模糊随机需求下多产品报童问题的均衡策略

针对模糊随机需求下单制造商多零售商的分布控制型多产品报童问题, 建立了含资金约束的期望利润最大化两层规划模型.结合模糊随机模拟技术与遗传算法, 设计了求解模型的混合智能算法.该算法不仅可获得上层制造商的最优折扣批发价及下层零售商的最优订购量,亦可求得该折扣形式的起始折扣点(折扣区间).算例分析表明,当制造商采取最优数量折扣策略时:1)促使零售商订货量增加至资金约束上限;2)部分产品订货量可达模糊随机市场需求的最大可能值:3)零售商和制造商的利润均增加.     

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.     

顾客需求量不确定时生产商的在线数量折扣策略

针对顾客需求量不确定时,生产商为节省库存成本而采用数量折扣策略鼓励零售商提高单次订货量的决策问题,从在线问题与竞争分析的角度出发,设计了平衡策略,并证明了该策略是最优在线数量折扣策略.然后给出了算例,算例表明了无论顾客的需求量如何变化,平衡策略确定出的数量折扣,总能使生产商的成本接近于顾客需求量确定时的最小成本;同时与传统的数量折扣模型进行了对比,说明了在传统的数量折扣模型中做出的决策,往往不是我们所提出的新的评价准则(竞争比)下的最优策略.     

三种不同需求分布条件下报童问题期望费用的计算

报童问题是运筹学中典型的随机性存贮模型.目前很多运筹学和管理科学教科书一般只给出报童问题最优订购量应该满足的条件,而没有给出具体的期望费用的表达式,这对于报童问题模型在实际中的应用造成不便.研究了三种不同需求分布条件下报童问题期望费用计算问题,所得出的关于期望费用计算公式具有简洁和便于实际应用的特点.     

风险厌恶条件下竞争报童问题的二层规划模型

有效应用数学规划方法研究报童类产品的库存管理,能够降低企业的成本和风险,从而提高企业的经济效益.此类问题的研究,目前主要集中在应用Nash博弈研究竞争报童问题以及零售商所持风险态度对供应链库存水平的影响.试图研究序贯决策下的竞争报童问题模型以及零售商所持风险态度对供应链库存水平的影响.由此建立了二层规划模型.算例表明序贯决策的竞争报童问题中,总订购量随风险厌恶程度的提高而降低.     

可追加订购的报童问题

研究带有追加 (两次 )订购的报童模型 ,分析了此模型与经典 (一次订购 )报童模型的收益关系 ,服务水平评价 ,订购量与需求均值、方差、价格等的灵敏度及发现了在适当的条件下 ,最大追加订购量 M的最优解存在 ,且给出了求解的方法 .     

基于灰色关联度下的报童问题

基于灰色系统的决策思想,提出了具有灰色特性的报童问题,即在传统报童问题的影响因素—成本价、零售量、零售价和处理价等因素中引入灰色信息.应用灰色决策方法,计算和比较理想方案和临界方案构成的综合关联度,综合关联度大者对应为优良方案.给出的算法使得决策更具有柔性,决策结果更贴近实际.     

线性数量折扣下零售商合作的联盟稳定性研究

在激烈的市场竞争中,通过降低原材料的采购成本来降低生产成本成为中小企业增加利润率水平和提高市场竞争力的重要手段之一.因此,越来越多的中小企业通过组成正式或非正式的联盟进行联合采购来降低采购价格.文章考虑了供应商提供线性数量折扣方案下多零售商联合采购的订货决策与费用分配问题.应用合作博弈理论,将数量折扣下多零售商联合采购的费用分配问题构造成联合采购博弈,证明了博弈满足次加性和平衡性,给出了一种属于博弈核心的费用分配方案,并用最大一致集分析了联盟的稳定性.     

