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。     

考虑返回补偿策略的制造与再制造的最优决策

作为减少成本的一种有效方式,近年来,再制造获得了企业越来越多的关注.对于再制造企业,如何有效地返回产品是一个基本的问题,为此,考虑了一个返回补偿策略,即企业支付给愿意返回产品的消费者一个价格补偿.在这个策略下,回收数量是随机需求的一个比例.研究了一个两周期的库存系统,企业需要在每周期初决策新材料的采购数量以及分配给制造和再制造方式的生产数量.通过建立一个三级随机动态规划模型,给出了制造和再制造混合系统对于已实现需求的最优生产策略,同时证明了每个周期的目标函数对于库存补充数量是凸的,进而证明基本的库存策略仍然是最优的.最后从管理者的角度进行了数值分析.     

考虑仓库容量扩张的多周期随机存贮模型——折扣准则

研究了仓库容量可以控制的、基于折扣准则的多周期随机存贮模型.利用马氏决策过程(MDP)的方法,建立了最小折现成本所满足的最优方程,在此基础上,得到了一个(Ut*,yt*(b))结构的最优策略:当仓库容量小于Ut*时将容量扩充到Ut*,并订货至Ut*;否则保持仓库容量不变,且当存贮量小于yt*(b)时订货到yt*(b),反之不订货.     

Heston随机波动率模型下的资产负债管理问题

应用随机最优控制方法研究Heston随机波动率模型下带有负债过程的动态投资组合问题,其中假设股票价格服从Heston随机波动率模型,负债过程由带漂移的布朗运动所驱动.金融市场由一种无风险资产和一种风险资产组成.应用随机动态规划原理和变量替换法得出了上述问题在幂效用和指数效用函数下最优投资策略的显示解,并给出数值算例分别分析了市场参数在幂效用和指数效用函数下对最优投资策略的影响.     

技术不确定环境下新产品策略的设计应用

技术的动态发展和企业间的竞争对企业新产品策略有很大影响,直接决定新产品的引进周期。本文在产业技术动态变化的随机环境下构建随机动态规划模型,关注产业技术进步、投资成本和产品市场竞争等影响因素,探讨企业进行新产品引进的周期选择,对新产品引进的周期和质量决策进行方法设计和应用举例。利用随机动态规划模型得出新产品引进的最优时间周期,用算例分析技术进步和产品研发成本对企业引进周期策略的影响,采取策略迭代的方法进行求解,发现技术进步较快时企业的新产品引进步伐也较快,研发成本的提高使企业的新产品引入步伐降低。     

随机利率环境下基于二次效用函数的动态投资组合(英文)

研究随机利率环境下基于效用最大化的动态投资组合,并假设利率是服从Ho-Lee利率模型和Vasicek利率模型的随机过程.应用动态规划原理得到值函数满足的HJB方程,并应用Legendre变换得到其对偶方程.最后,应用变量替换对二次效用函数下的最优投资策略进行研究,得到了最优投资策略的显示解.     

VaR约束下最优比例再保险和投资策略问题

研究了VaR动态约束下保险人的最优投资和再保险策略选择问题.假设保险人选择比例再保险来分散索赔风险,并通过银行存款和投资股票的手段来增加额外收益,其中股票价格满足Heston模型.保险人的目标是寻求使其终端财富的期望效用最大的最优策略.引入VaR约束条件并采用期望效用最大化为准则,运用随机控制理论建立具有VaR约束的随机控制问题,采用动态规划推导HJB方程,并利用Lagrange函数等方法得到指数效用下VaR约束有效和无效时的最优策略.另外,考虑了仅投资情形下的最优投资策略.最后通过仿真对最优策略进行敏感性分析.     

基于多数量需求拍卖机制的动态存贮/分配模型

该文分析了折扣准则下基于多数量需求拍卖机制的多阶段存贮问题,运用动态规划方法,在有限阶段对该问题研究了每个时期应该出售的最优产品数量、最优分配方案和最优订购策略,提出了运用修正的多需求二级价格拍卖模型来实现最优分配,并对无限阶段下的动态存贮/分配问题进行了讨论.     

