树形供应链中断风险应急模型研究

基于战略应急库存与实物期权组合策略,设计了树形供应链中断风险应急模型,并通过求解模型得到系统最优策略.应急模型既考虑了风险防范与应急供应所引发的成本,同时考虑了供应链系统中断导致的损失收益.最后进行了仿真分析,结果表明应急模型能够显著降低树形供应链系统的中断风险成本与系统中断时间.     

具有Weibull生存死亡特征的冷链品库存补货定价策略研究

建立了无限期内冷链品具有Weibull生存死亡特征、随机需求且受售价影响的库存补货定价模型,其中售价是连续变化的,需求率是售价的指数函数,变质率服从的三参数Weibull分布,提前期固定。系统以利润最大化为目标函数,在(r,Q)库存策略下,建立库存模型,采用直接法,对模型近似求解,得到最优补货定价策略。利用Matlab进行算例模拟和灵敏度分析发现:补货提前期和单位仓储成本对补货定价策略影响较大,二者增大会导致系统利润降低;单位处理成本的增加,在一定程度上使得系统降低最优补货量,使系统利润增加;保鲜期固定的前提下,受冷链品的流动环境因子和存储环境因子影响的变质率对补货定价策略影响较大,它的增大会使系统利润降低。这些发现能够帮助优化系统模型,对现实问题具有一定的指导意义。     

考虑顾客使用行为的汽车租赁容量分配策略研究

随着物联网技术的发展, 租赁公司通过智能技术可以实时监测顾客的使用行为, 因此可以根据顾客使用行为设计补贴策略以激励顾客在使用过程中保持良好的行为习惯。本文将租赁价格作为顾客行为的函数, 构建随机动态规划模型, 研究了多产品、多周期下汽车租赁公司的容量分配决策和补贴机制。考虑到所构建模型的状态变量维度较高, 因此提出两种近似算法对模型进行求解, 并通过数值仿真验证了模型的相关性质。在考虑顾客行为可以转变的前提下, 得到相关结论:租赁公司以机会成本作为容量分配决策的重要依据;当所需等级汽车缺货时, 由于低等级汽车的机会成本低于高等级汽车的机会成本, 因此满足升级条件时, 租赁公司总是按照等级顺序进行升级;在合理的补贴策略下, 公司的总收益将会随着补贴的增加而增加。     

集中式供应链的生产与库存分配联合决策

考虑一个由单一制造商和多销售点构成的二级集中式供应链,制造商采用积欠订货策略生产和销售产品,因而面临生产、运输和积欠订货型库存分配与补货的联合决策问题.综合考虑了延迟交货成本、库存持有成本、运输成本和生产成本,建立了最大化总利润的非线性混合整数规划模型,以联合优化供应链的生产、运输及库存分配方案.证明了最优的产品生产分配方案应该具有的结构性质:各个销售网点在任意销售间隔期末同时出现缺货或者库存剩余.同时,提出了基于逐级求解策略的分层求解算法,保证了解的全局最优性.最后,敏感性分析表明:单位产品的库存持有成本与延迟交货成本对最优分配次数及生产量的影响作用相反,固定运输成本也会影响制造商的分配方案及生产计划,但三者均不会影响每个间隔期末的产品分配方案(分配量).     

政府补贴方式下排放许可交易生产优化

从学术上探讨了政府补贴方式下有排放许可交易的生产优化问题.分析了配额和补贴方式效果在管理上的差异,给出了补贴方式下的各经济核算关系及模型,给出了最低净化水平定理的证明;给出优化计算,求解了最优净化水平,并探讨了其与最优产量和企业最优收益间的关系;给出仿真计算,并对企业生产在这种支持方式下的优化管理进行了分析.研究表明:企业将对生产中产生的全部排放物进行相同净化水平α的处理时,所需要的处理费用最低;在线性反需求函数下,最优产量的变化量只与净化成本函数、排放交易价格、补助价格等几个量相关,最优收益与最优产量的平方成正比.     

