带有两次订购机会且两阶段需求相关的Newsboy模型

首先建立需求形式为一般随机变量情形下零售商的两次订购决策模型,并从理论上证明了在两个阶段需求相互独立情形下,零售商期望利润函数的下凹性及最优订购策略的存在唯一性,从而弥补了已有模型在寻求最优订购策略时采用数值方法所存在的缺憾;然后,建立两阶段需求相关情形下带有两次订购的Newsboy模型,给出了模型的分析解.数值实例验证了模型的求解过程,并得到了相关管理启示.

     

两种需求情形下损失规避零售商的最优订货-定价联合决策

在加法需求情形和乘法需求情形下研究损失规避零售商的最优订货-定价联合决策问题. 基于前景理论, 建立损失规避零售商的期望效用函数, 并通过模型推导得到损失规避零售商的最优订货-定价决策的关系表达式, 发现库存因子满足特定条件时, 最优决策的关系表达式一定存在. 同时, 讨论零售商的损失规避行为对最优决策和期望效用的影响. 通过数值算例, 验证了所得结论的有效性, 发现商品定价较高时, 损失规避行为驱使零售商采取保守的订货策略, 使得最优订货量趋于均值需求.

     

考虑消费者缺货容忍的变质品订购与信用期策略研究

考虑消费者存在缺货容忍行为,研究变质品的联合订购和信用期决策问题.假设延迟订购消费者面对缺货等待时存在一个容忍期限(即在该容忍期限内零售商无需支付缺货成本),同时考虑市场需求受商业信用期的影响,库存系统允许缺货且短缺量部分延迟订购.以零售商的平均利润最大化为目标,分两种情形构建变质品的订购和信用期决策模型.从理论上证明最优解的存在性和唯一性,给出相关定理结论,并在此基础上设计一个寻找最优解的两阶段迭代算法.最后通过数值算例展示了模型和算法的应用,并完成主要参数的灵敏度分析.研究结果表明:消费者的缺货容忍行为可以有效增加零售商利润,减低产品变质损失,同时还可以激励零售商提供更长的商业信用期,进而实现买卖双方的共赢.     

库存水平影响需求下变质品订购、定价和保鲜技术投资的联合决策

研究变质品的订购、定价和保鲜技术投资联合决策问题.假设产品需求同时受价格和库存水平的影响,系统不允许缺货并放松期末库存水平为零的约束,零售商拥有有限的货架空间或存储空间,同时考虑零售商可以通过投资保鲜技术减低产品的变质率,以平均利润最大化为目标构建库存水平影响需求下变质品的订购、定价和保鲜技术投资联合决策模型.首先证明最优策略的存在性和唯一性,并给出零售商建立期末库存的条件;然后利用最优解的相关性质设计一个求解模型的多阶段迭代算法;最后通过具体算例验证展示模型和算法的可行性和实用性,并完成相关参数的敏感性分析,获得一定的管理启示.     

考虑参照价格效应的易逝品定价与订购策略

在考虑消费者参照价格效应的基础上,构建一个易逝品的定价与订购联合决策模型,其中产品的需求不仅依赖于销售价格还与该产品在消费者心目中的参照价格相关,变质率为常数,系统不允许缺货.分别讨论了对称参照价格效应和非对称参照价格效应两种情况下零售商的最优定价与订购决策问题,证明并得到关于模型结构的一些性质,进而设计了问题的求解算法.通过数值方法分析了参照价格效应参数和变质率对系统最优解的影响,以及两种情况下最优解之间的关系.结果显示:当面对具有参照价格依赖的消费者时,采用适当的营销策略来提高消费者的参照价格对零售商总是有利的;对高变质率产品而言,零售商可保持一个较稳定的订购策略,更多地关注产品的定价策略;面对损失厌恶型消费者,随着消费者参照价格的逐渐提高,零售商的定价与订购策略均应缓慢地改变,而不宜急剧变化.     

两阶段需求相关的动态易逝品最优订货策略

以动态市场环境下的易逝品为研究对象,针对两阶段需求相关、每次订货时带有固定的订购费用以及未满足需求部分延期供给等实际现象,建立了动态易逝品销售商两阶段订货决策模型.对模型的最优解进行了理论分析,并提出了寻求两阶段订货策略的简单方法.以朗科优盘经销商的采购实例来说明该模型的应用价值.最后,通过探讨订货决策与需求波动、两阶段需求相关系数以及延期供给率等因素的关系,得出了一些重要的管理启示.     

风险规避零售商期末二次订购的供应链决策模型

以风险规避零售商和风险规避供应商组成的两层供应链系统为研究对象, 在条件风险估值的风险度量准则下讨论两生产模式下风险规避零售商的最优订购策略和风险规避供应商的最优生产策略. 通过数值实验得出如下结论: 若零售商的风险规避度越小, 则零售商的第1 次最优订购量越多, 风险利润越大; 供应商的第1 次最优生产量随着供应商的风险规避度减小而增大; 零售商的风险规避度越小, 越不利于供应商的生产投机行为.