基于时变需求的集成多级供应链生产订货策略研究

考虑一个时变需求环境下集成多级供应链问题,在有限的规划时间内销售商以固定周期订货,而生产商以不同的周期生产,目的是寻找销售商最优的订货周期和生产商最佳的生产策略,从而使供应链系统的总运营成本最少.建立了该问题的混合整数非线性规划模型,求解该模型分为两步：先求对应一个订货周期的最佳生产策略,再求最优的订货周期,第一步用到了图论里求最短路方法.给出了两个步骤的算法和程序,实验证明它们是有效的.通过算例对模型进行了分析,研究了各参数对最优解及最小费用的影响.     

开环策略下多阶段均值-方差投资组合优化研究

首先研究开环策略下不同财富动态过程的多阶段均值-方差投资组合优化模型,讨论它们的实际意义和计算方法,其中投资比例财富动态过程模型为高度非线性非凸数学规划.进一步研究投资比例财富动态过程模型实际计算问题,并且通过构造辅助模型,给出投资比例两阶段模型的全局解求解方法并通过数值算例和仿真说明该方法的有效性和准确性.最后通过数值算例比较不同财富动态过程在开环策略下和闭环策略下前沿面的关系,结果表明在闭环策略下三种财富过程等价,但是在开环策略下资产财富模型的前沿面最高、资产调整模型的前沿面次之、投资比例多阶段模型的前沿面最低.     

A non-stationary periodic review production–inventory model with uncertain production capacity and uncertain demand

In this paper we consider a nonstationary periodic review dynamic production–inventory model with uncertain production capacity and uncertain demand. The maximum production capacity varies stochastically. It is known that order up-to (or base-stock, critical number) policies are optimal for both finite horizon problems and infinite horizon problems. We obtain upper and lower bounds of the optimal order up-to levels, and show that for an infinite horizon problem the upper and the lower bounds of the optimal order up-to levels for the finite horizon counterparts converge as the planning horizons considered get longer. Furthermore, under mild conditions the differences between the upper and the lower bounds converge exponentially to zero.     

On the single item fill rate for a finite horizon

We investigate expressions for expected item fill rate in a periodic inventory system. The typical treatment of fill rate found in many operations management texts assumes infinite horizon, independent and stationary demand. For the case when the horizon is finite, we show that the expected value of the actual fill rate is greater than the value given by the infinite horizon expression. The implication of our results is that an inventory manager in a finite horizon situation who uses the infinite horizon expression to set stocking levels will achieve a higher than desired expected fill rate at greater than necessary inventory expense.     

Selective and periodic inventory routing problem for waste vegetable oil collection

We consider a biodiesel production company that collects waste vegetable oil from source points that generate waste in large amounts. The company uses the collected waste as raw material for biodiesel production. The manager of this company needs to decide which of the present source points to include in the collection program, which of them to visit on each day, which periodic routing schedule to repeat over an infinite horizon and how many vehicles to operate such that the total collection, inventory and purchasing costs are minimized while the production requirements and operational constraints are met. For this selective and periodic inventory routing problem, we propose two different formulations, compare them and apply the better performing one on a real-world problem with 36 scenarios. We generate lower bounds using a partial linear relaxation model, and observe that the solutions obtained through our model are within 3.28% of optimality on the average. Several insights regarding the customer selection, routing and purchasing decisions are acquired with sensitivity analysis.     

Limit of the solutions for the finite horizon problems as the optimal solution to the infinite horizon optimization problems

In this paper, we consider how to construct the optimal solutions for the undiscounted discrete time infinite horizon optimization problems. We present the conditions under which the limit of the solutions for the finite horizon problems is optimal among all attainable paths for the infinite horizon problem under two modified overtaking criteria, as well as the conditions under which it is the unique optimum under the sum-of-utilities criterion. The results are applied to a parametric example of a simple one-sector growth model to examine the impacts of discounting on the optimal path.     

A comparison between the robust risk-aware and risk-seeking managers in R&D portfolio management

In this paper, we analyze two mathematical modeling frameworks that reflect different managerial attitudes toward upside risk in the context of R&D portfolio selection. The manager seeks to allocate a development budget between low-risk, low-reward projects, called incremental projects, and high-risk, high-reward projects, called innovational projects. Because of their highly uncertain nature and significant probability of failure, the expected value of the innovational projects is smaller than that of their incremental projects’ counterpart, but the long-term financial health of a company necessitates to take risk in order to maintain growth. We study the differences in strategy and portfolio’s risk profile that arise between a risk-aware manager, who takes upside risk because he has to for the long-term competitive advantage of his company, and a risk-seeking manager, who will take as big a bet as allowed by the model. To the best of our knowledge, this is the first paper to consider upside risk management using a robust-optimization-like methodology.     

