Newsvendor problems with sequentially revealed demand information

This article analyzes a capacity/inventory planning problem with a one‐time uncertain demand. There is a long procurement leadtime, but as some partial demand information is revealed, the firm is allowed to cancel some of the original capacity reservation at a certain fee or sell off some inventory at a lower price. The problem can be viewed as a generalization of the classic newsvendor problem and can be found in many applications. One key observation of the analysis is that the dynamic programming formulation of the problem is closely related to a recursion that arises in the study of a far more complex system, a series inventory system with stochastic demand over an infinite horizon. Using this equivalence, we characterize the optimal policy and assess the value of the additional demand information. We also extend the analysis to a richer model of information. Here, demand is driven by an underlying Markov process, representing economic conditions, weather, market competition, and other environmental factors. Interestingly, under this more general model, the connection to the series inventory system is different. © 2012 Wiley Periodicals, Inc. Naval Research Logistics 2012     

Models for production planning under power interruptions

We present a robust optimization model for production planning under the assumption that electricity supply is subject to uncertain interruptions caused by participation in interruptible load contracts (ILCs). The objective is to minimize the cost of electricity used for production while providing a robust production plan which ensures demand satisfaction under all possible interruption scenarios. The combinatorial size of the set of interruption scenarios makes this a challenging problem. Furthermore, we assume that no probabilistic information is known about the supply uncertainty: we only use the information given in the ILC to identify an uncertainty set that captures the possible scenarios. We construct a general robust framework to handle this uncertainty and present a heuristic to compute a good feasible solution of the robust model. We provide computational experiments on a real‐world example and compare the performance of an exact solver applied to the robust model with that of the heuristic procedure. Finally, we include the operational impact of interruptions such as “recovery modes” in the definition of the uncertainty set. © 2013 Wiley Periodicals, Inc. Naval Research Logistics, 2013     

Optimal production ramp-up in the smartphone manufacturing industry

Motivated by challenges in the smartphone manufacturing industry, we develop a dynamic production ramp-up model that can be applied to economically satisfy nonstationary demand for short-life-cycle products by high-tech companies. Due to shorter life cycles and more rapid evolution of smartphones, production ramp-up has been increasingly critical to the success of a new smartphone. In the production ramp-up, the key challenge is to match the increasing capacity to nonstationary demand. The high-tech smartphone manufacturers are urged to jointly consider the effect of increasing capacity and decreasing demand. We study the production planning problem using a high-dimensional Markov decision process (MDP) model to characterize the production ramp-up. To address the curse of dimensionality, we refine Monte Carlo tree search (MCTS) algorithm and theoretically analyze its convergence and computational complexity. In a real case study, we find that the MDP model achieves revenue improvement by stopping producing the existing product earlier than the benchmark policy. In synthetic instances, we validate that the proposed MCTS algorithm saves computation time without loss of solution quality compared with traditional value iteration algorithm. As part of the Lenovo production solution, our MDP model enables high-tech smartphone manufacturers to better plan the production ramp-up.     

The effect of process adjustment error on X̄ chart design

The existing literature on economic design of X̄ process control charts generally assumes perfect process adjustment, such that the process mean is returned to an exactly centered “in control” state following any real or false alarm control chart signal. This paper presents a model which demonstrates the effects of imperfect process adjustment on the economically designed control chart parameters. The model demonstrates that the optimal control limit width depends fundamentally on the precision with which the process can be adjusted. The greater the process adjustment error, all else constant, the wider will be the optimal control limits, in order to alleviate the potential for process overcontrol and tampering effects. By endogenously modeling these effects, the new model helps to rectify the problem of poor statistical properties for which the economic design approach has been criticized. © 1999 John Wiley & Sons, Inc. Naval Research Logistics 46: 597–612, 1999     

