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Motivated by the COVID‐19 pandemic, we study how a public health authority may allocate vaccines from a limited stockpile to different jurisdictions over time. We propose an epidemiological model with time‐varying contact rates determined by a stylized behavioral feedback mechanism to reflect multi‐wave transmission dynamics. We evaluate the performance of various information‐sensitive allocation policies (e.g., allocation proportional to local incidence) as alternatives to the widely used pro‐rata policy. We also obtain optimized allocation strategies under the proposed epidemiological model with fairness and implementable freeze‐period constraints. For the case of a multi‐wave epidemic as represented by our compartmental model with behavioral feedback, we find that none of the alternative policies offers consistently more efficient allocations than a simple pro‐rata policy across a broad range of behavioral parameter settings. In fact, in some cases the alternative policies may actually result in less efficient allocations than the pro‐rata policy. Thus our results support the conclusion that the widely used pro‐rata policy can be well justified because it is simple to explain/implement and does not cause unexpected adverse effects. However, if policy makers are willing to invest in more tailored strategies based on numerical optimization, then the identified optimized strategies are a more favorable option as they allow for a more efficient allocation of vaccines. [ABSTRACT FROM AUTHOR]

Shall Follow-up Appointments Be Booked in Advance? Appointment systems are ubiquitous, especially in healthcare. By looking into a large data set with over 1.6 million appointments, we observe that many doctors booked a follow-up appointment at the end of their meeting with their patients. This strategy ensures that the patients would follow up but at the risk that the patient may not show up and the appointment ends being wasted. We develop a slotted-service queue model to study if and when such a strategy should be used in three representative appointment systems, respectively. In an open access system, it is optimal to never use this strategy. In a traditional appointment system that allows patients to book in advance, it is optimal to apply this strategy to some patients. While in a hybrid system with both walk-in patients and patients with appointments, whether to use this strategy depends on the load balancing between the two patient queues. We study an appointment-based slotted-service queue with the goal of maximizing service volume. Returning customers prefer to be served by the same service agent as in their previous visit. This model captures aspects of a whole host of settings, including medical clinics, law firms, and tutoring services. We consider a simple strategy that a service provider may use to reduce balking among returning customers—designate some returning customers as high-priority customers. These customers are placed at the head of the queue when they call for a follow-up appointment. In an appointment-based system, this policy can be implemented by booking a high-priority returning customer's appointment right before he or she leaves the service facility. We focus on a need-based policy in which the decision to prioritize some customers depends on their return probability. We analyze three systems: an open-access system, a traditional appointment system, and a carve-out system. We show that in an open-access system, the service provider should never prioritize returning customers in order to maximize the throughput rate. However, it is always optimal to prioritize some customers in a traditional appointment system. In the carve-out system, which may be modeled as a system with two parallel queues, the optimal policy varies depending on which queue is more congested. In the traditional system, we prove that the throughput rate is a quasi-concave function of the threshold under the assumption that returning customers see time averages. This allows service systems to determine optimal operating policies that are both easy to implement and provably optimal. History: This paper has been accepted for the Operations Research Special Issue on Behavioral Queueing Science. Funding: This work was supported by Fonds de Recherche du Québec-Société et Culture [Grant 295837], National Natural Science Foundation of China [Grant 72061127002, 71731009], and Natural Sciences and Engineering Research Council of Canada [Grant RGPIN 2019-05539]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2022.2299. [ABSTRACT FROM AUTHOR]

Inpatient staffing costs are significantly affected by nurse absenteeism, which is typically high in U.S. hospitals. We use data from multiple inpatient units of two hospitals to study which factors, [...]

This paper provides a methodology for predicting post‐transplant kidney function, that is, the 1‐year post‐transplant estimated Glomerular Filtration Rate (eGFR‐1) for each donor‐candidate pair. We apply customized machine‐learning algorithms to pre‐transplant donor and recipient data to determine the probability of achieving an eGFR‐1 of at least 30 ml/min. This threshold was chosen because there is insufficient survival benefit if the kidney fails to generate an eGFR‐1 ≥ 30 ml/min. For some donor‐candidate pairs, the developed algorithm provides highly accurate predictions. For others, limitations of previous transplants' data results in noisier predictions. However, because the same kidney is offered to many candidates, we identify those pairs for whom the predictions are highly accurate. Out of 6977 discarded older‐donor kidneys that were a match with at least one transplanted kidney, 5282 had one or more identified candidate, who were offered that kidney, did not accept any other offer, and would have had ≥80% chance of achieving eGFR‐1 ≥ 30 ml/min, had the kidney been transplanted. We also show that transplants with ≥80% chance of achieving eGFR‐1 ≥ 30 ml/min and that survive 1 year have higher 10‐year death‐censored graft survival probabilities than all older‐donor transplants that survive 1 year (73.61% vs. 70.48%, respectively). [ABSTRACT FROM AUTHOR]

