需求时间不确定下的多供应商配套供货模型研究

本文在基于Supply Hub的运作模式下,研究了N个配套供应商对单个制造商的单周期准时供货模型。当制造商的需求时间不确定时,供应商需要依据预期成本最小化来确定各自最优的供货时间。通过找到各个供应商在信息封闭和信息共享这两种模式下的最优供货时间决策,并且对比不同模式下供应商成本和制造商服务水平,证明了配套供应商之间通过一定程度的信息共享能够有效降低成本和提高服务水平。最后提出一种基于Supply Hub主导的多供应商准时供货策略,从而实现了系统整体绩效的优化。     

线性量折扣方案下团购策略对渠道绩效的影响

以包含一个供应商和两个寡头零售商的分销渠道为研究对象,分别在正常运作情形和需求突变情形下研究了团购策略对于渠道绩效的影响。通过对正常运作情形下的零售商单独购买策略和团购策略建立非合作博弈模型,分析了两种策略所对应的纳什均衡解的性质,比较了单独购买和团购策略下均衡战略的区别,并总结了团购策略对于渠道及其成员最优利润的影响。另外,还就对称性零售商和非对称性零售商的纳什均衡战略进行了比较静态分析,研究了量折扣系数及零售商运作效率差异对于纳什均衡战略及其支付的影响。然后,考虑渠道面临需求突变的情形,重点分析了团购策略应对需求突变的效率。研究表明：（1）正常运作情形下,团购策略可能使得渠道绩效得到改善,但若零售商为非对称的,这一结论并不总是成立;（2）需求突变情形下,通过合理设置零售商承担偏离成本的比例,团购策略总能使得渠道绩效得以改进;（3）团购策略有益于稳定产品的市场价格。     

基于通用件的ATO系统对家电行业的影响分析

面对消费者个性化的需求以及家电产品更新换代的加快，家电行业MTS的供应链模式逐渐体现其局限性：产品库存积压、旧产品零部件难以配型、消费者个性化需求不能满足等。基于通用件的ATO系统通过风险混同，减少了通用件的安全库存，缩小了成本，更大满足了消费者需求。本文分别建立了单期与多期最小成本模型，比较有通用件ATO系统与没有通用件MTS系统成本。证明有通用件ATO模型可以减少成本，同时减少通用件库存。。在一定服务水平前提下，当通用件的价格比其替代零部件的价格增加百分比不超过16％时，无论是单期模型还是多期模型（缺货不可补），采用通用件成本更低。而对于多期模型（缺货可补时），在一定服务水平前提下，当通用件的价格比其替代零部件的价格增加酉分比不超过0．5％时，采用通用件成本更低。依据家电行业的现状，只需对零部件进行合理的模块化设计以及与零售企业建立信息交换系统，就能很好的移植ATO系统，但各家电企业还需根据自身情况，斟酌利弊，权衡ATO系统带来的成本的节省与移植系统带来的额外成本的影响。     

众包供应链基于On/OffLine混合定制设计生产决策模型分析

互联网平台经济（Platform Economy）正颠覆传统企业商业模式，众包供应链（Crowdsourcing Supply Chain，CSC）作为一种新型的"互联网平台+设计创新"供应链，也正成为人们关注和研究的热点。本文在订单定制设计模式（Engineering to Order，ETO）的基础上，将定制设计和订单生产两环节相结合，并以交货期为驱动，按常规生产时间和加班生产时间来优化生产流程。在此框架下，建立了线下（Offline）自行定制设计的供应链生产模型；接着结合互联网众包平台的特点，以及众包线上（Online）和线下（Offline）的互补性，将众包线上定制设计环节有机嵌入融合到线下自行定制设计供应链中，以此优化和构建出基于众包的线上线下混合定制设计的供应链生产决策模型，设计出众包线上定制设计和线下定制设计动态切换条件；并通过粒子群算法（Particle Swarm Optimization，PSO）对上述模型进行求解；最后通过实例进行分析，发现定制订单数量不多时，线上线下混合定制设计对成本的降低不是很显著，但随着订单数量越多，线上线下混合定制设计优势将显著变化，并且具有一定程度的抗风险性；通过这个转换点也发现，众包定制设计的订单生产最好安排在期初和期末，众包线上定制设计订单应尽可能减少挤占线下自行设计的常规生产时间，而应转向在加班时间生产更为经济；同时，通过增加对众包设计者的设计报酬，发现不仅对整个供应链的成本影响不大，反而对众包设计者形成较大激励作用，进一步证明该模式的可行性和实用性。     