An analysis of single item inventory systems with returns

Inventory systems with returns are systems in which there are units returned in a repairable state, as well as demands for units in a serviceable state, where the return and demand processes are independent. We begin by examining the control of a single item at a single location in which the stationary return rate is less than the stationary demand rate. This necessitates an occasional procurement of units from an outside source. We present a cost model of this system, which we assume is managed under a continuous review procurement policy, and develop a solution method for finding the policy parameter values. The key to the analysis is the use of a normally distributed random variable to approximate the steady-state distribution of net inventory. Next, we study a single item, two echelon system in which a warehouse (the upper echelon) supports N(N ? 1) retailers (the lower echelon). In this case, customers return units in a repairable state as well as demand units in a serviceable state at the retailer level only. We assume the constant system return rate is less than the constant system demand rate so that a procurement is required at certain times from an outside supplier. We develop a cost model of this two echelon system assuming that each location follows a continuous review procurement policy. We also present an algorithm for finding the policy parameter values at each location that is based on the method used to solve the single location problem.     

The cost impact of using simple forecasting techniques in a supply chain

In this paper we consider an inventory model in which the retailer does not know the exact distribution of demand and thus must use some observed demand data to forecast demand. We present an extension of the basic newsvendor model that allows us to quantify the value of the observed demand data and the impact of suboptimal forecasting on the expected costs at the retailer. We demonstrate the approach through an example in which the retailer employs a commonly used forecasting technique, exponential smoothing. The model is also used to quantify the value of information and information sharing for a decoupled supply chain in which both the retailer and the manufacturer must forecast demand. © 2003 Wiley Periodicals, Inc. Naval Research Logistics 50: 388–411, 2003     

A robust policy for serial agile production systems

One of the major problems in modeling production systems is how to treat the job arrival process. Restrictive assumptions such as Markovian arrivals do not represent real world systems, especially if the arrival process is generated by job departures from upstream workstations. Under these circumstances, cost‐effective policies that are robust with respect to the nature of the arrival process become of interest. In this paper, we focus on minimizing the expected total holding and setup costs in a two‐stage produce‐to‐order production system operated by a cross‐trained worker. We will show that if setup times are insignificant in comparison with processing times, then near‐optimal policies can be generated with very robust performances with respect to the arrival process. We also present conditions under which these near‐optimal policies can be obtained by using only the arrival and service rates. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

泵喷推进型潜艇电场防护效果与阴极保护性能分析

为探究泵喷推进型潜艇在电场防护和外加电流阴极保护这两种典型工作形态下的防护效果,采用边界元法建立了此型潜艇的腐蚀相关静电场模型,仿真分析了导流罩和导叶对电场防护与阴极保护效能的影响。结果表明:相较于传统推进方式,泵喷推进型潜艇在海水中电场峰峰值降低了约38%,且其仅需约50%的电流值便能达到更优的电场防护效果。此外,不论阴极保护过程中整艇涂层是否完好,泵喷推进装置处的电场峰值均有一定程度的降低,这表明泵喷推进装置具有更好的电场隐身性能;然而在对泵喷推进型潜艇外加电流阴极保护的过程中,只能在船身和大轴处达到较好的保护效果,其导流罩内部的叶轮和导叶难以达到保护电位,处于欠保护状态,局部腐蚀防护仍需加强。     

Robust capacity planning in semiconductor manufacturing

We present a stochastic programming approach to capacity planning under demand uncertainty in semiconductor manufacturing. Given multiple demand scenarios together with associated probabilities, our aim is to identify a set of tools that is a good compromise for all these scenarios. More precisely, we formulate a mixed‐integer program in which expected value of the unmet demand is minimized subject to capacity and budget constraints. This is a difficult two‐stage stochastic mixed‐integer program which cannot be solved to optimality in a reasonable amount of time. We instead propose a heuristic that can produce near‐optimal solutions. Our heuristic strengthens the linear programming relaxation of the formulation with cutting planes and performs limited enumeration. Analyses of the results in some real‐life situations are also presented. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

Country survey V: Poland and its arms industry

This study — which is a country survey — deals with basic problems of the functioning of the Polish arms industry in the conditions of systemic transformation, that has taken place in Poland since 1989. It also reflects new political and military conditions and changes in the economic system, commonly called marketization. The study is focused on reasons for the bad economic state of the enterprises engaged in military production, which include a drastic drop in demand on home and foreign markets. The study discusses the main direction of activities that are planned to reshape and adjust military production to the new, changed conditions covering such problems as ownership and organization changes as well as prospects for conversion.     