     

两种需求模式下报童模型的最优定价-订购联合决策

在加法和乘法两种需求模式下,研究了带有缺货惩罚的单周期报童模型的最优定价-订购联合决策问题,证明了最优决策的存在性及唯一性的充分条件,并具体给出了最优决策的解析表达式。通过数值算例,验证了结论的有效性,给出了价格敏感因子对最优决策和期望收益的影响。研究结果在理论上更具有一般性,而且为研究多零售商的价格竞争决策问题和供应链契约协调等问题提供了理论依据。     

零售商价格竞争下的最优决策与收益共享契约

研究由一个制造商与两个竞争零售商组成的两级供应链系统的最优决策及契约协调问题,其中每个零售商面临的需求是价格敏感和随机的.当随机需求分布具有递增失败率(IFR)时,竞争的零售商存在唯一最优的定价和订购决策,并给出了最优决策的解析表达式,证明了收益共享契约能使两个竞争零售商加盟的供应链达到协调及契约成立的条件.最后通过理论推导和数值分析给出了需求价格弹性系数对最优决策及协调的影响.     

基于延迟定价策略的供应链分散与集中决策

针对消费者市场需求不确定性大的特征, 采用延迟定价策略应对需求波动和实现产品售罄. 基于供应商和零售商的两级供应链, 探讨最优零售价格的确立, 分别建立分散决策下的零售商利润函数模型和集中决策下的供应链系统利润函数模型, 通过函数单峰性分析证明最优订货量的存在性和唯一性, 并求得最优解. 比较和数值分析表明, 在分散决策下, 采用延迟定价策略虽然未必能达到集中决策时的供应链系统最优, 但相比固定售价, 可以显著提高供应链的总利润.

     

Robust Control: A Note on the Timing of Model Uncertainty

In this note a one-state, one-control variable quadratic linear problem with robust control and discount factor is developed to examine the optimal response of the first-period control to changes in future model uncertainty. A change in future model uncertainty has an effect on the optimal first-period control response going in the same direction as the one caused by an equal size change in current model uncertainty. However, both analytical and numerical results show that such effect is much lower than the one derived from a change in current model uncertainty. Moreover, such effect is even much lower as the change in model uncertainty moves farther away into the future. Finally, the infinite horizon result confirms the reinforcing nature of the effects on the optimal first-period control response of current and future changes in model uncertainty.The author thanks P. Ruben Mercado, David A. Kendrick and an anonymous referee for useful comments on earlier versions of this note. This research work was completed while the author was working at the Centro de Investigación y Docencia Económicas (CIDE) in Mexico City. The content of this note is only responsibility of the author. Any opinions expressed here in no way reflect comments nor suggestions made by the Board of Governors or any other member of the Bank of Mexico.     

Fault tolerant scheduling of tasks of two sizes under resource augmentation

Guaranteeing the eventual execution of tasks in machines that are prone to unpredictable crashes and restarts may be challenging, but is also of high importance. Things become even more complicated when tasks arrive dynamically and have different computational demands, i.e., processing time (or sizes). In this paper, we focus on the online task scheduling in such systems, considering one machine and at least two different task sizes. More specifically, algorithms are designed for two different task sizes while the complementary bounds hold for any number of task sizes bigger than one. We look at the latency and 1-completed load competitiveness properties of deterministic scheduling algorithms under worst-case scenarios. For this, we assume an adversary, that controls the machine crashes and restarts as well as the task arrivals of the system, including their computational demands. More precisely, we investigate the effect of resource augmentation—in the form of processor speedup—in the machine’s performance, by looking at the two efficiency measures for different speedups. We first identify the threshold of the speedup under which competitiveness cannot be achieved by any deterministic algorithm, and above which there exists some deterministic algorithm that is competitive. We then propose an online algorithm, named \(\gamma \text{-Burst } \), that achieves both latency and 1-completed-load competitiveness when the speedup is over the threshold. This also proves that the threshold identified is also sufficient for competitiveness.     

