Equilibrium Joining Strategies and Optimal Control of a Make‐to‐Stock Queue

We consider a make‐to‐stock, finite‐capacity production system with setup cost and delay‐sensitive customers. To balance the setup and inventory related costs, the production manager adopts a two‐critical‐number control policy, where the production starts when the number of waiting customers reaches a certain level and shuts down when a certain quantity of inventory has accumulated. Once the production is set up, the unit production time follows an exponential distribution. Potential customers arrive according to a Poisson process. Customers are strategic, i.e., they make decisions on whether to stay for the product or to leave without purchase based on their utility values, which depend on the production manager's control decisions. We formulate the problem as a Stackelberg game between the production manager and the customers, where the former is the game leader. We first derive the equilibrium customer purchasing strategy and system performance. We then formulate the expected cost rate function for the production system and present a search algorithm for obtaining the optimal values of the two control variables. We further analyze the characteristics of the optimal solution numerically and compare them with the situation where the customers are non‐strategic.     

Building Supply Chain Resilience through Virtual Stockpile Pooling

Stockpiling inventory is an essential strategy for building supply chain resilience. It enables firms to continue operating while finding a solution to an unexpected event that causes a supply disruption or demand surge. While extremely valuable when actually deployed, stockpiles incur large holding costs and usually provide no benefits until such a time. To help to reduce this cost, this study presents a new approach for managing stockpiles. We show that if leveraged intelligently, stockpiles can also help an organization better meet its own regular demand by enabling a type of virtual pooling we call virtual stockpile pooling (VSP). The idea of VSP is to first integrate the stockpile into several locations’ regular inventory buffers and then dynamically reallocate the stockpile among these locations in reaction to the demand realizations to achieve a kind of virtual transshipment. To study how to execute VSP and determine when it can provide the most value, we formulate a stylized multi‐location stochastic inventory model and solve for the optimal stockpile allocation and inventory order policies. We show that VSP can provide significant cost savings: in some cases nearly the full holding cost of the stockpile (i.e., VSP effectively maintains the stockpile for free), in other cases nearly the savings of traditional physical inventory pooling. Last, our results prescribe implementing VSP with many locations for large stockpiles, but only a few locations for small stockpiles.     

Characterizing the Structure of Optimal Stopping Policies

This paper studies a stochastic model of optimal stopping processes, which arise frequently in operational problems (e.g., when a manager needs to determine an optimal epoch to stop a process). For such problems, we propose an effective method of characterizing the structure of the optimal stopping policy for the class of discrete‐time optimal stopping problems. Using this method, we also derive a set of metatheorems that can help identify when a threshold or control‐band type stopping policy is optimal. We show that our proposed method can determine the structure of the optimal policy for some stopping problems that conventional methods fail to do so. In some cases, our method also simplifies the analysis of some existing results. Moreover, the metatheorems we propose help identify sufficient conditions that yield simple optimal policies when such policies are not generally optimal. We demonstrate these benefits by applying our method to several optimal stopping problems frequently encountered in, for example, the operations, marketing, finance, and economics literatures. We note that with structural results, optimal‐stopping policies are easier to follow, describe, and compute and hence implement. They also help determine how a stopping policy should be adjusted in response to changes in the operational environment. In addition, as structural results are critical for the development of efficient algorithms to solve optimal stopping problems numerically, we hope that the method and results provided in the study will contribute to that effort.     

