直营连锁企业生产分配供应单周期模型研究

直营连锁企业一直面临着一个重要的决策问题是生产多少产品按什么分配方案供应给它的连锁店进行销售,使得连锁企业的损失最小和利润最大.文章首先建立了连锁企业在给定总生产量和基于期望损失下的单周期最优分配模型,导出了生产分配供应的最优策略公式,提出了求解最优生产总量和最优分配供应策略的近似计算方法,以及在销售周期内打折定价策略.最后通过对某食品直营公司的销售数据进行计算,所提近似算法可以获得连锁公司对应的最优分配供应策略,数值结果表明采用最优分配供应策略可以减少直营连锁企业的期望损失风险,增加期望收益.文章给出了连锁企业最优生产供应策略的模型和近似求解算法,对于连锁企业的生产和分配供应具有重要的理论指导意义.     

鲁棒凸优化问题拟近似解的刻划

本文研究鲁棒凸优化问题拟近似解的最优性条件和对偶理论.首先利用鲁棒优化方法,在由约束函数的共轭函数的上图给出的闭凸锥约束规格条件下,建立了拟近似解的最优性充要条件.其次给出了鲁棒凸优化问题拟近似解在Wolf型和Mond-weir型对偶模型下的强(弱)对偶定理.最后给出具体实例验证了本文获得的结果.     

关于具有不确定性凸半无限规划的近似解（英文）

在本文中,我们考虑约束函数带有不确定信息的凸半无限优化问题的近似解(也称为ε-解),并建立了凸半无限规划的鲁棒对等问题,同时给出了其近似解.进一步地,提出了鲁棒对偶问题的必要条件和充分条件.在锥约束条件下,基于鲁棒优化方法,证明了近似解意义下的拉格朗日对偶性质.     

多损失WCVaR模型的等价性定理

研究了多概率分布簇下的多损失下的WCVaR(Multi Worst Conditional Value-at-Risk)模型等价性定理, 根据概率分布簇的VaR测度值, 定义了多损失下的WCVaR风险测度值和对应的多目标优化模型(MWCVaR), 证明了多目标优化模型(MWCVaR)等价另一个多目标优化模型求解. 对于有限分布簇情形, 在一定条件下, 证明了用有限个分布簇就可以近似计算多损失(MWCVaR)优化模型.     

“供应侧”鲁棒报童模型

完全信息下的报童模型是研究单周期下企业运营的核心方法,制定不同情形下的订货决策是提高其运作效率的主要策略.Scarf在1958年构建了需求侧不确定时部分信息下的鲁棒报童模型,经过GallegoMoon更简便的证明后Worst-case的鲁棒订货方法得到了广泛的应用,供应侧不确定时的订货问题由于求解的困难性导致运作管理学者长期受其困扰.文章假设报童只知道供应商随机产出因子的均值和方差信息,建立了Worst-case的鲁棒订货模型,给出了订货决策的闭式最优解.研究表明:鲁棒报童模型能够恰当的描述供应侧信息缺失下的企业订货行为,鲁棒的订货策略能有效解决供应不确定的信息缺失环境下企业的订货决策问题.     

基于凸概度分布簇下WCVaR模型的有限逼近性

如何求解实际问题中Worst条件风险值模型是一个非常困难的问题,研究了凸概率分布簇下的WCVaR(Worst Conditional Value-at-Risk)模型等价性及其在序列分布簇下的有限逼近性,根据概率分布簇的VaR测度值,定义了WCVaR风险测度值和对应的WCVaR模型,证明了WCVaR模型等价一个另一个数学规划问题求解.在一定条件下,证明了在损失有界情形用有限个分布簇就可以足够近似计算WCVaR模型的最优解,因此,对于解决稳健型条件风险值模型具重要的实际价值.     

不确定凸优化问题鲁棒近似解的最优性

该文研究一类目标和约束函数均带有不确定信息的凸优化问题的鲁棒近似解.首先,在闭凸锥约束品性假设下,得到了该不确定优化问题关于近似解的最优性条件.然后,引入所研究不确定优化问题的近似鞍点的概念,并给出了近似解的鞍点刻划.     

多周期多目标条件风险值模型

在商业、工业、电力和房地产等行业中存在许多复杂的多周期风险决策问题,它的数学模型研究对于解决这些问题具有重要的作用.作者建立了一种新的多周期多目标条件风险值(CVaR)数学模型理论和方法.先定义了一种带时间段的多周期多目标损失函数下的α-VaR和α-CVaR值,给出了一类多周期多目标CVaR最优化模型.然后,证明了多目标意义下的对应模型的等价定理,给出了多周期多目标CVaR模型的近似求解等价模型.最后,建立了一种生产企业在供过于求和供不应求两种情形下产生的多周期双目标CVaR模型,针对一个电力生产企业进行的数值实验,表明了模型可以得到在最小供给的用电损失分布下的各周期下的相匹配供电策略,可以帮助供电部门各个时期供电不平衡状况下的风险控制.     