Capacity expansion under a service‐level constraint for uncertain demand with lead times

For a service provider facing stochastic demand growth, expansion lead times and economies of scale complicate the expansion timing and sizing decisions. We formulate a model to minimize the infinite horizon expected discounted expansion cost under a service‐level constraint. The service level is defined as the proportion of demand over an expansion cycle that is satisfied by available capacity. For demand that follows a geometric Brownian motion process, we impose a stationary policy under which expansions are triggered by a fixed ratio of demand to the capacity position, i.e., the capacity that will be available when any current expansion project is completed, and each expansion increases capacity by the same proportion. The risk of capacity shortage during a cycle is estimated analytically using the value of an up‐and‐out partial barrier call option. A cutting plane procedure identifies the optimal values of the two expansion policy parameters simultaneously. Numerical instances illustrate that if demand grows slowly with low volatility and the expansion lead times are short, then it is optimal to delay the start of expansion beyond when demand exceeds the capacity position. Delays in initiating expansions are coupled with larger expansion sizes. © 2009 Wiley Periodicals, Inc. Naval Research Logistics, 2009     

Algorithm to solve a chance‐constrained network capacity design problem with stochastic demands and finite support

We consider the problem of determining the capacity to assign to each arc in a given network, subject to uncertainty in the supply and/or demand of each node. This design problem underlies many real‐world applications, such as the design of power transmission and telecommunications networks. We first consider the case where a set of supply/demand scenarios are provided, and we must determine the minimum‐cost set of arc capacities such that a feasible flow exists for each scenario. We briefly review existing theoretical approaches to solving this problem and explore implementation strategies to reduce run times. With this as a foundation, our primary focus is on a chance‐constrained version of the problem in which α% of the scenarios must be feasible under the chosen capacity, where α is a user‐defined parameter and the specific scenarios to be satisfied are not predetermined. We describe an algorithm which utilizes a separation routine for identifying violated cut‐sets which can solve the problem to optimality, and we present computational results. We also present a novel greedy algorithm, our primary contribution, which can be used to solve for a high quality heuristic solution. We present computational analysis to evaluate the performance of our proposed approaches. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 236–246, 2016     

Assessing the value of demand sharing in supply chains

We consider the problem of assessing the value of demand sharing in a multistage supply chain in which the retailer observes stationary autoregressive moving average demand with Gaussian white noise (shocks). Similar to previous research, we assume each supply chain player constructs its best linear forecast of the leadtime demand and uses it to determine the order quantity via a periodic review myopic order‐up‐to policy. We demonstrate how a typical supply chain player can determine the extent of its available information in the presence of demand sharing by studying the properties of the moving average polynomials of adjacent supply chain players. The retailer's demand is driven by the random shocks appearing in the autoregressive moving average representation for its demand. Under the assumptions we will make in this article, to the retailer, knowing the shock information is equivalent to knowing the demand process (assuming that the model parameters are also known). Thus (in the event of sharing) the retailer's demand sequence and shock sequence would contain the same information to the retailer's supplier. We will show that, once we consider the dynamics of demand propagation further up the chain, it may be that a player's demand and shock sequences will contain different levels of information for an upstream player. Hence, we study how a player can determine its available information under demand sharing, and use this information to forecast leadtime demand. We characterize the value of demand sharing for a typical supply chain player. Furthermore, we show conditions under which (i) it is equivalent to no sharing, (ii) it is equivalent to full information shock sharing, and (iii) it is intermediate in value to the two previously described arrangements. Although it follows from existing literature that demand sharing is equivalent to full information shock sharing between a retailer and supplier, we demonstrate and characterize when this result does not generalize to upstream supply chain players. We then show that demand propagates through a supply chain where any player may share nothing, its demand, or its full information shocks (FIS) with an adjacent upstream player as quasi‐ARMA in—quasi‐ARMA out. We also provide a convenient form for the propagation of demand in a supply chain that will lend itself to future research applications. © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 515–531, 2014     