基于“零和收益”的SBM效率分配方法及应用

在松弛变量度量(slacks-based measure,SBM)效率评价方法的基础上,首先明确投入(产出)要素固定的生产系统中,投入(产出)要素在各决策单元间的竞争性关系;然后采用比例分配策略对SBM无效决策单元的投入(产出)松弛进行效率分配,以构建一个基于零和收益的SBM(zero sum gains SBM,ZSG-SBM)效率分配方法;再通过分析ZSG-SBM模型与SBM模型效率评价结果的关系,给出了比例分配策略ZSG-SBM模型的求解方法;最后应用实例研究验证了本文模型在要素存在竞争性的复杂生产系统效率评价和资源分配中的优势。     

具有两类平行顾客的M/M/1重试排队系统均衡策略研究

本文研究具有两类平行顾客且服务台可靠的M/M/1重试排队系统的均衡策略.在该排队系统中,两类顾客平行到达,并服从不同参数的负指数分布.当顾客进入系统时,若观察到服务台为空,将立刻开始服务;若观察到服务台处于忙期,则进入重试空间等待重试.在完全可见和几乎可见两种情形下,基于“收益-成本”理论提出合理的效用函数并对两类平行顾客进行均衡分析.此外,建立单位时间的社会收益函数,给出最优社会效益分析.最后运用数值分析直观地表示出随着系统参数的改变,顾客行为策略的变化情况.     

考虑互惠公平的创新顾客激励契约研究

基于公平偏好理论的互惠公平,从创新顾客的互惠偏好程度、激励契约类型与激励效果的关系入手,构建创新顾客参与企业创新活动的激励模型。通过模型求解和分析,探讨激励契约的外部性和互惠关系对于激励效果的影响;此外,进一步分析如何联合经济激励和心理激励,降低企业获得创新顾客高努力投入的成本;最后,通过仿真实验,验证模型分析结果。研究发现:当创新顾客之间出现互惠关系时,最优激励契约取决于创新顾客心理偏好与风险态度之间的相互作用:当创新顾客的风险规避程度较低时,最优激励契约为相对绩效契约;当创新顾客的风险规避程度较高时,最优激励契约为团队报酬契约。     

客源充足情境下的customer-intensive服务策略研究

在customer-intensive服务中服务速度越慢,顾客的效用就越高,然而等待时间也随之变长;而服务商则需对服务速度和价格进行决策,以求获得最优收益。本文基于客源丰富的服务垄断商对此问题采用M/M/1排队模型进行了研究,将顾客成本细分为时间成本和焦虑成本,给出了最优服务速度和价格。研究发现,单位焦虑成本的增加造成了服务商收益的减少,但对收益的影响要小于单位时间成本。最后,提出了服务商投入一定的服务成本来减少焦虑成本的策略,以达到获取更高收益的目的,并证明了策略的有效性。     

绿色消费视角的政企合作绿色技术创新动态决策

通过构建微分博弈模型,对比分析无成本分担、成本分担及集中式决策情形下,政府和企业最优反馈均衡策略和收益最优值函数.研究发现,成本分担情形下,企业绿色技术创新努力程度要高于无成本分担情形,该努力程度不仅取决于自身的边际收益,还与政府边际收益、企业技术创新社会收益正相关;集中决策情形下,政府、企业的最优努力水平和系统收益最优值均达到最高水平,且与二者的边际收益之和正相关;社会公众绿色消费的关注程度对系统收益具有正向影响,且影响程度边际递增;各参与主体努力程度对企业社会声誉影响越大,则合作系统收益越大.     

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.     

Resource capacity allocation to stochastic dynamic competitors: knapsack problem for perishable items and index-knapsack heuristic

In this paper we propose an approach for solving problems of optimal resource capacity allocation to a collection of stochastic dynamic competitors. In particular, we introduce the knapsack problem for perishable items, which concerns the optimal dynamic allocation of a limited knapsack to a collection of perishable or non-perishable items. We formulate the problem in the framework of Markov decision processes, we relax and decompose it, and we design a novel index-knapsack heuristic which generalizes the index rule and it is optimal in some specific instances. Such a heuristic bridges the gap between static/deterministic optimization and dynamic/stochastic optimization by stressing the connection between the classic knapsack problem and dynamic resource allocation. The performance of the proposed heuristic is evaluated in a systematic computational study, showing an exceptional near-optimality and a significant superiority over the index rule and over the benchmark earlier-deadline-first policy. Finally we extend our results to several related revenue management problems.     

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.     

Stochastic dynamic lot-sizing problem using bi-level programming base on artificial intelligence techniques

Simulation is generally used to study non-deterministic problems in industry. When a simulation process finds the solution to an NP-hard problem, its efficiency is lowered, and computational costs increase. This paper proposes a stochastic dynamic lot-sizing problem with asymmetric deteriorating commodity, in which the optimal unit cost of material and unit holding cost would be determined. This problem covers a sub-problem of replenishment planning, which is NP-hard in the computational complexity theory. Therefore, this paper applies a decision system, based on an artificial neural network (ANN) and modified ant colony optimization (ACO) to solve this stochastic dynamic lot-sizing problem. In the methodology, ANN is used to learn the simulation results, followed by the application of a real-valued modified ACO algorithm to find the optimal decision variables. The test results show that the intelligent system is applicable to the proposed problem, and its performance is better than response surface methodology.     

