Knowledge that Transforms

In recent years, many urban areas have established healthcare coalitions (HCCs) composed of autonomous (and often competing) hospitals, with the goal of improving emergency preparedness and response. We study the role of such coalitions in the specific context of response to multiple‐casualty incidents in an urban setting, where on‐scene responders must determine how to send casualties to medical facilities. A key function in incident response is multi‐agency coordination. When this coordination is provided by an HCC, responders can use richer information about hospital capacities to decide where to send casualties. Using bed availability data from an urban area and a suburban area in the United States, we analyze the response capability of healthcare infrastructures under different levels of coordination, and we develop a stress test to identify areas of weakness. We find that improved coordination efforts should focus on decision support using information about inpatient resources, especially in urban areas with high inter‐hospital variability in resource availability. We also find that coordination has the largest benefit in small incidents. This benefit is a new value proposition for HCCs, which were originally formed to improve preparedness for large disasters.

For many retailers, markdown decisions are taken by retail buyers whose compensation is based on sales revenue so their objective is to maximize it through the season. This implies that the buyers' objectives are not perfectly aligned with the overall profitability the firm. Many retailers set markdown budgets prior to the season to control margin erosion and increase profitability. Markdown budget constrains the buyers on the amount of discounts that they can apply on a given inventory of merchandise and sets a limit on the dollar value of markdowns for the season. While markdown budgets may be useful in preventing excessive discounts, they can have a detrimental effect on the buyers' ability to respond to poor market and remove distressed inventory. We investigate the effectiveness of this practice in aligning the incentives of buyers with that of the firm, and provide guidance on how these budgets should be established ahead of time. We consider a firm with a fixed inventory of a seasonable item, and a single chance to mark the price down. The retailer knows only the demand distribution at the beginning of the season, but the market information is revealed during the season to the buyer. We first characterize the buyer's markdown policy and understand the circumstances under which this can be different from the retailer's markdown policy. We use our model to determine the optimal markdown budget and quantify its effectiveness considering different factors such as the level of demand uncertainty, initial markup, and market's responsiveness to markdowns.

Due to yields of less than 50% during the production of curved glass for the displays on their new cell phone series, Samsung has to deal with higher than expected production costs of several million dollars. Where there is random yield, production costs as well as holding costs can be reduced by introducing quality inspections, in which defective items are discarded before further production. To achieve the greatest cost savings, it is important to determine the optimal number and positions of these inspections across the production process which, due to several influencing parameters, is not simple. We show how the positions of inspection within a production process influence the safety stock level that is required to buffer against uncertainties due to demand and yield randomness. Our approach is the first one, combining decisions about the number and positions of inspections with inventory control strategies in a warehouse. We achieve a maximum safety stock reduction of more than 30% in our examples, which can be even larger depending on the parameter setting. For a company like Intel, reporting inventories for finished goods of nearly 1.5 billion dollars in the 2014 annual report, this allows for significant savings.

We study a joint implementation of price‐ and availability‐based product substitution to better match demand and constrained supply across vertically differentiated products. Our study is motivated by firms that utilize dynamic pricing as well as customer upgrades, as ex ante and ex post mechanisms, respectively, to mitigate inventory mismatches. To gain insight into how offering product upgrades impacts optimal price selection, we formulate a multiple period, nested two‐stage model where the firm first sets prices and replenishment levels for each product while the demand is still uncertain, and after observing the demand, decides how many (if any) of the customers to upgrade to a higher quality product. We characterize the structure of the optimal upgrade, pricing and replenishment policies and find that firms having greater flexibility to offer product upgrades can restrain their reliance on dynamic pricing, enabling them to better protect the price differentiation between the products. We also show how the quality differential between the products or changes in the replenishment cost structures influence the optimal policy. Using insights gained from the optimal policy structure, we construct a heuristic policy and find that it performs well across various parameter values. Finally, we consider an extension in which the firm dynamically sets upgrade fees in each period. Our results overall help further our understanding of the intricate relationship among a firm's decisions on pricing, replenishment, and product upgrades in an effort to better match demand and constrained supply.

