模糊随机需求报童问题的Stackelberg-Nash均衡策略

针对模糊随机需求下的分布控制型报童问题,建立了无数量折扣和有数量折扣情况下的利润最大化两层规划模型,并结合模糊随机模拟技术和遗传算法设计了模型求解的混合智能算法.解决了上层制造商制定包括折扣区间和折扣价格的最优数量折扣策略,以及下层多零售商确定各自的最优订货量的Stackelberg-Nash均衡策略问题.     

碳排放交易机制下的供应链运作策略及协调研究

碳排放权交易是控制和减少碳排放的有效工具,同时也使得企业的运作成本增加,供应链的决策管理更复杂。考虑由零售商和制造商组成的二阶段供应链,研究不同碳排放权交易政策(供应链成员内部碳排放权交易、外部市场碳排放权交易)下基于数量折扣契约的供应链协调问题,并与无碳排放约束的情形进行比较。研究结果表明:在不同碳排放权交易政策下,数量折扣契约能够实现供应链的协调;无碳排放约束的供应链最优订货量大于外部碳排放权交易的供应链最优订货量,成员内部碳排放交易下的供应链最优订货量和供应链期望利润随着碳排放配额的增加最终等于无碳排放约束下的供应链最优订货量和供应链期望利润;外部市场碳排放交易政策下供应链的期望利润随着碳排放配额的增加而增加。     

保兑仓模式下供应链期末返利契约参数设计

以两阶段资金约束型供应链作为研究对象,以零售商运营资金不足作为前提条件,主要研究保兑仓模式下两级供应链期末返利契约的参数设计问题.首先构建了面向资金约束下银行直接贷款时供应链期末返利契约模型,作为模式比较的基础.然后,定量化建立了保兑仓模式下期末返利契约协调模型,该模型以制造商作为供应链的主导企业,承担全部的经营风险.引入银行作为第三方展开融资业务,以制造商信用作为基础,为零售商提供运营资金.应用Stackelberg博弈模型求解出保兑仓模式下银行、制造商、零售商总体利益最大化下最优的订货量、批发价格以及期末增量单位返利数量值.通过运算发现,在保兑仓模式下,制造商的最优批发价小于直接融资模式下的制造商批发价,由于制造商承担全部风险,致使零售商订货量增加,同时制造商利润上升,从而使整个供应链的利润随之上升.     

资金成本下延迟支付期限的收益分享契约

针对由单一制造商和单一零售商组成的供应链系统,建立了随机需求下考虑有资金成本延迟支付期限的收益分享契约模型,研究两种决策(分散化和集中化)下,延迟支付期限是如何影响资金约束零售商的最优订货数量以及供应链系统的利润.分析表明,不管是分散化还是集中化决策,考虑有资金成本延迟支付期限的收益分享契约使供应链系统的最优订货量和利润都增大.最后,运用数值分析验证了结论.     

需求信息非对称情况下零售商最优订货问题研究

考虑需求信息非对称环境下多个存在竞争的零售商的最优订货问题,每个零售商的需求分布函数未知,传统的通过最大化期望利润来求解最优订货量的方法此时不再适用,转而应用稳健优化中使用的方法-最小化最大后悔值方法.首先给出了零售商最优订货量的一般形式.其次考虑了问题的两种特殊情形:对称博弈、两个零售商的最优订货模型,得到:对称博弈存在Nash平衡点、竞争使得零售商订货量增大、信息不对称使得零售商订货量降低并且零售商的最优订货量在一定条件下会随回收残值的增大而增大;两个零售商最优订货模型下的最优订货量的具体形式.     

面向制造商资金约束的绿色供应链融资策略研究

考虑了生产绿色产品供应链中,由于制造商由于加大绿色产品投入而面临资金约束时,零售商是否应该参与融资的问题。分别研究了在分散决策和集中决策下,存在资金约束的制造商采用预付款融资策略和银行贷款融资策略对供应链中企业最优决策的影响。给出了不同融资策略下,制造商和零售商的最佳决策。研究表明,在制造商存在资金约束的绿色产品供应链中,在集中决策和分散决策的情况下,零售商都应该采取提前付款折扣为0的融资策略参与制造商融资。同时,对于生产绿色产品的供应链,零售商应对制造商采取激励措施,促使制造商进行绿色技术创新生产绿色度更高的产品,从而扩大市场提高竞争力。最后,应用数值模拟对结论进行进一步验证。     

数量折扣契约下基于成本分担的供应链突发事件协调应对

突发事件会增加供应链成本,如何进行成本分担是决定供应链能否协调应对突发事件的重要因素.用参数分别描述市场需求剧增时增加的生产成本和市场需求剧减时发生的多余产品处理成本,在数量折扣契约基础上,研究突发事件发生后制造商的最优批发价格和零售商的最优订货量,分析其影响因素比较突发事件发生前后包括市场剧增与市场剧减两种情况下的差异,并给出了数值算例.结果表明,成本分担系数和市场需求分布及其变化都会影响最优批发价格和最优订货量,只要根据市场需求变化相应调整契约参数并合理分担由突发事件增加的成本,通过数量折扣契约供应链就能够协调应对突发事件.     

