产品回收直接再利用的管理决策研究:基于回收品品质的分析

回收品品质下降是一个客观事实,部分企业为了在成本支付和产品可靠性之间达成均衡,抛弃当期未投入使用的旧产品,并设置一个回收品可靠性阈值以降低回收成本、回收清洁成本和潜在风险成本等。本文考察电子产品的新购和回收再利用过程,以新产品订购成本、新产品库存持有成本、旧产品回收成本、旧产品清洁成本、短缺成本和潜在风险成本为目标函数,获得"可靠性阈值-补货策略"的优解。公司案例表明,(s,Q)补货策略优于(s,S)补货策略;敏感性分析表明,新购单位变动成本对总成本的影响最为明显,旧产品单位回收成本次之。     

考虑产品变质的VMI混合补货发货策略及优化仿真

本文研究了“产品可变质”情况下的VMI库存补货与装运调度问题,并建立了Poisson需求过程下的VMI混合补货发货模型,根据此模型通过简单的规划求解即可得到使长期平均成本最小的最佳混合策略组合。由于模型推演过程中涉及到对补货周期内期望发货次数的近似估计,因而模型解是拟最优的。算例和模型仿真显示,模型结果与仿真结果十分相近,从而模型有效性得以确认。     

库存成本变化的VMI供应链协调

提出了库存成本变化的经济订货批量(EOQ)模型，基于该模型研究了库存成本变化时供应商管理库存(VMI)系统的最优协议问题。在该系统中，订货商和供应商达成缺货成本共担协议：当缺货发生时，供应商需要向订货商支付缺货补偿。订货商和供应商分散决策，订货商通过设计协议来减少其成本，而供应商通过制定补货决策来缩小自身成本。通过与传统系统和整合系统的比较，得出了库存成本变化时VMI系统的最优补货决策和缺货成本共担协议。采用数值算例验证了分析结果。结果表明，当且仅当供应商预期成本等于整合系统的最小总成本与固定缺货罚金之和时，VMI系统与整合系统具有相同的补货决策和系统绩效，即能够实现供应链协调。     

不确定环境下的Supply-hub协同补货决策

个性化需求与零部件创新使得产品需求和补货提前期不确定,对供应链补货决策和运行成本产生重要影响。将提前期不确定因素引入Supply-hub协同补货研究中,探讨提前期随机和需求不确定情况下,考虑零部件配套性的三供应商单制造商生产两定制产品的Supply-hub协同补货决策问题;提出了三种补货策略,以供应链运行成本最小化为目标,建立不同策略下的供应链补货模型并求解最优补货批量和供应链最小运行成本;发现三种补货策略均存在唯一最优补货批量,基于Supply-hub的两种协同补货策略和基于分散决策的供应商独立补货策略各有优势,但基于Supply-hub的批量及时间协同的补货策略恒优于基于Supply-hub的集中补货策略。最后,通过MATLAB进行算例分析验证结论,发现基于Supply-hub的批量及时间协同的补货策略能有效降低需求不确定性带来的成本增加风险;通用件的提前期波动对于供应链期望运行成本的影响要高于定制件提前期波动的影响,因此在进行供应链补货策略选择时更加关注通用件提前期。     

基于混合差分进化算法的联合补货-配送优化模型

本文综合考虑联合补货与配送决策,研究了随机需求、允许缺货环境下多企业多产品联合补货与配送集成优化模型,设计了混合差分进化算法(Hybrid Differential Evolution, HDE)对该模型进行求解,同时通过算例与遗传算法、标准的DE算法进行了比较,证实HDE算法高效且稳定;另外,设计了一个先补货再配送的两阶段优化模型,对比优化结果发现采用供应链协同时补货成本较高,配送成本较低,且总成本较低。最后,对相关参数进行了敏感性分析,发现需求率和库存维持成本的变动对总成本的影响远远大过次要订货成本对总成本的影响。     

基于差分进化算法的运输容量约束下有数量折扣的模糊联合补货模型研究

针对采购管理中广泛存在的不确定性,将单位库存成本和可变订货成本视为模糊变量,构建了运输容量约束下有数量折扣的模糊联合补货模型,此模型属于NP-hard问题,目前缺乏可靠的全局优化求解算法。在选取梯级平均综合表示法对总成本去模糊的基础上,设计了基于自适应混合差分进化算法的求解方法,并通过算例验证了此模糊联合补货模型的有效性和求解算法的全局优化能力。     