A Markov model for single-leg air cargo revenue management under a bid-price policy

In this paper, we consider the capacity allocation problem in single-leg air cargo revenue management. We assume that each cargo booking request is endowed with a random weight, volume and profit rate and propose a Markovian model for the booking request/acceptance/rejection process. The decision on whether to accept the booking request or to reserve the capacity for future bookings follows a bid-price control policy. In particular, a cargo will be accepted only when the revenue from accepting it exceeds the opportunity cost, which is calculated based on bid prices. Optimal solutions are derived by maximizing a reward function of a Markov chain. Numerical comparisons between the proposed approach and two existing static single-leg air cargo capacity allocation policies are presented.     

Admission control strategies for tandem Markovian loss systems

Motivated by communication networks, we study an admission control problem for a Markovian loss system comprised of two finite capacity service stations in tandem. Customers arrive to station 1 according to a Poisson process, and a gatekeeper, who has complete knowledge of the number of customers at both stations, decides whether to accept or reject each arriving customer. If a customer is rejected, a rejection cost is incurred. If an admitted customer finds that station 2 is full at the time of his service completion at station 1, he leaves the system and a loss cost is incurred. The goal is to find easy-to-implement policies that minimize long-run average cost per unit time. We formulate two intuitive, extremal policies and provide analytical results on their performances. We also present necessary and/or sufficient conditions under which each of these policies is optimal. Next, we show that for some states of the system it is always optimal to admit new arrivals. We also fully characterize the optimal policy when the capacity of each station is two and discuss some characteristics of optimal policies in general. Finally, we design heuristic admission control policies using these insights. Numerical experiments indicate that these heuristic policies yield near-optimal long-run average cost performance.     

Dynamic cruise ship revenue management

Recently, it has been recognized that revenue management of cruise ships is different from that of airlines or hotels. Among the main differences is the presence of multiple capacity constraints in cruise ships, i.e., the number of cabins in different categories and the number of lifeboat seats, versus a single constraint in airlines and hotels (i.e., number of seats or rooms). We develop a discrete-time dynamic capacity control model for a cruise ship characterized by multiple constraints on cabin and lifeboat capacities. Customers (families) arrive sequentially according to a stochastic process and request one cabin of a certain category and one or more lifeboat seats. The cruise ship revenue manager decides which requests to accept based on the remaining cabin and lifeboat capacities at the time of an arrival as well as the type of the arrival. We show that the opportunity cost of accepting a customer is not always monotone in the reservation levels or time. This non-monotone behavior implies that “conventional” booking limits or critical time periods capacity control policies are not optimal. We provide analysis and insights justifying the non-monotone behavior in our cruise ship context. In the absence of monotonicity, and with the optimal solution requiring heavy storage for “large” (industry-size) problems, we develop several heuristics and thoroughly test their performance, via simulation, against the optimal solution, well-crafted upper bounds, and a first-come first-served lower bound. Our heuristics are based on rolling-up the multi-dimensional state space into one or two dimensions and solving the resulting dynamic program (DP). This is a strength of our approach since our DP-based heuristics are easy to understand, solve and analyze. We find that single-dimensional heuristics based on decoupling the cabins and lifeboat problems perform quite well in most cases.     

Dynamic pricing with two revenue streams

We study casino revenue management through the pricing of hotel rooms in the presence of gaming revenue, which is random. We identify a stochastic order based on customers’ gaming profiles, from which a monotonic inventory price of rooms is obtained. We develop a threshold-type pricing policy for a special customer segmentation scheme that allows customers’ winning profiles to be ranked in terms of the failure rate order. Our results shed new light on customer valuation and market segmentation.     

Probabilistic programming models for traffic incident management operations planning

This paper proposes mathematical programming models with probabilistic constraints in order to address incident response and resource allocation problems for the planning of traffic incident management operations. For the incident response planning, we use the concept of quality of service during a potential incident to give the decision-maker the flexibility to determine the optimal policy in response to various possible situations. An integer programming model with probabilistic constraints is also proposed to address the incident response problem with stochastic resource requirements at the sites of incidents. For the resource allocation planning, we introduce a mathematical model to determine the number of service vehicles allocated to each depot to meet the resource requirements of the incidents by taking into account the stochastic nature of the resource requirement and incident occurrence probabilities. A detailed case study for the incident resource allocation problem is included to demonstrate the use of proposed model in a real-world context. The paper concludes with a summary of results and recommendations for future research.