零售商资金约束下两级供应链的延期支付与协调策略研究

本文研究了随机需求下两级供应链的延期支付问题，其中供应商作为主导者将首先设置延期支付利率，而受资金约束的零售商作为追随者将根据利率来确定订货量。通过构建Newsvendor模型对供应链各成员的利润进行了分析，并基于Stackelberg博弈研究了两级供应链下的延迟支付策略，得到了零售商的最优订货量及供应商的最优利率。此外，我们还研究了延期支付条件下的供应链协调问题，分别探讨了产品回购契约和收益共享契约下的供应链协调。研究结果表明：只有当供应商设置的利率低于特定阈值时，受资金约束的零售商才会采用延期支付，否则零售商会将全部初始资金用于采购而不采用延期支付；在延期支付条件下，提高利率将导致零售商订货量减少，并且供应商设置的利率不会随零售商的初始资金增大；延期支付只能部分协调供应链，同时在延期支付条件下，通过产品回购契约无法实现供应链的完全协调；在延期支付条件下，存在关于收益占比的帕累托改进区间，使得收益共享契约在该区间内能够实现供应链协调。最后，我们通过数值实验对研究结论进行了验证。     

考虑随机模糊缺陷率且允许缺货的EOQ模型

针对实际库存管理中的产品缺陷问题,研究了含随机模糊缺陷率且允许缺货的经济订购批量(EOQ)模型,并运用随机模糊理论将其转化为确定模型,设计了随机模糊模拟仿真算法进而确定了其最优订购策略.数值算例分析了缺陷率对最优订货量和最优利润的影响.     

考虑消费者低碳偏好的可替代产品订货决策

基于产品的可替代性,考虑零售商有无能力限制两种情况,订购环境质量和价格不同的两种产品,建立利润最大化模型,确定两种产品的最优订货量和消费者低碳偏好对其的影响.理论分析表明:库存能力的提高使得订货量增大,订单数量和利润随环境质量的变化而变化;算例计算的结果验证了模型的合理性,数值分析表明:提高库存容量和订购替代率大的两种产品有利于利润的增加,当库存无容量限制时,订货量和销售商利润水平不再受容量变化的影响.     

模糊随机需求的连续盘点存储策略研究

考虑到需求的模糊随机性,建立模糊随机需求情况下连续盘点存储策略的模糊随机成本模型。利用模糊随机变量的期望值理论,推导出了其成本期望值模型的解析表达式,进而给出了最优再订货点所属区间的判别条件以及最优再订货点和经济订货量的计算式;基于此,设计了一模糊随机需求的连续盘点最优存储策略算法。最后结合数值算例,分析了模糊随机需求概率分布及缺货成本对最优存储策略的影响。     

随机模糊需求报童问题

考虑到需求的随机模糊性,建立了随机模糊需求报童的利润模型。利用可信性测度理论给出了其期望利润模型,并揭示了期望利润函数的凹性,证明了最优订货量的存在性和唯一性。结合随机模糊模拟技术和随机扰动近似算法设计了求解最优订货量的混合算法。最后,通过数值算例说明了算法的有效性。     

基于可信性理论的均值-熵-偏度投资组合模型及其算法求解

本文考虑到证券市场的投资者往往面临着随机和模糊两种不确定性的情形,在模糊随机环境下把证券的收益率视作三角模糊变量,在可信性理论基础上建立了带融资约束条件的均值-熵-偏度三目标投资组合决策模型,拓展了基于可信性理论的投资组合决策模型的研究内容,同时通过对约束条件处理方法,外部档案维护方法等关键算子的改良,提出了一种新的约束多目标粒子群算法。本文运用该算法对模型进行求解,把得到的最优解与传统的多目标粒子群算法得到的最优解进行对比,结果表明新算法得到的最优解的质量会显著地优于传统的多目标粒子群算法的最优解,从而验证了算法的有效性和准确性。该算法可以在三维空间中得到一个分布性和逼近性较好的Pareto最优曲面,满足投资者对不同目标的差异需求,为投资者提供合理的投资组合决策方案。     