A WTP‐Choice Model: Empirical Validation,Competitive and Centralized Pricing

Willingness To Pay (WTP) of customers plays an anchoring role in pricing. This study proposes a new choice model based on WTP, incorporating sequential decision making, where the products with positive utility of purchase are considered in the order of customer preference. We compare WTP‐choice model with the commonly used (multinomial) Logit model with respect to the underlying choice process, information requirement, and independence of irrelevant alternatives. Using WTP‐choice model, we find and compare equilibrium and centrally optimal prices and profits without considering inventory availability. In addition, we compare equilibrium prices and profits in two contexts: without considering inventory availability and under lost sales. One of the interesting results with WTP‐choice model is the “loose coupling” of retailers in competition; prices are not coupled but profits are. That is, each retailer should charge the monopoly price as the collection of these prices constitute an equilibrium but each retailer's profit depends on other retailers' prices. Loose coupling fails with dependence of WTPs or dependence of preference on prices. Also, we show that competition among retailers facing dependent WTPs can cause price cycles under some conditions. We consider real‐life data on sales of yogurt, ketchup, candy melt, and tuna, and check if a version of WTP‐choice model (with uniform, triangle, or shifted exponential WTP distribution), standard or mixed Logit model fits better and predicts the sales better. These empirical tests establish that WTP‐choice model compares well and should be considered as a legitimate alternative to Logit models for studying pricing for products with low price and high frequency of purchase.     

Airline Safety Improvement Through Experience with Near‐Misses: A Cautionary Tale

In recent years, the U.S. commercial airline industry has achieved unprecedented levels of safety, with the statistical risk associated with U.S. commercial aviation falling to 0.003 fatalities per 100 million passengers. But decades of research on organizational learning show that success often breeds complacency and failure inspires improvement. With accidents as rare events, can the airline industry continue safety advancements? This question is complicated by the complex system in which the industry operates where chance combinations of multiple factors contribute to what are largely probabilistic (rather than deterministic) outcomes. Thus, some apparent successes are realized because of good fortune rather than good processes, and this research intends to bring attention to these events, the near‐misses. The processes that create these near‐misses could pose a threat if multiple contributing factors combine in adverse ways without the intervention of good fortune. Yet, near‐misses (if recognized as such) can, theoretically, offer a mechanism for continuing safety improvements, above and beyond learning gleaned from observable failure. We test whether or not this learning is apparent in the airline industry. Using data from 1990 to 2007, fixed effects Poisson regressions show that airlines learn from accidents (their own and others), and from one category of near‐misses—those where the possible dangers are salient. Unfortunately, airlines do not improve following near‐miss incidents when the focal event has no clear warnings of significant danger. Therefore, while airlines need to and can learn from certain near‐misses, we conclude with recommendations for improving airline learning from all near‐misses.     

Understanding Local Policy Elites’ Perceptions on the Benefits and Risks Associated with High‐Voltage Power Line Installations in the State of Arkansas

Recently, a controversial policy debate has arisen concerning the installation of high‐voltage power lines in northwest Arkansas. While proponents argue that such an installation is inevitable to efficiently and reliably support the identified electric load in the region, opponents claim that the lines will degrade the natural environment and hamper the tourism‐based local economy in affected regions, notably in Ozark Mountain areas. Of particular interest is to understand how local policy elites perceive the benefits and risks associated with such divisive proposals, which is critical for comprehending the formation and changes of related government policies. Based upon the dual process theory of judgment, this study systematically investigates the triadic relationships between (a) more profound personal value predispositions, (b) affects and feelings, and (c) perceived benefits and risks related to the proposed installation of high‐voltage power lines among local policy elites in the state of Arkansas. In doing so, we analyze original data collected from a statewide Internet survey of 420 local leaders and key policymakers about their opinions on the related issues, while considering other factors claimed by previous literature to influence risk perceptions, including trust, knowledge level, and demographic characteristics. Analytical results suggest that grid‐group cultural predispositions, as deeply held core values within local policy elites’ individual belief systems, both directly and indirectly—through affective feelings—shape perceived utility associated with the installation of high‐voltage power lines. We conclude this article by suggesting some practical considerations for better designing policy addressing controversial issues of this nature.     

