基于战略顾客行为的最优定价与容量选择模型

研究了当市场中同时存在战略顾客和短视顾客时零售商的最优定价与容量选择问题。零售商在正常销售阶段和出清销售阶段制定不同的销售价格,同时通过容量选择影响战略顾客的购买行为,而战略顾客则根据零售商的定价和容量选择确定最优购买时机。分别分析了零售商在无限容量时的定价决策、固定价格时的容量选择、固定容量时的定价决策以及有限容量下的定价与容量选择四种情形。研究结果表明,零售商在无容量限制时的最优定价决策是制定两阶段定价策略,在固定价格时的最优容量选择依赖于模型的参数,而当零售商的容量固定时,部分满足出清销售阶段的顾客需求始终优于完全满足出清销售阶段的顾客需求。     

新能源背景下考虑政府补贴的汽车供应链决策研究

研究了政府对新能源汽车实施政府补贴政策所采取的两种模式:直接补贴制造商(GM模式)与直接补贴销售商(GS模式),并建立了两种不同政府补贴模式的汽车供应链模型.通过比较不同模式下汽车供应链各方及政府补贴政策的最优决策,发现制造商作为市场的主导者,相比销售商来说具有更强的讨价还价能力;政府实施新能源汽车补贴政策时存在着政府补贴的支付转移,各节点企业一方受益必将带动另一方受益.随着消费者对新能源汽车的偏好增加,政府可以逐渐降低新能源汽车的政府补贴甚至取消新能源汽车的政府补贴政策,政府补贴政策对新能源汽车的发展起着强有力的促进和调节作用.     

努力效应下订单农业供应链融资中的政府补贴机制

本研究考虑一个农户和一个销售公司组成的订单农业供应链，其中，农户存在资金约束且农产品产出具有随机性。在政府不同农业补贴策略下，构建无补贴、补贴银行、补贴农户和补贴公司四种补贴策略下农户和公司的序贯博弈模型，研究不同补贴策略下的最优决策、收益以及社会福利。并在假设政府补贴支出相等的情况下，比较分析了不同补贴策略的补贴效果。研究表明：不同补贴策略下的订单农业供应链的最优决策，收益以及社会福利与产出波动性、资金期望收益水平负相关，与消费者的敏感系数和政府补贴率正相关，且产出波动性、资金期望收益水平、消费者的敏感系数和政府补贴率的变化不影响它们的大小排序。无论政府补贴谁，有政府补贴策略情况下，订单农业供应链的最优决策，收益以及社会福利高于无政府补贴策略时的情况，缓解农户融资压力，且在政府补贴银行时，农户的生产投入量、公司的促销努力水平和它们的收益以及社会福利最大。资金期望收益水平和消费者敏感系数影响政府的补贴率。     

顾客延时离开的共享车位预订控制策略研究

基于车位共享用户未按约定时间离开而导致车位使用时间冲突的问题,构建了随机动态规划模型。通过模型,研究了平台的预订控制策略,并针对平台能否拒绝延时需求的情形,设计两种算法对模型进行求解,最后通过数值仿真验证了算法的有效性。研究结果表明:(1)随着预订周期的增加,平台的边际收益会先增加后减少,因此存在最优预订周期;(2)若平台可以决策延时需求是否被接受,则随着延时概率的增加,机会成本将减少,而平台服务率和车位利用率将增加;(3)若平台只能被动接受延时需求,则随着延时概率的增加,平台服务率将减少,而机会成本和车位利用率将增加。     

两阶段再制造企业的产品定价研究

二手产品的回收再处理是众多制造商和再制造商不得不面对的问题.企业根据实际情况,考虑到顾客对再制造产品的认知差异,一般都是先翻新产品然后再升级再制造产品,因此,顾客对再制造产品的接受程度影响着再制造企业的定价决策,针对该问题构建了一个两阶段模型,其中第一阶段再制造企业将回收的产品进行简单翻新并投放市场;第二阶段,则会将产品拆卸加工再升级.以两阶段的价格作为决策变量分析再制造企业的最优生产策略.通过仿真得出结论,为再制造企业的生产决策提供一些依据.     