A new data envelopment analysis model with fuzzy random inputs and outputs

This paper first presents several formulas for mean chance distributions of triangular fuzzy random variables and their functions, then develops a new class of fuzzy random data envelopment analysis (FRDEA) models with mean chance constraints, in which the inputs and outputs are assumed to be characterized by fuzzy random variables with known possibility and probability distributions. According to the established formulas for the mean chance distributions, we can turn the mean chance constraints into their equivalent stochastic ones. On the other hand, since the objective in the FRDEA model is the expectation about the ratio of the weighted sum of outputs and the weighted sum of inputs for a target decision-making unite (DMU), for general fuzzy random inputs and outputs, we suggest an approximation method to evaluate the objective; and for triangular fuzzy random inputs and outputs, we propose a method to reduce the objective to its equivalent stochastic one. As a consequence, under the assumption that the inputs and the outputs are triangular fuzzy random vectors, the proposed FRDEA model can be reduced to its equivalent stochastic programming one, in which the constraints contain the standard normal distribution function, and the objective is the expectation for a function of the normal random variable. To solve the equivalent stochastic programming model, we design a hybrid algorithm by integrating stochastic simulation and genetic algorithm (GA). Finally, one numerical example is presented to demonstrate the proposed FRDEA modeling idea and the effectiveness of the designed hybrid algorithm.     

Supply chain coordination for fuzzy random newsboy problem with imperfect quality

Facing to imperfect quality and fuzzy random market demand in the real-life inventory management, a two-echelon supply chain system with one retailer and one manufacturer for perishable products is considered. Two fuzzy random models for the newsboy problem with imperfect quality in the decentralized and centralized systems are presented. The expectation theory and signed distance are employed to transform the fuzzy random model into crisp model. The optimal policies in the two decision-making systems are derived and analyzed contrastively. The theoretical analysis shows that manufacturer’s repurchase strategy can achieve the increase in the whole supply chain profit. The influence of the fuzzy randomness of the demand and the defective rate on the optimal order quantity, the whole supply chain profit and the repurchasing price is analyzed via numerical examples.     

A comprehensive note on: An economic order quantity with imperfect quality and quantity discounts

Lin [T.Y. Lin, An economic order quantity with imperfect quality and quantity discounts, Appl. Math. Model. 34 (10) (2010) 3158–3165] recently proposed an EOQ model with imperfect quality and quantity discounts, where the lot-splitting shipments policy is adopted. In this note we first rectify the holding cost terms showed in Lin to obtain a new objective function, then resolve the problem and develop an easy to implement algorithm to find the overall optimal solutions for the model. Besides, we present a new model for items with imperfect quality, where lot-splitting shipments and different holding costs for good and defective items are considered. The closed-form formulas for determining the optimal ordering and shipping policies are derived. Also, the results are examined analytically and numerically to gain more insights of the solutions.     

基于遗传算法的随机模糊立体运输问题

随机模糊立体运输问题的研究是为了解决现实生活中双因素不确定性问题,在遗传算法的基础上,运用可信性理论建立随机模糊运输问题的机会约束规划模型.通过算例进行VC++编程模拟计算,验证了此模型的可行性,最终提出了基于遗传算法解决随机模糊立体运输问题的模型.     

A fuzzy random periodic review system with variable lead-time and negative exponential crashing cost

In the present model a fuzzy random periodic review system has been investigated with the annual demand assumed to be a discrete fuzzy random variable with associated imprecise probabilities. Keeping in mind the widespread application of the Just-In-Time manufacturing philosophy and lead-time management being one of its most effective methods of implementation, the lead-time has been assumed to be an added control parameter. Also as it may not be always possible to resolve the lead-time into all its components and estimate their individual crashing costs, the crashing cost has been introduced as a negative exponential function of the lead-time. A methodology has been developed in this regard such that the total inventory cost is minimized and the optimal period of review, the optimal target inventory level and the optimal lead-time are determined in the process. An algorithm has been provided to encapsulate the methodology and it has been illustrated by way of a numerical example.     

An inventory model for a deteriorating item with displayed stock dependent demand under fuzzy inflation and time discounting over a random planning horizon

An inventory model for a deteriorating item (seasonal product) with linearly displayed stock dependent demand is developed in imprecise environment (involving both fuzzy and random parameters) under inflation and time value of money. It is assumed that time horizon, i.e., period of business is random and follows exponential distribution with a known mean. The resultant effect of inflation and time value of money is assumed as fuzzy in nature. The particular case, when resultant effect of inflation and time value is crisp in nature, is also analyzed. A genetic algorithm (GA) is developed with roulette wheel selection, arithmetic crossover, random mutation. For crisp inflation effect, the total expected profit for the planning horizon is maximized using the above GA to derive optimal inventory decision. On the other hand when inflationary effect is fuzzy then the above expected profit is fuzzy in nature too. Since optimization of fuzzy objective is not well defined, the optimistic/pessimistic return of the expected profit is obtained using possibility/necessity measure of fuzzy event. Fuzzy simulation process is proposed to determine this optimistic/pessimistic return. Finally a fuzzy simulation based GA is developed and is used to maximize the above optimistic/pessimistic return to get optimal decision. The models are illustrated with some numerical examples and some sensitivity analyses have been presented.