Health‐Economics Analyses Applied to ELF Electric and Magnetic Fields

Extremely low frequency electric and magnetic fields (ELF EMFs) are a common exposure for modern populations. The prevailing public‐health protection paradigm is that quantitative exposure limits are based on the established acute effects, whereas the possible chronic effects are considered too uncertain for quantitative limits, but might justify precautionary measures. The choice of precautionary measures can be informed by a health‐economics analysis (HEA). We consider four such analyses of precautionary measures that have been conducted at a national or state level in California, the Netherlands, the United Kingdom, and Israel. We describe the context of each analysis, examine how they deal with some of the more significant issues that arise, and present a comparison of the input parameters and assumptions used. The four HEAs are methodologically similar. The most significant qualitative choices that have to be made are what dose‐response relationship to assume, what allowance if any to make for uncertainty, and, for a CBA only, what diseases to consider, and all four analyses made similar choices. These analyses suggest that, on the assumptions made, there are some low‐cost measures, such as rephasing, that can be applied to transmission in some circumstances and that can be justifiable in cost‐benefit terms, but that higher cost measures, such as undergrounding, become unjustifiable. Of the four HEAs, those in the United Kingdom and Israel were influential in determining the country's EMF policy. In California and Netherlands, the HEA may well have informed the debate, but the policy chosen did not stem directly from the HEA.     

Estimating the Probability of Rare Events Occurring Using a Local Model Averaging

In statistical applications, logistic regression is a popular method for analyzing binary data accompanied by explanatory variables. But when one of the two outcomes is rare, the estimation of model parameters has been shown to be severely biased and hence estimating the probability of rare events occurring based on a logistic regression model would be inaccurate. In this article, we focus on estimating the probability of rare events occurring based on logistic regression models. Instead of selecting a best model, we propose a local model averaging procedure based on a data perturbation technique applied to different information criteria to obtain different probability estimates of rare events occurring. Then an approximately unbiased estimator of Kullback‐Leibler loss is used to choose the best one among them. We design complete simulations to show the effectiveness of our approach. For illustration, a necrotizing enterocolitis (NEC) data set is analyzed.     

The Impact of Perceived Flood Exposure on Flood‐Risk Perception: The Role of Distance

Natural hazards, such as major flood events, are occurring with increasing frequency and inflicting increasing levels of financial damages upon affected communities. The experience of such major flood events has brought about a significant change in attitudes to flood‐risk management, with a shift away from built engineering solutions alone towards a more multifaceted approach. Europe's experience with damaging flood episodes provided the impetus for the introduction of the European Floods Directive, requiring the establishment of flood‐risk management plans at the river‐basin scale. The effectiveness of such plans, focusing on prevention, protection, and preparedness, is dependent on adequate flood awareness and preparedness, and this is related to perception of flood risk. This is an important factor in the design and assessment of flood‐risk management. Whilst there is a modern body of literature exploring flood perception issues, there have been few examples that explore its spatial manifestations. Previous literature has examined perceived and real distance to a hazard source (such as a river, nuclear facility, landfill, or incinerator, etc.), whereas this article advances the literature by including an objectively assessed measure of distance to a perceived flood zone, using a cognitive mapping methodology. The article finds that distance to the perceived flood zone (perceived flood exposure) is a crucial factor in determining flood‐risk perception, both the cognitive and affective components. Furthermore, we find an interesting phenomenon of misperception among respondents. The article concludes by discussing the implications for flood‐risk management.     

Understanding the Downshifting Phenomenon: A Case of South East Queensland,Australia

A quality of life survey of a sample of households across the Brisbane‐South East Queensland region has identified about 28 percent of people as ‘downshifters.‘ They are defined as people who voluntarily make a long‐term change in their lifestyle – other than planned retirement – which reduces income and adjusts their lifestyle conditions. A typology of downshifters is developed on the basis of their motives for downshifting and their socio‐economic and demographic characteristics using a Two Step Cluster Analysis. Results indicate that the social and economic circumstances and the reasons and methods of downshifting tend to vary substantially across the clusters.