基于现金折扣和延期支付条件下变质产品的补货策略

针对变质产品,考虑产品非瞬时补货的情况下,在允许延期支付且如果零售商提前还款,供应商给予零售商现金折扣优惠的条件下,建立了相应的零售商库存决策模型。对模型进行求解,得出零售商的最优订货周期及最优付款时间。最后,通过具体算例,结合灵敏度分析方法,分析了模型中参数变化对最优订货策略的影响。     

基于库存分担策略的装配系统准时供货模型研究

本文考虑一个由多供应商和单制造商构成的装配系统。当市场的需求时间无法确定时,制造商通过对供应商设定合理的库存分担策略来降低自身成本,供应商则需要自行决定对制造商的补货时间并承担相应的库存持有成本和延迟惩罚成本。文章同时建立了供应商之间的纳什博弈模型和以制造商为主方的主从博弈模型,以找到供应商的最佳供货时间和制造商最优的库存承担时限。通过对比不同模式下供应链的整体绩效,找到实现供应链协调运作的必要条件,并通过数据分析进一步证明相关结论。     

产品服务升级下的契约设计与战略库存策略研究

零售企业进行升级产品服务的一个重要策略就是打造自有品牌。在考虑零售商拥有自有品牌以及具有战略库存能力的基础上,构建了两阶段博弈模型,得到了零售商和制造商在动态定价合约和价格承诺合约下的最优决策,探讨了自有品牌引入下供应链的最优契约设计。研究结果表明：当且仅当制造商的库存持有成本和自有品牌质量较低时,零售商才会在动态定价合约下持有战略库存。自有品牌对零售商的战略库存行为产生了抑制作用,自有品牌质量越高,战略库存水平越低。制造商更倾向于采用动态定价合约而零售商偏好价格承诺合约则仅出现在自有品牌的质量相对较低且库存持有成本处于中等水平的情况下;整体供应链对合约的倾向受到库存持有成本和自有品牌质量的双重影响;当零售商可选择最优的定价合约时,提高自有品牌质量不一定对制造商有害。     

变质率呈Weibull分布的一体化三级冷链库存策略研究

本文以有限期内一个生产商、多个分销商和多个零售商组成的冷链系统库存一体化决策为研究内容,建立了考虑可变运输成本、变质率服从三参数Weibull分布的三级冷链库存模型。文章从系统利润最大化的角度确定各个供应链成员的最佳补货策略,在与零售商主导的非一体化决策的对比分析中发现,库存一体化决策带来系统利润增加的同时也牺牲了部分成员的利益。与此同时,通过对变质率参数和运输成本参数的灵敏度分析发现,在运输距离一定的条件下,固定运输成本对最优库存策略的影响较大;在保鲜期一定情况下,变质率的尺度因子对系统总变质成本的影响较大。     

Multiple In‐Cycle Transshipments with Positive Delivery Times

We study a centralized inventory sharing system of two retailers that are replenished periodically. Between two replenishments, a unit can be transshipped to a stocked‐out retailer from the other. It arrives a transshipment time later, during which the stocked‐out retailer incurs backorder cost. Without transshipment, backorder cost is incurred until the next replenishment. Since the transshipment time is shorter than the time between two replenishments, transshipments can reduce the backorder cost at the stocked‐out retailer and the holding costs at the other retailer. The system is directed by a centralized inventory manager, who minimizes the long‐run average cost consisting of replenishment, holding, backorder, and transshipment costs. The transshipment policy is characterized by hold‐back inventory levels, which are nonincreasing in the remaining time until the next replenishment. The transshipment policy differs from those in the literature because we allow for multiple transshipments between replenishments, positive transshipment times, and backorder costs. We also discuss the challenges associated with positive replenishment time and develop upper and lower bounds of average cost in this case. Bounds are numerically shown to have an average gap of 1.1%. A heuristic solution is based on the upper bound and differs from the optimal cost by at most this gap.     