基于回购契约的供应链鲁棒协调模型

研究了需求不确定条件下供应链回购契约协调问题。针对未知需求具体分布形式的两级供应链系统,建立了基于回购契约的供应链鲁棒协调模型。给出了仅知需求均值和方差信息条件下,集成供应链系统的鲁棒订货策略和分散供应链系统的鲁棒契约协调策略。分析了不同契约参数条件下的供应链及其成员利润情况。最后,进行了数值计算,验证了当获得需求的真实分布形式时．供应链鲁棒回购契约协调策略的有效性。结果表明,供应链鲁棒回购契约协调策略能够有效减少需求不确定性对系统及其成员利润的影响。     

加权均值方差准则下的最优资本分配

本文考虑相依风险情形下的最优资本分配问题.采用随机加权损失函数,在期望-方差准则下,研究最优资本分配解的存在性,分析加权随机变量选取.进一步,文章提出了以破产概率作为模型评价标准,采用随机模拟的方法分别求解不同模型最优资本分配和相应的破产概率,对模型做出评价.最后,在假设相依风险分别为多元正态分布和多元t分布的情形下,用数值模拟的方法对本文提出的加权期望-方差模型与Dhaene提出的加权均值模型和XU和MAO提出的尾部均值方差模型进行比较,结果显示在破产概率准则下,本文提出的加权期望-方差模型所给出的资金分配比例显著优于其他模型.     

A new distributionally robust p-hub median problem with uncertain carbon emissions and its tractable approximation method

The p-hub median problem is to determine the optimal location for p hubs and assign the remaining nodes to hubs so as to minimize the total transportation costs. Under the carbon cap-and-trade policy, we study this problem by addressing the uncertain carbon emissions from the transportation, where the probability distributions of the uncertain carbon emissions are only partially available. A novel distributionally robust optimization model with the ambiguous chance constraint is developed for the uncapacitated single allocation p-hub median problem. The proposed distributionally robust optimization problem is a semi-infinite chance-constrained optimization model, which is computationally intractable for general ambiguity sets. To solve this hard optimization model, we discuss the safe approximation to the ambiguous chance constraint in the following two types of ambiguity sets. The first ambiguity set includes the probability distributions with the bounded perturbations with zero means. In this case, we can turn the ambiguous chance constraint into its computable form based on tractable approximation method. The second ambiguity set is the family of Gaussian perturbations with partial knowledge of expectations and variances. Under this situation, we obtain the deterministic equivalent form of the ambiguous chance constraint. Finally, we validate the proposed optimization model via a case study from Southeast Asia and CAB data set. The numerical experiments indicate that the optimal solutions depend heavily on the distribution information of carbon emissions. In addition, the comparison with the classical robust optimization method shows that the proposed distributionally robust optimization method can avoid over-conservative solutions by incorporating partial probability distribution information. Compared with the stochastic optimization method, the proposed method pays a small price to depict the uncertainty of probability distribution. Compared with the deterministic model, the proposed method generates the new robust optimal solution under uncertain carbon emissions.     

A discrete time optimal control model with uncertainty for dynamic machine allocation problem and its application to manufacturing and construction industries

This paper proposed a discrete time optimal control model in which machine failure time is modeled assuming a Weibull distribution and machine productivity is regarded as a fuzzy variable for dealing with a dynamic machine allocation problem (DMAP) in manufacturing and construction industries. The aim is to maximize total production or construction throughput when uncertainties such as machine breakdowns are taken into account. A failure probability-work time equation is presented to describe the relationship between machine failure probability and mean time to work. To transform the uncertain optimal control model into a deterministic one, the expected value model (EVM) was introduced for forming an equivalent crisp model. The fuzzy variables in the model are also defuzzified by using an expected value operator with an optimistic–pessimistic index. Then a number of lemmas and theorems are presented and proved to formulate the theoretical algorithm so that the crisp model of the DMAP can be solved. Three actual construction and production projects are used as practical application examples. The theoretical algorithm results for the three project examples are compared with a particle swarm optimization approach and a genetic algorithm method, which demonstrates the practicality and efficiency of our optimization method.     

装配型企业订货量分配与订单排产联合决策研究

针对供应商交货数量不确定环境下,多品种小批量装配型制造企业因生产物料不配套造成生产计划不可行甚至客户订单拖期的问题,从企业运作整体出发,考虑订货量分配决策对订单生产和交货的影响,以最小化采购成本和最小化订单排产相关成本为优化目标,在允许零部件拖期交货且供应商提供拖期价格折扣条件下,建立订货量分配与订单排产联合优化模型。针对可行解空间巨大、传统数学规划方法难以求解的问题,从增强搜索性能角度出发,设计基于自定义邻域搜索算子的局部搜索机制和基于随机与种群重构变异机制的改进粒子群算法的模型求解策略。通过应用实例对本文模型和算法进行了有效性验证和灵敏度分析,结果表明,相比于传统的分散决策方案,本文模型能够有效降低整体成本水平,引入的改进机制能够显著提升算法搜索性能,为企业供应风险下的运营决策制定提供理论参考。     