采购当中的数学问题——数量折扣条件下采购成本优化分析

<正>1问题的提出当供应商给出价格折扣的时候,作为采购方就需要作出决策,到底选择哪个档次的折扣才可以获得最为有利的采购条件,才能够使得采购相关总成本最低.在现实当中供应商给出的价格折扣包括多种形式,但使用最频繁的是数量折扣,因此作为采购方就需要选择恰当的采购数量,     

广告分担、价格折扣与供应链的纵向合作广告

研究了生产商和零售商的纵向合作广告问题。分别在广告分担和价格折扣策略下，探讨了双方的均衡结果和利润。当生产商的边际利润较小时，生产商的最优决策是不采取任何一种策略。当生产商的边际利润达到一定范围时，广告分担策略是双方共同的最优选择。而无论在什么样的条件内，价格折扣策略都不会使双方同时满意。为了增加双方的收益，供应链成员应该在广告上集成决策。最后给出了系统集成的可行最优解的范围和Nash讨价还价解。     

Probability-free solutions to the non-stationary newsvendor problem

This paper concerns the multi-period newsvendor problem. In this problem, the decision maker has to decide the order quantity of an item in the subsequent period in which the demand is usually unknown. No statistical assumptions are made about the unknown demand. We adopt an online learning method from the field of prediction with expert advice to study the non-stationary newsvendor problem. We propose newsvendor strategies for both real-valued and integer order quantities. Taking the non-stationary strategies that can switch between different order quantities as benchmark, we prove that our proposed strategies can guarantee that the newsvendor’s cumulative gains are almost as large as those of the best switching strategies with not too many switches. Simple computational experiments are further performed to illustrate the effectiveness of our strategies.     

基于价格可变的占线优惠卡问题及其竞争分析

优惠卡问题是租赁问题的一个推广,已往的研究都是考虑只存在一种优惠活动的情况,并给出最优的单阀值购买策略。在本文中,则考虑更接近实际的情况,即存在多种优惠活动的问题,给出了当所有优惠卡价格在有界范围内的双阀值购买策略,分别给出了其竞争比上下界。最后,通过数值分析说明优惠卡价格和折扣率对消费者购买策略和竞争比的影响。     

A manufacturer's optimal quantity discount strategy and return policy through game-theoretic approach

This study generalised the traditional quantity discount problem with return contracts, in which a manufacturer promises to refund some fraction of the retailer's wholesale price if an item is returned, as a two-stage game. In the first stage the manufacturer and retailer determine the inventory level cooperatively. In the second stage, the manufacturer bargains with the retailer for quantity discount and return schemes to maintain channel efficiency. A menu of discount–return combinations is proposed for the manufacturer to make inventory decisions. The model developed will demonstrate that the return policy can be considered as mirror images of quantity discount strategy. That is, options with more generous return privileges are coupled with higher wholesale prices, whereas the lowest wholesale price comes with very strict limits on returns and a restocking fee for any returned goods.     

E-retailing of restaurant services: pricing strategies in a competing online environment

Establishing online channels and providing online discounts by building business partner relationships with third-party websites have emerged as important and effective marketing strategies in the restaurant industry. This study examines the optimal pricing strategy of restaurants in a competing environment when they participate in this relationship with a third-party website. Results suggest that neither participation nor online price discount should be encouraged for all restaurants. In particular, for a restaurant with a fixed service capacity, participation and online price discount are recommended when the number of offline loyal customers is relatively small. With the increase in the number of loyal offline customers, the optimal online discount rate decreases, whereas the unit commission fee for the third-party website remains constant. When the optimal discount rate reaches zero, the optimal decision for the restaurant is to decrease the unit commission fee. Based on these findings, this study analytically provides the optimal pricing strategies for restaurants and the corresponding boundaries for the strategy set.     