Inventory‐Based Dynamic Pricing with Costly Price Adjustment

We study an average‐cost stochastic inventory control problem in which the firm can replenish inventory and adjust the price at anytime. We establish the optimality to change the price from low to high in each replenishment cycle as inventory is depleted. With costly price adjustment, scale economies of inventory replenishment are reflected in the cycle time instead of lot size—An increased fixed ordering cost leads to an extended replenishment cycle but does not necessarily increase the order quantity. A reduced marginal cost of ordering calls for an increased order quantity, as well as speeding up product selling within a cycle. We derive useful properties of the profit function that allows for reducing computational complexity of the problem. For systems requiring short replenishment cycles, the optimal solution can be easily computed by applying these properties. For systems requiring long replenishment cycles, we further consider a relaxed problem that is computational tractable. Under this relaxation, the sum of fixed ordering cost and price adjustment cost is equal to (greater than, less than) the total inventory holding cost within a replenishment cycle when the inventory holding cost is linear (convex, concave) in the stock level. Moreover, under the optimal solution, the time‐average profit is the same across all price segments when the inventory holding cost is accounted properly. Through a numerical study, we demonstrate that inventory‐based dynamic pricing can lead to significant profit improvement compared with static pricing and limited price adjustment can yield a benefit that is close to unlimited price adjustment. To be able to enjoy the benefit of dynamic pricing, however, it is important to appropriately choose inventory levels at which the price is revised.     

部分短缺量拖后下基于顾客敏感度的补货策略适用范围研究

顾客对缺货数量和等待时间的敏感度影响着部分短缺量拖后率。基于此,根据顾客对缺货数量和等待时间的敏感度以及成本结构,对部分短缺量拖后下不同补货策略的适用范围进行了研究,得出以下结论:(s,S)连续性检查策略适用于单位缺货和丢单成本较高的库存系统;(t,S)周期性检查策略和(t,s,S)混合策略适用于单位缺货和丢单成本较低的库存系统;在单位缺货和丢单成本较低的库存系统中,当顾客对缺货数量较敏感时,(t,s,S)混合策略的运作成本更低,否则(t,S)周期性检查策略更适用,而顾客对等待时间的敏感度对补货策略适用范围的影响不明显。     

考虑订货协调成本与数量折扣的改良品供应链水平协调

在单个供应商与多个销售商构成的二级改良品供应链中，销售商们采用联合补货方式不仅可以分摊订货成本，而且由于订货批量的增加从而更易获得供应商提供的数量折扣合同。建立多个销售商在独立补货与联合补货中的利润水平函数，推导联合补货优于独立补货的充要条件。将销售商之间的联合补货行为转化成多人合作博弈问题，证明博弈具有的基本性质，设计基于博弈核心的利润分配方法。通过数值算例和敏感性分析验证了文中所构建模型的正确性。     

零售物流中心运作优化的一种T型补货策略

现代零售物流中心一般都包含数十万个大大小小的货位,每日需处理几万到数十万货位品类（SKU）的物流运作。如何合理存储货品并将货品从存储位补充至拣货位,即补货,是仓储管理中常见的瓶颈流程,直接决定了零售物流中心的作业效率。该研究基于虚拟商务理论提出了一种新的T型补货策略,即以拣货位为基点,呈T字形搜索寻址来上架存储货品,并按照先入先出和就近原则将货品补充至拣货位。与常规补货策略相比,T型补货策略不只是考虑单一货品的补充距离,更注重物流中心存储的整体合理性。最后以中国某百强连锁零售企业在上海的物流中心为例,抽取了采用T型补货策略前后的10000条数据并进行分析,结果显示T型补货策略极大地提升了物流中心的补货效率。     

考虑顾客缺货反应的多产品两阶段JIT联合订货

为了解决多产品两阶段联合订货决策问题,综合考虑顾客缺货反应及资源限制对订货决策的影响,提出资源限制条件下考虑CRS的多产品两阶段JIT联合订货策略。在此基础上,以零售商收益最大化为目标,建立了多产品两阶段JIT联合订货决策模型,并从初始解生成和殖民地国家革命方式两方面设计了改进的帝国主义竞争算法。从Gruen调查报告中选取三种不同规模的算例验证了模型和算法的适用性和有效性。研究表明：本文提出的考虑顾客缺货反应的多产品两阶段JIT联合订货方法既可以提高零售商的总收益,也能提高顾客的服务水平;当单位库存资源机会收益相近时,同组产品间不会发生替代情况,而当同组产品间的单位库存资源机会收益差距较大,且高于其他产品组的单位库存资源机会收益时,则该产品组便会出现缺货和替代情况,且若该组缺货产品的剩余资源单位机会收益小于其他组产品的单位资源机会收益,则该产品的剩余资源便会转移给其他缺货产品。研究结论可为零售商多产品两阶段JIT联合订货决策提供有益的参考。     