低碳供应链纵向减排合作下的政府补贴策略

为了研究低碳供应链减排合作的政府补贴问题,分别构建了在纳什博弈、斯塔克尔伯格博弈和供应链集中决策三种不同的博弈关系下,政府对制造商和零售商合作减排投入进行补贴的博弈模型。分析了企业的最优减排成本投入和政府的最优补贴率,并进一步讨论了政府补贴行为和企业选择减排合作行为之间的互动博弈。研究显示:政府提供的减排补贴和企业的收益随着供应链上下游合作的紧密程度不同而不同,在纳什博弈下最高,在集中决策下最低,斯塔克尔伯格博弈下介于两者之间。比较了力量不对等供应链中企业分别处于领导者和跟随者时获得的政府补贴的差异。同时在政府补贴行为和供应链企业合作行为的交互博弈中,政府的最优策略是不对任何一方实行减排补贴。     

顾客策略行为下基于联合促销努力的风险规避供应链协调模型

本文在考虑顾客策略行为情形下,利用条件风险度量准则建立了带有联合促销努力供应链协调模型,研究了风险规避对销售商和供应商决策行为的影响,得到了如下研究结论:(1)集中决策情形,价格承诺策略既可以激励供应商提高生产量,还能够有效降低顾客策略行为对其产生的负面影响。(2)分散决策情形,销售商风险规避度增大,销售商的订购量将增加,供应商的销售努力也将增加;供应商风险规避度增大,销售商的订购量将减小,供应商的销售努力也将减小;无论供应商和销售商的风险规避如何变化,销售商的销售努力不变。(3)利用回购和成本分担组成的混合契约可以实现供应链完美协调。     

在线多租赁选择问题的最优竞争策略

在线算法与竞争分析是研究信息不确定决策问题的一种新工具,应用该方法研究在线租赁问题是近年来国内外的一个研究热点。传统的在线租赁问题以经典的"雪橇租赁模型"为基础,考虑在线决策者可以选择购买或按单位时间租赁的方式来使用设备。然而现实租赁市场(比如汽车租赁,房屋租赁)往往提供多种租赁方式供在线决策者选择,除了按单位时间进行租赁,通常可以以一个较优惠的价格租赁多个单位时间。在这种现实背景下,本文建立了多种租赁形式下的在线租赁模型,给出了这种租赁模型下的确定性竞争策略,并证明该策略具有最优竞争比。     

基于顾客策略行为的两周期供应链动态定价,契约协调与利润分配机制

构建考虑顾客策略行为的两周期供应链博弈模型,分析分散式和集中式决策下,供应链成员的最优决策及顾客策略行为强度对各成员企业决策和利润的影响,研究两部定价契约和数量折扣契约的协调问题,提出一个基于成员议价能力的利润分配机制,并探讨顾客策略行为和成员议价能力对最优补偿金额的影响.主要结论为:1)顾客策略性越强,对供应链成员和总利润的负效应越大,且存在一个阈值,超过此阈值后,供应链各成员利润和总利润均降至最低限,但供应商利润总是高于零售商;2)两部定价契约和数量折扣契约能实现上述供应链的协调,但总有一参与方独占整个供应链的利润;3)基于成员议价能力的利润分配机制下,最优补偿金额与补偿提供方的议价能力负相关,顾客策略行为将导致补偿金额的降低.     

基于网络顾客行为意向的物流服务供应链定价策略

就一个运营网络购物的供应链,分析物流服务需求方和服务提供方的定价和服务水平决策等问题。在成本共担优化模型中考虑基于顾客购买行为意向的产品需求函数,进而分别给出非合作、准合作和完全合作模式下供应链企业决策间的关系,以及网购顾客重购概率对最优定价策略的影响。结论有:证明三种合作模式下双方最优策略的存在性及存在条件;给出最优产品定价策略和服务定价策略间的数量关系,并证明其与网购顾客行为意向有关。数值分析表明,最优定价策略随服务水平和网购顾客重购概率的变化趋势受成本共担系数的影响;较小的成本分摊系数使最优产品定价随着服务水平和网购顾客重购概率的变化幅度增大。     