价格、质量与服务竞争情形下的二级供应链系统协调策略分析

本文研究了由一个零售商向两个制造商购买产品，在产品价格、质量和服务竞争同时作用下，供应链成员的协调运作策略。通过构建一条包括制造商、零售商和顾客的二级供应链模型，应用逆向归纳法以及最优化理论，求解供应链在集中决策、制造商不合作、制造商合作以及半合作（零售商与其中一个制造商合作）情形下供应链成员的博弈均衡。分析四种合作关系下两种竞争产品关于质量和服务的均衡解，并且分析消费者剩余。运用数值仿真讨论各种参数变化带来的影响。结果表明：制造商不合作情形虽然不能提高产品的服务水平，但是能够提高制造商的质量努力程度；零售商的集中协调有助于提升产品质量和服务水平，避免制造商之间合作可以为顾客提供较高质量和服务水平的产品，使顾客收益，增加市场中顾客总量，同时可以提升供应链的整体利润。     

提高顾客等待满意度的两类排队管理策略

现代等待心理学认为:好的排队管理能极大地提高顾客的等待满意度.基于这一点,论文通过M/M/1排队系统研究了两类排队管理策略:一是为队列前N名顾客提供额外服务的策略H;二是为队列中处于N之后的顾客提供额外服务的策略T.通过分析期望顾客等待满意度和期望额外服务成本,论文给出了企业在给定额外服务预算下的最大化顾客等待满意度的最优策略.数值算例显示:当预算足够大时,若系统的服务强度不大或者有无额外服务对顾客等待时间敏感程度的影响很大,策略H优于策略T;当预算不充足,且服务强度不是很大时,策略T优于策略H.若预算少且服务强度大,两类策略对提高顾客等待满意度基本不起作用.     

顾客可选末端交付方式和时间窗的城市配送动态订单接受优化研究

在顾客可以自选末端交付方式和配送时间窗的情境下，为及时决策是否接受顾客配送订单，构建了城市配送动态订单接受决策框架，定义了配送路径预规划、配送需求评估、订单接受策略调整和路径全局优化四个阶段，建立了基于时间窗偏差阈值的配送需求评估方法，设计了全部接受、交付方式静态分配和服务选项动态分配三种订单接受策略调整算法。仿真表明：服务选项动态分配算法能够获取更高的利润，在单批次配送任务较多的情形下还具有更好的时间性能；随着时间窗宽度逐渐增加，自提柜交付收益和行驶里程逐渐减少，而送货上门交付收益和利润逐渐增加；时间窗偏差阈值对配送服务利润影响明显，但不存在趋势性关系；仿真算例显示，送货上门交付收益较高的时间窗偏差阈值，往往会获取更高的利润。     

政府动态奖惩机制下重大疫情防控演化博弈研究

重大疫情的爆发对于人类健康和社会经济都具有极大的负面影响,当前控制疫情扩散的主要手段是做好隔离防控。在隔离防控的过程中,政府部门和社会公众存在着一种博弈关系。文章分别在静态和三种动态奖惩机制下构建了政府部门和社会公众的博弈矩阵,重点分析了防控成本、奖励上限值和惩罚上限值对博弈系统演化稳定策略的影响,最后进行了算例仿真分析。研究结果表明：政府部门采取静态奖惩机制时,不存在演化稳定策略,采用动态奖惩机制可以有效地弥补静态奖惩机制的不足,实现演化稳定状态;动态奖励与静态惩罚机制在疫情防控中优于其他动态奖惩机制;社会公众采取自愿隔离的概率与防控成本、奖励上限值呈负相关,与惩罚上限值呈正相关。     

网上超市拆分订单合并打包策略经济决策模型

针对"一地多仓型"网上超市面临的订单拆分难题,以减少订单的拆分包裹数为切入点,提出采用基于仓库的合并打包策略来履行拆分的订单,从而有效降低配送成本,减少顾客扰动和包装废弃物对环境的污染.首先分别建立订单拆分策略和合并打包策略下的履行成本模型,然后在此基础上构建合并打包策略的经济决策模型.从订单规模、平均拆单数这两个关键影响要素对该经济决策模型进行分析,从而得出合并打包策略的经济适用范围,给出管理启示.算例结果验证了模型分析的结论,为网上超市等电商零售企业采用合并打包策略解决拆单难题提供理论指导和决策支持.     