Optimal dynamic pricing of inventories with stochastic demand and discounted criterion

We consider a continuous time dynamic pricing problem for selling a given number of items over a finite or infinite time horizon. The demand is price sensitive and follows a non-homogeneous Poisson process. We formulate this problem as to maximize the expected discounted revenue and obtain the structural properties of the optimal revenue function and optimal price policy by the Hamilton-Jacobi-Bellman (HJB) equation. Moreover, we study the impact of the discount rate on the optimal revenue function and the optimal price. Further, we extend the problem to the case with discounting and time-varying demand, the infinite time horizon problem. Numerical examples are used to illustrate our analytical results.     

Optimal dividend-financing strategies in a dual risk model with time-inconsistent preferences

In this paper, we consider an optimal dividend-financing problem for a company whose capital reserve is described by the dual of classical risk model. We assume that the manager of the company has time-inconsistent preferences, which are described by a quasi-hyperbolic discount function, and that financing is permitted to prevent the company from going bankrupt. The manager’s objective is to maximize the expected cumulative dividend payments minus financing costs. We solve the optimization problems for a naive manager and a sophisticated manager, and obtain explicit solutions for both managers. Our results show that the manager with time-inconsistent preferences tends to pay out dividends earlier. We also present some economic implications and sensitivity analysis for our results.     

The value of dynamic pricing for cores in remanufacturing with backorders

In remanufacturing, the supply of used products and the demand for remanufactured products are usually mismatched because of the great uncertainties on both sides. In this paper, we propose a dynamic pricing (DP) policy to balance this uncertain supply and demand. Specifically, we study a remanufacturer’s problem of pricing a single class of cores with random price-dependent returns and random demand for the remanufactured products with backlogs. We model this pricing task as a continuous-time Markov decision process, which addresses both the finite and infinite horizon problems, and provide managerial insights by analysing the structural properties of the optimal policy. We then use several computational examples to illustrate the impacts of particular system parameters on pricing policy and the benefit of DP. In addition, the models are extended to account for the price adjustment costs. We show through numerical example that the nice structural properties do not exist any longer, and find when DP is better than static pricing.     

Optimal partially reversible investment with entry decision and general production function

This paper studies the problem of a company that adjusts its stochastic production capacity in reversible investments with controls of expansion and contraction. The company may also decide on the activation time of its production. The profit production function is of a very general form satisfying minimal standard assumptions. The objective of the company is to find an optimal entry and production decision to maximize its expected total net profit over an infinite time horizon. The resulting dynamic programming principle is a two-step formulation of a singular stochastic control problem and an optimal stopping problem. The analysis of value functions relies on viscosity solutions of the associated Bellman variational inequations. We first state several general properties and in particular smoothness results on the value functions. We then provide a complete solution with explicit expressions of the value functions and the optimal controls: the company activates its production once a fixed entry-threshold of the capacity is reached, and invests in capital so as to maintain its capacity in a closed bounded interval. The boundaries of these regions can be computed explicitly and their behavior is studied in terms of the parameters of the model.     

Furniture supply chain tactical planning optimization using a time decomposition approach

We study the supply chain tactical planning problem of an integrated furniture company located in the Province of Québec, Canada. The paper presents a mathematical model for tactical planning of a subset of the supply chain. The decisions concern procurement, inventory, outsourcing and demand allocation policies. The goal is to define manufacturing and logistics policies that will allow the furniture company to have a competitive level of service at minimum cost. We consider planning horizon of 1 year and the time periods are based on weeks. We assume that customer’s demand is known and dynamic over the planning horizon. Supply chain planning is formulated as a large mixed integer programming model. We developed a heuristic using a time decomposition approach in order to obtain good solutions within reasonable time limit for large size problems. Computational results of the heuristic are reported. We also present the quantitative and qualitative results of the application of the mathematical model to a real industrial case.