基于收益管理和仿真的第三方仓储资源分配

针对不确定市场需求条件下第三方仓储资源的能力规划与分配问题,构建随机数学规划模型,理论分析证明了最优资源分配量的存在性,并指出最优资源分配量是单位资源成本的递减函数、单位资源收益和单位损失成本的递增函数。鉴于解析求解的复杂性,基于收益管理思想,结合离散事件仿真技术和响应曲面法,提出一种新的分析求解框架:收益管理用于细分顾客、构建资源分配策略,仿真模型刻画系统随机特性并评估系统绩效指标,响应曲面法则优化分配策略并探寻绩效改进方向。案例研究和仿真实验结果显示,根据顾客类别分配仓储能力的策略优于传统的先到先服务策略,收益管理、响应曲面法与仿真的综合集成,能够提高系统收益,从而使本文所提方法体系得到了有效验证。     

Optimal allocation of stock levels and stochastic customer demands to a capacitated resource

This paper considers a new class of stochastic resource allocation problems that requires simultaneously determining the customers that a capacitated resource must serve and the stock levels of multiple items that may be used in meeting these customers’ demands. Our model considers a reward (revenue) for serving each assigned customer, a variable cost for allocating each item to the resource, and a shortage cost for each unit of unsatisfied customer demand in a single-period context. The model maximizes the expected profit resulting from the assignment of customers and items to the resource while obeying the resource capacity constraint. We provide an exact solution method for this mixed integer nonlinear optimization problem using a Generalized Benders Decomposition approach. This decomposition approach uses Lagrangian relaxation to solve a constrained multi-item newsvendor subproblem and uses CPLEX to solve a mixed-integer linear master problem. We generate Benders cuts for the master problem by obtaining a series of subgradients of the subproblem’s convex objective function. In addition, we present a family of heuristic solution approaches and compare our methods with several MINLP (Mixed-Integer Nonlinear Programming) commercial solvers in order to benchmark their efficiency and quality.     

基于分类服务机制的可预约排队系统设计和服务定价研究

采取合理有效的服务机制和服务定价是确保服务型企业持续有效运行的重要方式,服务提供商设计合理的服务策略可以使得服务收益达到最优。本文以服务可预约的排队系统为研究背景,对服务提供商分类服务机制选择和服务定价策略进行研究。通过考虑由两种服务策略下顾客之间的平均等待时间的对比和顾客等待厌恶心理参数形成的全新顾客服务效用模型,给出服务提供商在不同服务机制下的最优服务策略以及顾客市场规模对其服务策略选择的影响。研究表明,当预约顾客的市场规模相对较小时,服务提供商可以采取分类服务机制,即同时服务预约顾客和未预约顾客以增加服务收益,反之亦然。此外,预约顾客等待厌恶程度能够显著增大服务提供商的服务定价。     

需求导向的容量设施竞争选址问题研究

客户意愿与容量限制是竞争设施选址问题中两个重要的影响因素,在考虑客户意愿与设施容量共同作用条件下,建立了最小化企业总成本以及每个客户费用为目标的竞争设施选址问题优化模型,通过设计需求导向服务分配机制解决设施与客户之间服务关系分配问题,结合模拟退火思想提出了求解模型的算法。最后利用数值例子分析了需求导向服务分配机制以及目标权重、预算限额等参数对于选址决策的影响,其中考虑需求导向因素会适当增加企业的总成本,但可以减少客户所付出的费用从而增强对客户的吸引力;另外企业的预算限额对于企业的设施选址决策有着重要的影响,企业所能获取的市场份额与其选址预算限额呈正相关的关系;而客户所需付出的总费用与企业提供服务的总成本两者之间则呈负相关的关系,因此需要通过服务质量与成本之间的权衡实现最理想的选址决策。     

Capacity allocation for demand of different customer-product-combinations with cancellations, no-shows, and overbooking when there is a sequential delivery of service

We consider a problem where different classes of customers can book different types of services in advance and the service company has to respond immediately to the booking request confirming or rejecting it. Due to the possibility of cancellations before the day of service, or no-shows at the day of service, overbooking the given capacity is a viable decision. The objective of the service company is to maximize profit made of class-type specific revenues, refunds for cancellations or no-shows as well as the cost of overtime. For the calculation of the latter, information of the underlying appointment schedule is required. Throughout the paper we will relate the problem to capacity allocation in radiology services. Drawing upon ideas from revenue management, overbooking, and appointment scheduling we model the problem as a Markov decision process in discrete time which due to proper aggregation can be optimally solved with an iterative stochastic dynamic programming approach. In an experimental study we successfully apply the approach to a real world problem with data from the radiology department of a hospital. Furthermore, we compare the optimal policy to four heuristic policies, of whom one is currently in use. We can show that the optimal policy significantly improves the currently used policy and that a nested booking limit type policy closely approximates the optimal policy and is thus recommended for use in practice.     