时变供需下基于商业信用的零售商最优订货策略

论文在时变供需环境下,即市场需求为斜坡式时间函数及补货率依赖于市场需求情境下,探讨了基于商业信用的供应链中零售商最优订货策略。首先,根据商业信用期和零售商补货周期的关系,分两种情形构建了零售商库存模型;其次,根据零售商的库存模型,证明了其最优解的存在,并给出相关的命题和算法来求解零售商的最优库存策略;最后,通过数值算例和灵敏度分析来论证文中的有关结论。研究发现:当供应商给予零售商的商业信用期较短时,此时市场需求仍处于增长期,随着市场需求稳定时间点的增大,零售商的最优订货周期逐渐减小,最优订货量和年费用也逐渐减小;当供应商给予零售商的商业信用期较长时,此时市场需求已趋于稳定期,随着市场需求稳定时间点的增大,零售商的最优订货周期逐渐增大,最优订货量和年费用也逐渐增大;当供应商生产与市场需求依赖性逐渐增大时,零售商的最优订货周期逐渐增大,最优订货量及年费用也逐渐增大。     

Replenishment Policies for Multi‐Product Stochastic Inventory Systems with Correlated Demand and Joint‐Replenishment Costs

This study analyzes optimal replenishment policies that minimize expected discounted cost of multi‐product stochastic inventory systems. The distinguishing feature of the multi‐product inventory system that we analyze is the existence of correlated demand and joint‐replenishment costs across multiple products. Our objective is to understand the structure of the optimal policy and use this structure to construct a heuristic method that can solve problems set in real‐world sizes/dimensions. Using an MDP formulation we first compute the optimal policy. The optimal policy can only be computed for problems with a small number of product types due to the curse of dimensionality. Hence, using the insight gained from the optimal policy, we propose a class of policies that captures the impact of demand correlation on the structure of the optimal policy. We call this class (s, c, d, S)‐policies, and also develop an algorithm to compute good policies in this class, for large multi‐product problems. Finally using an exhaustive set of computational examples we show that policies in this class very closely approximate the optimal policy and can outperform policies analyzed in prior literature which assume independent demand. We have also included examples that illustrate performance under the average cost objective.     

Replenishment Strategies for Micro‐Retailers in Developing Countries

When formal distribution channels are absent in developing countries, micro‐retailers travel a long distance to replenish their stocks directly from suppliers. This “informal” replenishment strategy is inefficient due to high imputed travel costs involved in the replenishment process. To improve efficiency, one “hybrid” replenishment strategy has emerged under which one of the micro‐retailers in a neighborhood, while continuing its own retail business, also operates as a wholesaler to serve other micro‐retailers in the neighborhood. A major obstacle for the transition from the informal strategy to the hybrid strategy is that the micro‐retailers are reluctant to source from a wholesaler who also competes with them in the retail market. Thus, it is not clear when the micro‐retailers adopt the hybrid strategy instead of the informal strategy. Meanwhile, the micro‐retailers may prefer the “formal” strategy: a traditional replenishment strategy under which one of the micro‐retailers relinquishes its retail business and operates purely as a wholesaler. We examine a situation when competing micro‐retailers contemplate with the three potential aforementioned replenishment strategies. Our equilibrium analysis of the two‐store model reveals that the dominant strategy is: (a) the hybrid strategy when the travel cost is high; (b) the formal strategy when the travel cost is medium; and (c) the informal strategy when the travel cost is low. This key insight is shown continue to hold when we extend the two‐store model to incorporate other issues including: quantity discounts from the supplier, variable operating costs, price competition, local monopolies, and different decision sequences. One additional finding is that the transition from the informal strategy to the hybrid strategy could benefit consumers if the micro‐retailers receive quantity discounts from the supplier. Furthermore, when there are more than two stores, we show that the formal strategy is never dominant. Moreover, when the number of stores increases, the hybrid strategy becomes more preferable to the informal strategy.