In scheduling medical residents, the objective is often to maximize resident satisfaction across the space of feasible schedules, relative to the many hard constraints that ensure appropriate patient coverage, adequate training opportunities, etc. A common metric of resident satisfaction is the number of time‐off requests that are granted. Simply maximizing this total, however, may lead to undesirable schedules since some requests have higher priority than others. For example, it might be better to grant one resident's request for a family member's wedding in place of two residents’ requests to attend a rugby game. Another approach is to assign a weight to each request and maximize the total weight of granted requests, but determining weights that accurately represent residents’ and schedulers’ preferences can be quite challenging. Instead, we propose to identify the exhaustive collection of maximally feasible and minimally infeasible sets of requests which can then be used by schedulers to select their preferred solution. Specifically, we have developed two algorithms, which we call Sequential Request Selection Via Cuts (Sequential RSVC) and Simultaneous Request Selection Via Cuts (Simultaneous RSVC), to identify these sets by solving two sequences of optimization problems. We present these algorithms along with computational results based on a real‐world problem of scheduling residents at the University of Michigan C.S. Mott Pediatric Emergency Department. Although we focus our exposition on the problem of resident scheduling, our approach is applicable to a broad class of problems with soft constraints.

We study a single‐stage, continuous‐time inventory model where unit‐sized demands arrive according to a renewal process and show that an ( s, S) policy is optimal under minimal assumptions on the ordering/procurement and holding/backorder cost functions. To our knowledge, the derivation of almost all existing ( s, S)‐optimality results for stochastic inventory models assume that the ordering cost is composed of a fixed setup cost and a proportional variable cost; in contrast, our formulation allows virtually any reasonable ordering‐cost structure. Thus, our paper demonstrates that ( s, S)‐optimality actually holds in an important, primitive stochastic setting for all other practically interesting ordering cost structures such as well‐known quantity discount schemes (e.g., all‐units, incremental and truckload), multiple setup costs, supplier‐imposed size constraints (e.g., batch‐ordering and minimum‐order‐quantity), arbitrary increasing and concave cost, as well as any variants of these. It is noteworthy that our proof only relies on elementary arguments.

The Braess Paradox (BP) illustrates an important counterintuitive observation that adding links to a directed transportation network with usage externalities may raise the costs of all users. Research on the BP traditionally focuses on congestible networks. We propose and experimentally test a new and more dramatic version of the BP, where the network exhibits both congestion (negative externalities) and cost‐sharing (positive externalities) characteristics. Our design also involves experimental manipulation of choice observability, where players choose routes simultaneously in one condition and sequentially in the other. We report robust behavioral evidence of the BP in both conditions. In nine of 10 sessions in the basic network, subjects coordinated successfully to achieve the welfare‐maximizing equilibrium. But once the network was augmented with a new link, coordination failure resulted in a major proportion of subjects switching to a new route, resulting in a 37% average increase in individual travel cost across conditions.

In project portfolio selection, the aim is to choose projects which are expected to offer most value and satisfy relevant risk and other constraints. In this study, we show that uncertainties about how much value the projects will offer, combined with the fact that only a subset of the proposed projects will be selected, lead to inaccurate risk estimates about the aggregate value provided by the selected project portfolio. In particular, when downside risks are measured in terms of lower percentiles of the distribution of portfolio value, these risk estimates will exhibit a systematic bias. For deriving unbiased risk estimates, we present a calibration framework in which the required calibration can be presented in closed‐form in some cases or, more generally, derived by using Monte Carlo simulation to study a large number of project selection decisions. We also show that when the decision must comply with risk constraints, the introduction of tighter (more demanding) risk constraints can counterintuitively aggravate the underestimation of risks. Finally, we present how the calibrated risk estimates can be employed to align the portfolio with the decision maker's risk preferences while eliminating systematic biases in risk estimates.