基于收益管理理论的门诊预约存量控制研究

门诊预约机制能够帮助医疗服务系统实现合理的资源组织与顺畅的服务流程,目前是医疗系统管理中的热点问题。论文分析了收益管理的适用性条件,提出其在医院门诊预约调度问题中应用的可行性,然后构建了门诊预约存量控制的Littlewood基础模型,并考虑患者爽约的情况,建立了超订策略下的门诊预约存量控制模型,最终用实例验证了所提出方法的实用性和有效性。     

A framework for solving network revenue management problems with customer choice behavior

Revenue management is the process of understanding, anticipating and influencing consumer behavior in order to maximize revenue. Network revenue management models attempt to maximize revenue when customers buy bundles of multiple resources. The dependence among the resources in such cases is created by customer demand. Network revenue management can be formulated as a stochastic dynamic programming problem whose exact solution is computationally intractable. Solutions are based on approximations of various types. Customer choice behavior modeling has been gaining increasing attention in the revenue management. A framework for solving network revenue management problems with customer choice behavior is proposed. The modeling and solving framework is composed from three inter-related network structures: basic network model, Petri net, and neural net.     

Multi-source facility location–allocation and inventory problem

We consider a joint facility location–allocation and inventory problem that incorporates multiple sources of warehouses. The problem is motivated by a real situation faced by a multinational applied chemistry company. In this problem, multiple products are produced in several plants. Warehouse can be replenished by several plants together because of capabilities and capacities of plants. Each customer in this problem has stochastic demand and certain amount of safety stock must be maintained in warehouses so as to achieve certain customer service level. The problem is to determine number and locations of warehouses, allocation of customers demand and inventory levels of warehouses. The objective is to minimize the expected total cost with the satisfaction of desired demand weighted average customer lead time and desired cycle service level. The problem is formulated as a mixed integer nonlinear programming model. Utilizing approximation and transformation techniques, we develop an iterative heuristic method for the problem. An experiment study shows that the proposed procedure performs well in comparison with a lower bound.     

Mathematical programming models for revenue management under customer choice

We study a network airline revenue management problem with discrete customer choice behavior. We discuss a choice model based on the concept of preference orders, in which customers can be grouped according to a list of options in decreasing order of preference. If a customer’s preferred option is not available, the customer moves to the next choice on the list with some probability. If that option is not available, the customer moves to the third choice on the list with some probability, and so forth until either the customer has no other choice but to leave or his/her request is accepted. Using this choice model as an input, we propose some mathematical programs to determine seat allocations. We also propose a post-optimization heuristic to refine the allocation suggested by the optimization model. Simulation results are presented to illustrate the effectiveness of our method, including comparisons with other models.     

Revenue Models and Policies for the Car Rental Industry

In this paper, we consider the application of revenue management techniques in the context of the car rental industry. In particular, the paper presents a dynamic programming formulation for the problem of assigning cars of several categories to different segments of customers, with rental requests arising dynamically and randomly with time. Customers make a rental request for a given type of car, for a given number of days at a given pickup time. The rental firm can satisfy the demand for a given product with either the product requested or with a car of at most one category superior to that initially required, in this case an “upgrade” can take place. The one-way rental scenario, which allows the possibility of the rental starting and ending at different locations, is also addressed. In the framework considered, the logistic operator has to decide whether to accept or reject a rental request. Since the proposed dynamic programming formulations are impractical due to the curse of dimensionality, linear programming approximations are used to derive revenue management decision policies for the operator. Indeed, primal and dual acceptance policies are developed (i.e. booking limits, bid prices) and their effectiveness is assessed on the basis of an extensive computational phase.