A risk function for the stochastic modeling of electric capacity expansion

We present a stochastic optimization model for planning capacity expansion under capacity deterioration and demand uncertainty. The paper focuses on the electric sector, although the methodology can be used in other applications. The goals of the model are deciding which energy types must be installed, and when. Another goal is providing an initial generation plan for short periods of the planning horizon that might be adequately modified in real time assuming penalties in the operation cost. Uncertainty is modeled under the assumption that the demand is a random vector. The cost of the risk associated with decisions that may need some tuning in the future is included in the objective function. The proposed scheme to solve the nonlinear stochastic optimization model is Generalized Benders' decomposition. We also exploit the Benders' subproblem structure to solve it efficiently. Computational results for moderate‐size problems are presented along with comparison to a general‐purpose nonlinear optimization package. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48:662–683, 2001     

Using the Feder-Ram and Military Keynesian Models to Examine the Link Between Defence Spending and Economic Growth in Sri Lanka

This study uses the Feder-Ram model in conjunction with the military Keynesian model to examine the nexus between defence spending and economic growth in Sri Lanka. We find that the Keynesian aggregate demand model is better suited to analyse the link than the Feder-Ram model for the case of Sri Lanka. Based upon our results we expect a higher economic growth rate in Sri Lanka if more public resources are diverted from the defence to civilian sectors of the economy, now that the war between the government and separatist guerrillas has come to an end. However, recent post war events cast doubt upon whether a diversion of sources from military to non-military spending will actually occur. We conclude that the sanguine predictions of our economic analysis are entirely dependent upon the political decisions of the Sri Lankan government for their realization.     

Selling to strategic and loss‐averse consumers: Stocking,procurement, and product design policies

Motivated by the presence of loss‐averse decision making behavior in practice, this article considers a supply chain consisting of a firm and strategic consumers who possess an S‐shaped loss‐averse utility function. In the model, consumers decide the purchase timing and the firm chooses the inventory level. We find that the loss‐averse consumers' strategic purchasing behavior is determined by their perceived gain and loss from strategic purchase delay, and the given rationing risk. Thus, the firm that is cognizant of this property tailors its inventory stocking policy based on the consumers' loss‐averse behavior such as their perceived values of gain and loss, and their sensitivity to them. We also demonstrate that the firm's equilibrium inventory stocking policy reflects both the economic logic of the traditional newsvendor inventory model, and the loss‐averse behavior of consumers. The equilibrium order quantity is significantly different from those derived from models that assume that the consumers are risk neutral and homogeneous in their valuations. We show that the firm that ignores strategic consumer's loss‐aversion behavior tends to keep an unnecessarily high inventory level that leads to excessive leftovers. Our numerical experiments further reveal that in some extreme cases the firm that ignores strategic consumer's loss‐aversion behavior generates almost 92% more leftovers than the firm that possesses consumers’ loss‐aversion information and takes it into account when making managerial decisions. To mitigate the consumer's forward‐looking behavior, we propose the adoption of the practice of agile supply chain management, which possesses the following attributes: (i) procuring inventory after observing real‐time demand information, (ii) enhanced design (which maintains the current production mix but improves the product performance to a higher level), and (iii) customized design (which maintains the current performance level but increases the variety of the current production line to meet consumers’ specific demands). We show that such a practice can induce the consumer to make early purchases by increasing their rationing risk, increasing the product value, or diversifying the product line. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 435–453, 2015     