政府动态奖惩机制下绿色建筑供给侧演化博弈研究

从绿色建筑供给侧角度出发，构建消费者效用函数、开发商利润函数和政府与开发商的演化博弈模型，分析消费者绿色偏好、建筑绿色度、成本等因素对建筑价格、需求和利润的影响以及政府动态奖惩政策下系统的演化稳定策略。结果表明：消费者绿色偏好支付系数和建筑绿色度的增大有利于绿色建筑价格、需求和利润的提高，但高成本却使需求和利润降低；政府采用静态补贴与静态税收政策时，博弈系统不存在演化稳定策略，采用动态补贴与静态税收、静态补贴与动态税收、动态补贴与动态税收三种政策组合时，系统存在演化稳定策略；动态补贴与静态税收的政策组合在推动绿色建筑发展方面优于其他政策；开发商开发绿色建筑的概率与补贴上限值呈负相关，与税收上限值呈正相关。     

Optimal Policies for Selling New and Remanufactured Products

Because of environmental and economic reasons, an increasing number of original equipment manufacturers (OEMs) nowadays sell both new and remanufactured products. When both products are available, customers will buy the one that gives them a higher (and non‐negative) utility. Thus, if the firm does not price the products properly, then product cannibalization may arise and its revenue may be adversely impacted. In this paper, we study the pricing problem of a firm that sells both new and remanufactured products over a finite planning horizon. Customer demand processes for both new and remanufactured products are random and price‐sensitive, and product returns (also called cores) are random and remanufactured upon receipt. We characterize the optimal pricing and manufacturing policies that maximize the expected total discounted profit. If new products are made‐to‐order (MTO), we show that when the inventory level of remanufactured product increases, the optimal price of remanufactured product decreases while the price difference between new and remanufactured products increases; however, the optimal selling price of new product may increase or decrease. If new products are made to stock (MTS), then the optimal manufacturing policy is of a base‐stock policy with the base‐stock level decreasing in the remanufactured product inventory level. To understand the potential benefit in implementing an MTO system, we study the difference between the value functions of the MTO and MTS systems, and develop lower and upper bounds for it. Finally, we study several extensions of the base model and show that most of our results extend to those more general settings.     

A hybrid inventory management system responding to regular demand and surge demand

This paper proposes a hybrid policy for a stochastic inventory system facing regular demand and surge demand. The combination of two different demand patterns can be observed in many areas, such as healthcare inventory and humanitarian supply chain management. The surge demand has a lower arrival rate but higher demand volume per arrival. The solution approach proposed in this paper incorporates the level crossing method and mixed integer programming technique to optimize the hybrid inventory policy with both regular orders and emergency orders. The level crossing method is applied to obtain the equilibrium distributions of inventory levels under a given policy. The model is further transformed into a mixed integer program to identify an optimal hybrid policy. A sensitivity analysis is conducted to investigate the impact of parameters on the optimal inventory policy and minimum cost. Numerical results clearly show the benefit of using the proposed hybrid inventory model. The model and solution approach could help healthcare providers or humanitarian logistics providers in managing their emergency supplies in responding to surge demands.     

航空客运舱位控制和超售综合静态建模研究

本文研究航空运输收益管理的舱位控制和超售综合静态建模问题.通过将机票销售过程模拟成排队过程,以收益最大化为目标函数,首先给出了单航段单等级票价下的超售水平公式.然后将该思路推广到多等级票价情况,应用动态规划方法建立了舱位控制和超售综合控制静态模型,在建立了两个定理的基础上,由该模型进一步推导出了各等级舱位最优订座限制的决策方程.最后分析了一个实例以说明决策方程的应用.     

A new MRP/GT lot sizing heuristic: A simulation study

This paper studies the lot-sizing problem in Material Requirements Planning/Group Technology (MRP/GT) systems. A GT production cell is designed to produce many families of components. A major setup is required when switching from manufacturing one family of components to another family, and a minor setup is needed when switching from manufacturing a component type to another component type within the same family. Inventory holding cost is incurred if inventory level is positive, and inventory shortage cost is incurred if inventory level is negative, that is, backordering. The objective of the proposed lot-sizing problem is to minimize the sum of major and minor setup costs, holding and shortage costs, and regular production cost, and to meet simultaneously the demand requirements. The proposed problem is modelled into a linear integer program, a heuristic method to solve the problem is proposed, and a simulation experiment conducted to evaluate the performance of the proposed heuristic and some existing heuristics. The computational results show that the proposed heuristic is useful to reduce the total cost significantly over a wide variety of simulated environments.     

Dynamic performance of a hybrid inventory system with a Kanban policy in remanufacturing process

In this paper we study a hybrid system with both manufacturing and remanufacturing. The inventory control strategy we use in the manufacturing loop is an automatic pipeline, inventory and order based production control system (APIOBPCS). In the remanufacturing loop we employ a Kanban policy to represent a typical pull system. The methodology adopted uses control theory and simulation. The aim of the research is to analyse the dynamic (as distinct from the static) performance of the specified hybrid system. Dynamics have implications on total costs in terms of inventory holding, capacity utilisation and customer service failures. We analyse the parameter settings to find preferred “nominal”, “fast” and “slow” values in terms of system dynamics performance criteria such as rise time, settling time and overshoot. Based on these parameter settings, we investigate the robustness of the system to changes in return yield and the manufacturing/remanufacturing lead time. Our results clearly show that the system is robust with respect to the system dynamics performance and the remanufacturing process can help to improve system dynamics performance. Thus, the perceived benefits of remanufacturing of products, both environmentally and economically, as quoted in the literature are found not to be detrimental to system dynamics performance when a Kanban policy is used to control the remanufacturing process.     

