Deterministic models for premature and postponed replacement

We examine the possibilities of premature and postponed replacement in a deterministic infinite horizon model when there is technological progress. Both revenue and operating cost deteriorate with age, but at different rates. The optimal deterministic replacement time is an implicit solution from the timing boundary obtained for the equivalent real option model using a dynamic programming framework, and then by setting the underlying volatilities equal to zero. A step change improvement characterizing technological progress in the initial operating cost level for the successor occurring during the economic lifetime of the incumbent justifies premature replacement, compared to the traditional present value approach. This finding can be extended to step change improvements in the initial revenue level for the successor and for the re-investment cost. In contrast, if the technological progress can be characterized by a constant declining rate for the initial operating cost level for the successor, then the replacement is postponed for certain parameter values. This finding can be extended to different assumed improvement rates in the initial revenue level for the successor and for the re-investment cost.     

通胀对高管的股权激励和工作努力策略的影响

股权激励能够使得公司与高管的共同利益最大化,并且解决由于信息不对称所产生的委托代理问题。高管不可观测的工作控制着公司股票价格。由于高管的真实财富受到市场通胀不确定性的影响,本文研究了通胀环境下,以风险厌恶和工作努力效率为特质的高管,在银行无风险资产账户、市场投资组合和公司股票间寻求最优配置以达到个人终端真实财富预期效用最大化。首先利用伊藤公式得到了高管的真实财富过程;其次利用动态规划原理构建了满足最优工作努力策略和股权激励问题的哈密尔顿-雅可比-贝尔曼（HJB）方程,并找到了解析解;最后对结果进行了数值模拟,并从经济学的角度分析了通胀风险对高管的股权激励和工作努力策略的影响。本文研究表明：由于通胀不确定性影响着高管财富的实际价值,当高管财富效用服从对数函数时,公司股票占高管个人财富的最优比例不受通胀风险影响,但是高管在市场投资组合的投资比例随通胀波动率的增加而下降,进而影响到高管对市场投资组合账户和银行无风险资产账户的投资比例。高管的财富效用服从幂函数时,对于长期契约,股权激励代表的长远共同利益引导着高管看好公司的发展前景,激励着高管付出更高水平的努力,此时通胀带来的货币贬值风险对高管的的影响较小;而对于短期契约,长远共同利益的缺乏和通胀风险的变大诱导着高管的短视行为,高管将减持公司股票并且减少努力。本文所得结论对公司的股权激励理论做了有益的补充。     

A hierarchical production plan for a make-to-order steel fabrication plant

A three-tiered hierarchical production plan (HPP) for a strictly make-to-order steel fabrication plant with the objective of developing a production plan and master schedule for a set of product archetypes is implemented. Data are collected from an actual steel fabrication plant located in the Midwestern section of the US. An aggregate linear programming model, a non-linear disaggregate model and a master production schedule comprise the respective tiers. Appropriate models provide the forecasts needed in the first two tiers. A production plan and master schedule based on data collected at the plant, benefits expected for its implementation and practical limitations are reported.     

A Choice‐Based Dynamic Programming Approach for Setting Opaque Prices

Opaque pricing is a form of pricing where certain characteristics of the product or service are hidden from the consumer until after purchase. In essence, opaque selling transforms a differentiated good into a commodity. Opaque pricing has become popular in service pricing as it allows firms to sell their differentiated product at higher prices to regular brand loyal customers while simultaneously selling to non‐brand loyal customers at discounted prices. We use a nested logit model in combination with logistic regression and dynamic programming to illustrate how a service firm can optimally set prices on an opaque sales channel. The choice model allows the characterization of consumer trade‐offs when purchasing opaque products while the dynamic programming approach allows the characterization of the optimal pricing policy as a function of inventory and time remaining. We compare optimal prices and expected revenues when dynamic pricing is restricted to daily price changes. We provide an illustrative example using data from an opaque selling mechanism ( Hotwire.com ) and a Washington DC‐based hotel.     