考虑供应不足的应急物流车辆路径优化模型及算法

灾害发生后的关键救援期内,应急物资有限且受灾点对应急物资的需求具有不确定性,为提高应急物流工作效率,需同时对应急资源分配和运输车辆路径进行优化决策。针对救援关键期内应急物资可能供应不足的情况,在假设物资需求为随机其服从正态分布的前提下,以最小化供应不足和供应过量所带来的损失、运输成本和车辆使用成本等为优化目标,考虑服务时间窗和车辆装载能力等约束,建立了随机需求环境下应急物流车辆路径问题的优化模型,并基于遗传算法设计了模型的求解方法。算例分析表明,本文所提出的优化方法运算快捷且结果合理,可为相关决策者提供科学的决策依据。     

Scenario-based robust optimization of a water supply system under risk of facility failure

In this paper, we propose a scenario-based robust optimization model for the design of a water supply system considering the risk of facility failure, which is represented as an uncertainty set generated by a finite set of scenarios. New facilities are planned to be built to hedge against the possible failure of existing system facilities that would potentially damage the capacity of the system to meet given user demands. The goal is to build facilities that are both cost-effective and make the system robust. The system robustness is defined as the ability to satisfy user demands for every data realization in the uncertainty set. The proposed model is shown to be equivalent to a large-scale mixed-integer linear program that is solved by a Benders decomposition algorithm. Computational results demonstrate the efficiency of the proposed algorithm, and show that substantial improvement in system robustness can be achieved with minimal increase in system cost.     

CIMS环境下技改项目集成管理信息系统的设计与实现

以成都桌公司CIMS应用工程为背景，介绍了一个CIMS环境下技术改造项目集成管理信息系统的设计与实现。分析了技术改造项目各阶段的信息需求，设计了典型功能模型，既考虑了用户的需求，又兼顾了系统的通用性，实现了专用性与通用性的统一。     

CIMS环境下技改项目集成管理信息系统的设计与实现*

以成都某公司CIMS应用工程为背景,介绍了一个CIMS环境下技术改造项目集成管理信息系统的设计与实现。分析了技术改造项目各阶段的信息需求,设计了典型功能模型,既考虑了用户的需求,又兼顾了系统的通用性,实现了专用性与通用性的统一。     

基于改进PSO算法的随机投入产出模型?

建立一种基于改进PSO算法的随机投入产出模型，在随机变量分别服从正态分布和指数分布时比较其优化结果，利用改进粒子群算法和标准粒子群算法对模型进行实例求解。仿真实验结果表明，考虑随机变量服从指数分布更符合实际经济运行状况，且计算得到的各行业产出大于随机变量服从正态分布时的情况。     

Dynamic cluster resource allocations for jobs with known andunknown memory demands

The cluster system we consider for load sharing is a compute farm which is a pool of networked server nodes providing high-performance computing for CPU-intensive, memory-intensive, and I/O active jobs in a batch mode. Existing resource management systems mainly target at balancing the usage of CPU loads among server nodes. With the rapid advancement of CPU chips, memory and disk access speed improvements significantly lag behind advancement of CPU speed, increasing the penalty for data movement, such as page faults and I/O operations, relative to normal CPU operations. Aiming at reducing the memory resource contention caused by page faults and I/O activities, we have developed and examined load sharing policies by considering effective usage of global memory in addition to CPU load balancing in clusters. We study two types of application workloads: 1) Memory demands are known in advance or are predictable and 2) memory demands are unknown and dynamically changed during execution. Besides using workload traces with known memory demands, we have also made kernel instrumentation to collect different types of workload execution traces to capture dynamic memory access patterns. Conducting different groups of trace-driven simulations, we show that our proposed policies can effectively improve overall job execution performance by well utilizing both CPU and memory resources with known and unknown memory demands     

Tolerance design optimization on cost–quality trade-off using the Shapley value method

Part tolerance design is important in the manufacturing process of many complex products because it directly affects manufacturing cost and product quality. It is significant to develop a reasonable tolerance scheme considering the demands of cost and quality to reduce the production risk and provide a guide for supplier management. Traditionally, some kinds of cost objective functions or variation propagation models are often applied in part tolerance design. Moreover, designers usually solve the tolerance design problem by constructing a single-objective model, dealing with several single-objective problems, or establishing a comprehensive evaluating function combining several optimization objectives with different weights. These approaches may not adequately consider the interdependent and the interactional relations of various demands and balance them. This paper presents a kind of tolerance design approach at the early design stage of automotive parts based on the Shapley value method (SVM) of coalitional game theory considering the demands of manufacturing cost and product quality. First the part tolerance design problem is defined. The measuring data in regular production is collected instead of working on specific objective functions or design models. Then how the SVM is adopted to solve the tolerance design problem is discussed. Lastly, a tolerance design example of a vehicle front lamp demonstrates the application and the performance of the proposed method.     

一种基于小波变换和ARIMA的短期电价混合预测模型

为在实时电价情况下预测未来24小时电价, 提出一种基于小波变换和差分自回归移动平均(ARIMA)的短期电价混合预测模型。该模型分别根据是否受到需求量影响使用ARIMA模型对多尺度小波变换分解后的时间序列进行预测。同时提出一种电价突变点发现和处理算法。使用澳大利亚新南威尔士州2012年真实数据验证表明, 相对ARIMA预测, 改进后的混合模型在不考虑需求量影响时预测精度更高; 电价突变点发现和处理算法能够准确处理电价异常点, 提高预测精度。