基于在线购买历史聚合的B2C电子商务差异化折扣模型

众多B2C网站已建立起会员等级制度,并据此向买家提供价格折扣。但会员等级制模型仅考虑买家交易金额,无法全面反映买家在线购买历史(online purchase history),故不能准确提供差异化折扣。针对上述问题,提出了一种面向B2C电子商务的差异化折扣模型,该模型包含能体现买家在线购买历史的交易、退单、推荐购买、晒单等四个指标,将买家在线购买历史聚合为一个综合值,进而通过min-max标准化方法进行线性转换,将转换后的聚合值与会员等级基准折扣结合得到最终的差异化折扣,从而使得B2C网站可向同级别会员实施更精准的一对一营销和价格歧视策略。以京东商城为背景的仿真实验结果证明了本文新模型的有效性。     

Copula-CVaR下考虑随机成本和需求的单周期订货决策研究

不确定环境下的单周期最优订货量决策具有重要且广泛的应用价值。与传统的仅考虑需求不确定性的报童模型不同,本文考虑市场价格恒定,但成本和需求随机变化且相关联下的报童决策问题。为此,采用Copula函数构建成本和需求之间的关联,考虑决策者可能具有的风险态度,建立了相应的Copula-CVaR模型,证明了模型解的存在性和唯一性,并将模型离散化为易求解的线性规划问题。最后,通过不同的风险水平和多种Copula函数下的仿真,分析了随机成本与需求的相关性和波动性对最优决策结果影响,并得到相关结论,为相关企业决策提供依据。     

Integrated inventory model with quantity discount and price-sensitive demand

Quantity discount has been a subject of study for a long time; however, little is known about its effect on integrated inventory models when price-sensitive demand is placed. The objective of this study is to find the optimal pricing and ordering strategies for an integrated inventory system when a quantity discount policy is applied. The pricing strategy discussed here is one in which the vendor offers a quantity discount to the buyer. Then, the buyer will adjust his retail price based on the purchasing cost, which will influence the customer demand as a result. Consequently, an integrated inventory model is established to find the optimal solutions for order quantity, retail price, and the number of shipments from vendor to buyer in one production run, so that the joint total profit incurred has the maximum value. Also, numerical examples and a sensitivity analysis are given to illustrate the results of the model.     

A comparison of different quantity discount pricing policies in a two-echelon channel with stochastic and asymmetric demand information

In this paper, we study quantity discount pricing policies in a channel of one manufacturer and one retailer. The paper assumes that the channel faces a stochastic price-sensitive demand but the retailer can privately observe the realization of an uncertain demand parameter. The problem is analyzed as a Stackelberg game in which the manufacturer declares quantity discount pricing schemes to the retailer and then the retailer follows by selecting the retail price and associated quantity. Proposed in the paper are four quantity-discount pricing policies: “regular quantity discount”; “fixed percentage discount”; “incremental volume discount” and “fixed marginal-profit-rate discount”. Optimal solutions are derived, and numerical examples are presented to illustrate the efficiency of each discount policy.     

Channel coordination under two-level trade credits and demand uncertainty

This study considers a decentralized supply chain where a retailer has an opportunity to order a product from a supplier prior to the sales season to satisfy uncertain demand. The retailer provides trade credit to end customers and makes credit period and order quantity decisions to maximize profits. The end demand is both random and credit period-dependent. On the basis of the newsvendor model, this paper focuses on channel coordination when a retailer provides trade credit to end customers. When the supplier also provides trade credit to the retailer, we show that the traditional trade credit contract cannot coordinate the channel. Four composite contracts based on trade credit (trade credit cost sharing with buy back or quantity flexibility; modified trade credit with buy back or quantity flexibility) are provided to induce the retailer to make decisions while optimizing the channel profit. This paper shows that the retailer provides a longer credit period to its customers and orders a larger quantity from the supplier under the composite contracts. With these contracts, the profit sharing between both parties depends on the wholesale price (Pareto improvement) for the fixed retail price and the purchasing cost.     

Coherent risk measures in inventory problems

We analyze an extension of the classical multi-period, single-item, linear cost inventory problem where the objective function is a coherent risk measure. Properties of coherent risk measures allow us to offer a unifying treatment of risk averse and min–max type formulations. For the single period newsvendor problem, we show that the structure of the optimal solution of the risk averse model is similar to that of the classical expected value problem. For a finite horizon dynamic inventory model, we show that, again, the optimal policy has a similar structure as that of the expected value problem. This result carries over even to the case when there is a fixed ordering cost. We also analyze monotonicity properties of the optimal order quantity with respect to the degree of risk aversion for certain risk measures.