the inventory of items not displayed on the shelves, introducing the need for efficient management of the backroom and on‐shelf inventories. The purpose of this study is to address such an issue by considering a periodic‐review inventory system in which demand in each period is stochastic and depends on the amount of inventory displayed on the shelf. We first analyze the problem in a finite‐horizon setting and show under a general demand model that the system inventory is optimally replenished by a base‐stock policy and the shelf stock is controlled by two critical points representing the target levels to raise up/drop down the on‐shelf inventory level. In the infinite‐horizon setting, we find that the optimal policies simplify to stationary base‐stock type policies. Under the billboard effect, we further show that the optimal policy is monotone in the system states. Numerical experiments illustrate the value of smart backroom management strategy and show that significant profit gains can be obtained by jointly managing the backroom and on‐shelf inventories.     

爱尔朗型按订单装配系统最优生产——库存控制策略研究

本文研究具有复杂装配结构的爱尔朗型按订单装配(ATO)系统的组件生产与库存优化控制问题。系统涉及多种组件,一个最终产品和多类客户需求。在此系统中,各种组件的生产时间服从爱尔朗分布,各类客户的需求为泊松到达过程。针对不同客户需求类型:产品需求与独立组件需求且同为销售损失型,建立基于马尔可夫决策过程(MDP)的平均总成本模型,应用动态规划方法求解最优策略。仿真模拟方法实现最优策略,并通过数值实验分析多生产阶段和系统参数对最优策略的影响。研究结果表明,爱尔朗型生产时间ATO系统的最优策略为状态依赖型策略,即组件的生产与库存分配由动态基础库存水平值和动态库存配给水平值控制。对于任一组件,其基础库存水平值和库存配给水平值均随着生产阶段的增加而降低,且生产阶段对基础库存水平值和平均总成本的影响较显著。     

考虑旅客跨区间流转的机票多预定区间折扣优化模型研究

多预定区间差异化折扣逐渐成为机票收益管理的重要分支。本文提出了一种新的收益管理模型：基于顾客跨区间流转的收益管理模型,并给出了二分法迭代求解方法。假设各个预订时间区间的潜在需求可以通过大数据手段进行预测,首先结合旅客的价格敏感和潜在需求跨时间段流转的特性分析了各区间的需求函数,然后结合需求函数构建了多预定区间折扣优化模型。由于该模型属于动态的收益管理模型,因此构建了一种动态求解方法——二分迭代法。最后,依据航空公司的实际情况设计了两个仿真实验。实验计算结果不仅验证了新模型和算法的有效性,而且得出一些比较有用的结论：（1）票价与提前购票时间不存在单调的线性关系;（2）预订区间远离离港日折扣逐渐变大,靠近离港日的折扣会逐渐减少,但是包含离港日的预订区间的折扣又会变大;（3）流转率越高则折扣越少;（4）价格敏感系数越高折扣越高;（5）流转率通过改变价格敏感系数而影响折扣的大小。本文给出的折扣优化决策模型符合旅游产品多预定区间折扣决策的实践,可以为机票、酒店、景区等多种旅游产品的票价决策提供有益参考。     

存在竞争性制造商的集团采购供应链需求预测信息的共享与激励

针对两个竞争性制造商通过集团采购组织(GPO)采购部件时,是否向GPO共享需求预测信息的激励问题,考虑各制造商拥有有限信息、共享部分信息、基于产量竞争的情形,通过建立博弈模型,分析了竞争强度、信息精度和市场波动对信息共享价值的影响,进而提出了信息共享激励策略。研究表明:信息共享能够协调制造商间的竞争,当且仅当竞争强度超过某阈值时,信息共享能够增加供应链的系统预期利润,此时,用两部制补偿协议可激励制造商完全共享信息;当竞争强度和市场波动均较大时,通过信息共享激励,分散式决策能够实现比集中式决策更大的系统预期利润。最后,通过算例对研究结论进行了直观考察和说明。     

Managing a Remanufacturing System with Random Yield: Properties,Observations, and Heuristics

We study a remanufacturing system that involves the ordering of a serviceable product and the remanufacturing of multiple types of returned products (cores) into the serviceable product. In addition to random demand for the serviceable product and random returned quantities of different types of cores in each time period, the remanufacturing yield of each type of core is also uncertain. By analyzing a multi‐period stochastic dynamic program, we derive several properties of the optimal ordering/remanufacturing policy. In addition to some insights, these properties can be used to reduce the search effort of the optimal policy. We also demonstrate that some existing results derived from related models no longer hold in remanufacturing systems with random yield. Recognizing the optimal ordering/remanufacturing policy is highly complex, we examine three simple heuristics that can be efficiently solved and implemented in practice. Among these three heuristics, our numerical analysis suggests that the heuristic that captures most of the yield uncertainty and future system evolvement as well as some of the properties of the optimal ordering/remanufacturing policy outperforms the other two heuristics.