基于患者两阶段医疗服务过程的病床资源优化

随着医疗需求的日益增长，供需不平衡的矛盾日益显著，病床资源短缺已经成为制约医疗服务发展的重要因素之一。针对病床资源供需失衡的问题，本文构建患者两阶段医疗服务过程的病床资源优化模型。考虑患者的医疗服务是一个两阶段串联排队过程，第一阶段是强制治疗阶段，第二阶段是康复阶段。在患者排队系统中，服务台总数固定的前提下，本文利用增补变量方法给出了该排队系统的概率等式，通过求解偏微分方程得出系统的状态概率，并推导出医院拒绝患者的概率、平均康复时间等相关指标。考虑部分患者因病床资源不足而无法入院和康复阶段患者提前出院两种风险因素，本文基于排队指标构建多病种间的病床分配模型，再利用动态规划的求解思路得到病床分配的最优解。最后，本文采用2017年卫生统计数据和模拟数据相结合进行数值分析。研究结果表明与传统单阶段模型相比，两阶段模型对不同病种间病床数差别影响显著，并且患者入院率对病床分配影响较大。进一步，基于数值结果给出了医疗管理建议和研究展望。     

持有成本和变质率时变的非立即变质品库存策略

本文主要研究非立即变质品的库存策略设计问题。以平均利润最大化为决策目标,构建了一个一般化的库存模型,假设需求受即时库存水平影响且在保鲜期和变质期内库存水平对需求的影响系数不同,持有成本和变质期内的变质率均随时间发生变化,系统允许缺货且短缺量部分延迟订购。进一步证明了当参数满足一定条件时,模型存在唯一的最优解。最后,采用牛顿法给出一些具体的数值算例,并对模型中主要参数的灵敏度进行了分析。结果显示:延长物品的保鲜期将有助于增加系统的平均利润;增加变质期内的需求比增加保鲜期内的需求更有利于增加系统的平均利润;对于变质率高的产品来说,其变质处理成本对最优策略和平均利润的影响不容忽视。此外,本文所构建的模型具有一般性,因此,其应用范围更加广泛。     

Point‐of‐Use Hybrid Inventory Policy for Hospitals

Modern point‐of‐use technology at hospitals has enabled new replenishment policies for medical supplies. One of these new policies, which we call the hybrid policy, is currently in use at a large U.S. Midwest hospital. The hybrid policy combines a low‐cost periodic replenishment epoch with a high‐cost continuous replenishment option to avoid costly stockouts. We study this new hybrid policy under deterministic and stochastic demand. We develop a parameter search engine using simulation to optimize the long‐run average cost per unit time and, via a computational study, we provide insights on the benefits (reduction in cost, inventory, and number of replenishments) that hospitals may obtain by using the hybrid policy instead of the commonly used periodic policies. We also use the optimal hybrid policy parameters from the deterministic analysis to propose approximate expressions for the stochastic hybrid policy parameters that can be easily used by hospital management.      

Sourcing from Multiple Suppliers for Price‐Dependent Demands

We analyze a model that integrates demand shaping via dynamic pricing and risk mitigation via supply diversification. The firm under consideration replenishes a certain product from a set of capacitated suppliers for a price‐dependent demand in each period. Under deterministic capacities, we derive a multilevel base stock list price policy and establish the optimality of cost‐based supplier selection, that is, ordering from a cheaper source before more expensive ones. With general random capacities, however, neither result holds. While it is optimal to price low for a high inventory level, the optimal order quantities are not monotone with respect to the inventory level. In general, a near reorder‐point policy should be followed. Specifically, there is a reorder point for each supplier such that no order is issued to him when the inventory level is above this point and a positive order is placed almost everywhere when the inventory level is below this point. Under this policy, it may be profitable to order exclusively from the most expensive source. We characterize conditions under which a strict reorder‐point policy and a cost‐based supplier‐selection criterion become optimal. Moreover, we quantify the benefit from dynamic pricing, as opposed to static pricing, and the benefit from multiple sourcing, as opposed to single sourcing. We show that these two strategies exhibit a substitutable relationship. Dynamic pricing is less effective under multiple sourcing than under single sourcing, and supplier diversification is less valuable with price adjustments than without. Under limited supply, dynamic pricing yields a robust, long‐term profit improvement. The value of supply diversification, in contrast, mainly comes from added capacities and is most significant in the short run.     

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.