A Universal Appointment Rule in the Presence of No‐Shows and Walk‐Ins

This study introduces a universal “Dome” appointment rule that can be parameterized through a planning constant for different clinics characterized by the environmental factors—no‐shows, walk‐ins, number of appointments per session, variability of service times, and cost of doctor's time to patients’ time. Simulation and nonlinear regression are used to derive an equation to predict the planning constant as a function of the environmental factors. We also introduce an adjustment procedure for appointment systems to explicitly minimize the disruptive effects of no‐shows and walk‐ins. The procedure adjusts the mean and standard deviation of service times based on the expected probabilities of no‐shows and walk‐ins for a given target number of patients to be served, and it is thus relevant for any appointment rule that uses the mean and standard deviation of service times to construct an appointment schedule. The results show that our Dome rule with the adjustment procedure performs better than the traditional rules in the literature, with a lower total system cost calculated as a weighted sum of patients’ waiting time, doctor's idle time, and doctor's overtime. An open‐source decision‐support tool is also provided so that healthcare managers can easily develop appointment schedules for their clinical environment.     

Using branch-and-price approach to solve the directed network design problem with relays

We present node-arc and arc-path formulations, and develop a branch-and-price approach for the directed network design problem with relays (DNDR). The DNDR problem can be used to model many network design problems in transportation, service, and telecommunication system, where relay points are necessary. The DNDR problem consists of introducing a subset of arcs and locating relays on a subset of nodes such that in the resulting network, the total cost (arc cost plus relay cost) is minimized, and there exists a directed path linking the origin and destination of each commodity, in which the distances between the origin and the first relay, any two consecutive relays, and the last relay and the destination do not exceed a predefined distance limit. With the node-arc formulation, we can directly solve small DNDR instances using mixed integer programming solver. With the arc-path formulation, we design a branch-and-price approach, which is a variant of branch-and-bound with bounds provided by solving linear programs using column generation at each node of the branch-and-bound tree. We design two methods to efficiently price out columns and present computational results on a set of 290 generated instances. Results demonstrate that our proposed branch-and-price approach is a computationally efficient procedure for solving the DNDR problem.     

Simultaneous production and logistics operations planning in semicontinuous food industries

The production and logistics operations planning in real-life single- or multi-site semicontinuous food industries is addressed in this work. A discrete/continuous-time mixed integer programming model, based on the definition of families of products, is developed for the problem in question. A remarkable feature of the proposed approach is that in the production planning problem timing and sequencing decisions are taken for product families rather than for products. However, material balances are realized for every specific product, thus permitting the detailed optimization of production, inventory, and transportation costs. Changeovers are also explicitly taken into account and optimized. Moreover, alternative transportation modes are considered for the delivery of final products from production sites to distribution centers. The efficiency and the applicability of the proposed approach is demonstrated by solving to optimality two industrial-size case studies, for an emerging real-life Greek dairy industry.     

我国商业银行经营体制转型的策略

当今世界金融业的混业经营和统一监管已成为国际潮流,我国商业银行的出路只有一条,即积极创造条件,向金融混业经营转型。我国商业银行应在现实给定的资源和制度条件的前提下,选择最优的混业经营模式,并逐步为从分业经营制度向混业经营制度的转型创造客观条件。     

机械臂非线性定位方程计算新方法

针对机械臂定位算法中,数值算法计算量大,仿真过程存在累积误差,几何算法通用性不强等问题,以手术机器 人六自由度机械臂为研究对象,建立了一组多关节机械臂非线性定位方程组,提出了其基于非线性大范围渐近稳定的 求解方法。结果表明该方法既简化运算,又有利于物理实现,为机械臂高速、准确运动提供了基础,实验表明该算法适用 多关节的移动机械臂的非线性定位方程的求解,有较高的求解精度和收敛速度。     

基于多目标二层规划的服务供应链服务能力协同决策模型

为了解决服务供应链系统运作过程中存在的服务能力建设过量或不足问题,本文构建了以服务集成商作为服务供应链整体计划、控制和协调中心的上层计划者、各服务供应商作为具有相对自主权的下层计划者的服务供应链协同决策机制,应用多目标二层规划方法建立了服务供应链服务能力分配的优化模型,同时给出了模型的具体算法框图和流程。最后,通过实证算例对服务能力协同决策优化模型进行了计算和有效性验证。