A robust optimization model for agile and build-to-order supply chain planning under uncertainties

Supply chain planning as one of the most important processes within the supply chain management concept, has a great impact on firms’ success or failure. This paper considers a supply chain planning problem of an agile manufacturing company operating in a build-to-order environment under various kinds of uncertainty. An integrated optimization approach of procurement, production and distribution costs associated with the supply chain members has been taken into account. A robust optimization scenario-based approach is used to absorb the influence of uncertain parameters and variables. The formulation is a robust optimization model with the objective of minimizing the expected total supply chain cost while maintaining customer service level. The developed multi-product, multi-period, multi-echelon robust mixed-integer linear programming model is then solved using the CPLEX optimization studio and guidance related to future areas of research is given.     

Robust reward–risk ratio optimization with application in allocation of generation asset

In this article, we study reward–risk ratio models under partially known message of random variables, which is called robust (worst-case) performance ratio problem. Based on the positive homogenous and concave/convex measures of reward and risk, respectively, the new robust ratio model is reduced equivalently to convex optimization problems with a min–max optimization framework. Under some specially partial distribution situation, the convex optimization problem is converted into simple framework involving the expectation reward measure and conditional value-at-risk measure. Compared with the existing reward–risk portfolio research, the proposed ratio model has two characteristics. First, the addressed problem combines with two different aspects. One is to consider an incomplete information case in real-life uncertainty. The other is to focus on the performance ratio optimization problem, which can realize the best balance between the reward and risk. Second, the complicated optimization model is transferred into a simple convex optimization problem by the optimal dual theorem. This indeed improves the usability of models. The generation asset allocation in power systems is presented to validate the new models.     

考虑风险偏好的动态生产库存问题的鲁棒优化模型

不同阶段需求不确定情况下,决策者的风险偏好和生产过程中的废品处理影响着供应链生产库存管理和供应链整体效益。本文考虑决策者风险偏好下,构建了包含I个生产者企业,一个库存点和一个废物处理基地的T阶段动态供应链生产库存框架,建立了椭球型需求不确定集下,以追求整体收益最大化为目标的不确定优化模型,并应用鲁棒优化理论得到了数据确定性线性鲁棒对应模型,讨论了模型解的可靠性和有效性。最后的算例表明,只有当决策者风险偏好参数在一定范围内时,才会存在满足条件且具有较高可靠性的鲁棒决策,验证了该鲁棒优化模型的合理性。     

VMI条件下具有复合二项随机需求的销售商库存策略研究

考虑一个典型的单一产品的二级供应链系统:单供应商对单销售商,假定系统中销售商的需求分布为复合二项分布,未满足的需求机会损失;补货间隔时间为一随机变量.本文采用概率方法对销售商的需求分布、期望缺货、期望库存周期及库存的稳定性分布进行研究的基础上,构建了使单位时间内销售商的期望库存成本费用最小的库存模型,由此模型便可确定VMI模式下供应商对销售商的库存补货参数s和S,并且给出了在补货响应时间为泊松分布的情况下模型的求解算法,还给出了及时补货响应情况下的5个算例.为补货策略的实施提供了一种简单易于控制的思路和方法.     

An integrated inventory model with controllable lead time and distribution‐free demand

The impact of lead time reduction on an integrated periodic review inventory system comprising a single vendor and multiple buyers with a step crashing cost function and service‐level constraints is studied. The probability distribution of demand during the protection period for each buyer is unknown, but the mean and the variance are given. Each production lot of the vendor can be delivered in a number of shipments to all buyers. A minimax distribution‐free procedure with Lagrange multipliers is applied to determining the lead time, the common shipment cycle time, the target levels of replenishments and the number of shipments per production cycle so that the expected total system cost is minimized. Numerical experiments along with sensitivity analysis were performed to illustrate the effects of parameters on the decision and the total system cost. Copyright © 2009 John Wiley & Sons, Ltd.     

Multiproduct single-machine production system with stochastic scrapped production rate, partial backordering and service level constraint

In this paper, a multiproduct single-machine production system under economic production quantity (EPQ) model is studied in which the existence of only one machine causes a limited production capacity for the common cycle length of all products, the production defective rates are random variables, shortages are allowed and take a combination of backorder and lost sale, and there is a service rate constraint for the company. The aim of this research is to determine the optimal production quantity, the allowable shortage level, and the period length of each product such that the expected total cost, including holding, shortage, production, setup and defective items costs, is minimized. The mathematical model of the problem is derived for which the objective function is proved to be convex. Then, a derivative approach is utilized to obtain the optimal solution. Finally, two numerical examples in each of which a sensitivity analysis is performed on the model parameters, are provided to illustrate the practical usage of the proposed methodology.