Early infant diagnosis (EID) programs in many resource-limited settings are aimed at diagnosing infants born to HIV positive mothers. Due to the complexity of the diagnostic technology, EID programs are often highly centralized with few laboratories testing blood samples from a large network of health facilities. This leads to long diagnostic delays and consequent failure of patients to collect results in a timely manner. Several point-of-care (POC) devices that provide rapid diagnosis within the health facilities are being developed to mitigate these drawbacks of centralized EID networks. We study the decision of which facilities should receive the POC device (the placement plan) using the EID program in Mozambique as a case-study. We argue that the choice of an appropriate plan is critical to maximizing the public health impact of POC devices in the presence of tight budget constraints. To formalize this argument, we develop a detailed simulation model to evaluate the impact of a placement plan. It comprises two parts: an operational model that quantifies the impact of a POC placement plan on the diagnostic delay and a behavioral part that quantifies the impact of diagnostic delay on the likelihood of result collection by infants' caregivers. We also develop an approximate version of these operational and patient behavior dynamics and embed them in an optimization model to generate candidate POC placement plans. We find that the optimization based plan can result in up to 30% more patients collecting their results compared to rules of thumb that have practical appeal. Finally, we show that the effectiveness of POC devices is much higher than other operational improvements to the EID network such as increased laboratory capacity, reduced transportation delay, and more regularized transport.
W e consider a two-echelon supply chain with a manufacturer supplying to multiple downstream retailers engaged in differentiated Cournot competition. Each retailer has private information about uncertain demand. The manufacturer is the Stackelberg leader who sets the contract terms with the retailers, and benefits from retailers sharing their private information. When all retailers are given the same wholesale price, truthful information sharing is not an equilibrium outcome. We propose two variants of differential pricing mechanisms that induce truthful information sharing by all retailers. The first variant rewards a retailer for providing optimistic information and achieves truthful information sharing as a unique equilibrium. The differential pricing mechanism is optimal in the class of linear-price, incentive-compatible, direct mechanisms. The second variant, which incorporates provision for a fixed payment in addition to wholesale prices, preserves all the equilibrium properties of the first variant and additionally ''nearly coordinates'' the supply chain. Our analysis of differential pricing with a fixed payment provides interesting observations regarding the relationship between product substitutability, number of retailers, information precision, and market power. As products become closer substitutes and/or number of retailers increase, the manufacturer's market power increases, enabling her to extract a larger fraction of the supply chain surplus.
Flight delays have been a growing issue and they have reached an all-time high in recent years, with the airlines' on-time performance at its worst level in 2007 since 1995. A recent report by the Joint Economic Committee of the U.S. Congress chaired by Senator Charles E. Schumer has estimated that the total cost to the U.S. economy because of flight delays was as much as $41 billion in 2007. The goal of this paper is to build stochastic models of airline networks and utilize publicly available data to answer the following policy questions: Which are the bottleneck airports in the U.S. air-travel infrastructure (i.e., airports that cause most delay propagation)? How would increasing airport capacity at these airports alleviate delay propagation? What are the appropriate metrics for measuring the robustness of airline schedules? How could these schedules be made more robust? Which flight in an aircraft rotation is a bottleneck flight (and, hence, deserves managerial attention)? Flight delays are typically attributed to two factors: (i) the randomness in the intrinsic travel time for a scheduled flight (which is the travel time excluding propagated delays), and (ii) the propagation of this randomness through the air-travel network and infrastructure. We model both of these factors that cause travel delays. The contribution of this paper is twofold. First, we develop stochastic models, using empirical data, to analyze the propagation of delays through air-transportation networks. Our stochastic models allow us to develop three important robustness measures for airline networks. Second, our analysis enables us to make policy recommendations regarding managing bottleneck resources in the air-travel infrastructure, which, if addressed, could lead to a significant improvement in air-travel reliability.
A irline schedule development continues to remain one of the most challenging planning activities for any airline. An airline schedule comprises a list of flights and specifies the origin, destination, scheduled departure, and arrival time of each flight in the airline's network. A critical component of the schedule development activity is the choice of flight block-times, which depend on several factors. Many airlines decide schedule blocktimes based on fixed percentiles of block-time distributions built from historical data, however, such techniques have not resulted in significantly improved on-time performance (OTP) of the schedule during operations. Thus, from a passenger's perspective, the service-level guarantee of an airline's network continues to be low. We first define two service-level metrics for an airline schedule. The first one is similar to the OTP measure of the U.S. Department of Transportation and we define it as the flight service level. The second metric, called the network service level, is geared toward completion of passenger itineraries. We then develop a stochastic integer programming formulation that optimally perturbs a given schedule to maximize expected profit, while ensuring the two service levels. We also develop a variant of this model that maximizes service levels, while achieving desired network profitability. To solve these models, we develop an efficient algorithm that guarantees optimality. Through extensive computational experiments, using real-world data, we demonstrate that our models and algorithms are efficient and achieve the desired trade-off between service level and profitability.
In this paper we study threshold-based sales-force incentives and their impact on a dealer's optimal effort.A phenomenon, observed in practice, is that the dealer exerts a large effort towards the end of the incentive period to boost sales and reach the threshold to make additional profits. In the literature, the resulting last period sales spike, is sometimes called the hockey stick phenomenon (HSP.) We show that lack of information leads to the HSP and characterize its form over multiple time periods. Under perfect information it is possible to completely eliminate the HSP, however, this may be difficult in practice. We show that the manufacturer can control the HSP by using imperfect information to set the threshold and delay its computation until the last period. We discuss an implementation plan that allows the manufacturer to do so. We then study the impact of competition on the HSP and show conditions under which the HSP can be dampened or exacerbated. We also characterize the variance of the total sales across all the periods and demonstrate conditions under which offering a bonus contract may be beneficial in controlling the variance.
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