随机弱实时系统作业调度概率保证分析

随机弱实时系统与强实时系统的显著区别是 ,它不要求实时任务的每一项作业都满足时限要求 ,只要保证作业的时限延误率低于某一阈值 ,其总体执行性能就是可以接受的。提出了概率时间需求分析法 (PTDA) ,用于估计可剥夺静态优先级调度策略下周期性任务的作业满足时限约束的概率的下界 ,并通过一个具体的实例考察了下界的紧性。对实例系统的仿真结果表明 ,PTDA分析的误差小于 1 0 % ,计算速度快 ,可以为弱实时应用的设计提供重要的参考依据     

Impact of tax reduction policies on consumer purchase of new automobiles: An analytical investigation with real data‐based experiments

We investigate and compare the impact of the tax reduction policies implemented in the United States and China to stimulate consumer purchase of new automobiles and improve manufacturers' profits. The U.S. policy provides each qualifying consumer with a federal income tax deduction on state and local sales and excise taxes paid on the purchase price (up to a cutoff level), whereas the Chinese policy reduces the vehicle sales tax rate for consumers. We observe that these policy designs are consistent with the tax management system and the economic environment in the respective country. We analytically determine the effects of the two tax reduction policies on the automobile sales and the manufacturer's and the retailer's profits. Numerical examples are then used to provide insights on the importance of certain factors that influence the effects of the two policies. Finally, a numerical experiment with sensitivity analysis based on real data is conducted to compare the merits and characteristics of the two policies under comparable conditions. We find that the U.S. policy is better than the Chinese policy in stimulating the sales of high‐end automobiles, whereas the Chinese policy is better than the U.S. policy in improving the sales of low‐end automobiles. The U.S. policy is slightly more effective in increasing the profitability of the automobile supply chain; but, in general, the Chinese policy is more cost effective. The methodology developed herein can be used to evaluate other tax reduction policies such as those related to the purchase of energy‐saving vehicles and to serve as a decision model to guide the choice of alternative tax reduction policies. © 2014 Wiley Periodicals, Inc. Naval Research Logistics, 61: 577–598, 2014     

Predicting intensive care unit bed occupancy for integrated operating room scheduling via neural networks

In a master surgery scheduling (MSS) problem, a hospital's operating room (OR) capacity is assigned to different medical specialties. This task is critical since the risk of assigning too much or too little OR time to a specialty is associated with overtime or deficit hours of the staff, deferral or delay of surgeries, and unsatisfied—or even endangered—patients. Most MSS approaches in the literature focus only on the OR while neglecting the impact on downstream units or reflect a simplified version of the real‐world situation. We present the first prediction model for the integrated OR scheduling problem based on machine learning. Our three‐step approach focuses on the intensive care unit (ICU) and reflects elective and urgent patients, inpatients and outpatients, and all possible paths through the hospital. We provide an empirical evaluation of our method with surgery data for Universitätsklinikum Augsburg, a German tertiary care hospital with 1700 beds. We show that our model outperforms a state‐of‐the‐art model by 43% in number of predicted beds. Our model can be used as supporting tool for hospital managers or incorporated in an optimization model. Eventually, we provide guidance to support hospital managers in scheduling surgeries more efficiently.     

Markets for surplus components with a strategic supplier

We study markets for surplus components, which allow manufacturers with excess component inventory to sell to firms with a shortage. Recent developments in internet commerce have the potential to greatly increase the efficiency of such markets. We develop a one‐period model in which a monopolist supplier sells to a number of independent manufacturers who are uncertain about demand for final goods. After uncertainty is resolved, the manufacturers have the opportunity to trade. Because uncertainty is over demand functions, the model allows us to endogenize both the price of final goods and the price of components in wholesale and surplus markets. We derive conditions on demand uncertainty that determine whether a surplus market will increase or decrease supplier profits. Increased costs of transacting on the surplus market may benefit manufacturers, because of the impact of these costs on the supplier's pricing power. The surplus market can decrease overall efficiency of the supply chain, since the benefit of better allocation of components may be outweighed by an increased double‐marginalization effect. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.