Patients' satisfaction with an appointment system when they attempt to book a nonurgent appointment is affected by their ability to book with a doctor of choice and to book an [...]

Suppliers routinely sell goods to retailers on credit. Common credit terms are tantamount to a schedule of declining discounts (escalating penalties) that depend on how long the retailer takes to [...]

Appointment scheduling systems are used by primary and specialty care clinics to manage access to service providers, as well as by hospitals to schedule elective surgeries. Many factors affect the performance of appointment systems including arrival and service time variability, patient and provider preferences, available information technology and the experience level of the scheduling staff. In addition, a critical bottleneck lies in the application of Industrial Engineering and Operations Research (IE/OR) techniques. The most common types of health care delivery systems are described in this article with particular attention on the factors that make appointment scheduling challenging. For each environment relevant decisions ranging from a set of rules that guide schedulers to real-time responses to deviations from plans are described. A road map of the state of the art in the design of appointment management systems is provided and future opportunities for novel applications of IE/OR models are identified. Keywords: Appointment scheduling, health care operations management, access rules, 1. Introduction The health care industry represents approximately 15% of the gross domestic product of the United States. Health care expenditures are growing at a rate such that the amount [...]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2005.04.021 Byline: Diwakar Gupta, Waressara Weerawat Keywords: Game theory; Inventory; Supply chain coordination; Incentives; Production economics Abstract: Manufacture-to-order is an increasingly popular strategy in commodity electronics and other similar markets where many different product configurations can be produced from common components. To succeed in this environment, manufacturers need to keep both cost and order fulfillment time low. In this article, we compare three different mechanisms that a manufacturer, whose revenues depend on order delays, may use to affect its component supplier's inventory decisions. These mechanisms are specifying components inventory level, offering a share of the earned revenues to the supplier (called simple revenue sharing), and offering a two-part revenue-sharing scheme. We show that whereas the first two approaches do not lead to supply chain coordination, the two-part scheme does. We demonstrate with numerical experiments that up to a point, the component supplier benefits from having a high utilization of its production facility, whereas the manufacturer benefits from having excess production capacity. Author Affiliation: Graduate Program in Industrial Engineering, Department of Mechanical Engineering, University of Minnesota, Minneapolis, MN 55455, United States Department of Industrial Engineering, Mahidol University, Thailand Article History: Received 10 March 2003; Accepted 1 April 2005

Consider two markets of different sizes but similar costs and fare structure. All other things being equal, is an airline's expected revenue larger in the market with larger demand? If [...]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2004.07.048 Byline: Diwakar Gupta (a), Arthur V. Hill (b), Tatiana Bouzdine-Chameeva (c) Keywords: Retailing; Inventory; Pricing Abstract: The problem of setting prices for clearing retail inventories of fashion goods is a difficult task that is further exacerbated by the fact that markdowns enacted near the end of the selling season have a smaller impact on demand. In this article, we present discrete-time models for setting clearance prices in such an environment. When demand is deterministic, we compute optimal prices and show that decreasing reservation prices lead to declining optimal prices. When demand is stochastic and arbitrarily correlated across planning periods, we obtain bounds on the optimal expected revenue and on optimal prices. We also develop a heuristic procedure for finding near-optimal prices and test its accuracy through numerical experiments. These experiments reveal new insights for practitioners. For example, the penalty for choosing clearance price once and keeping it unchanged for the remainder of the selling season is found to be small when either the mean reservation prices do not change appreciably over time or when they drop sharply after the first period. Author Affiliation: (a) Department of Mechanical Engineering, Graduate Program in Industrial Engineering, University of Minnesota, 111 Church Street, SE Minneapolis, MN 55455, USA (b) Operations and Management Science Department, Curtis L. Carlson School of Management, University of Minnesota, 321 19th Avenue South, Room 3-150, Minneapolis, MN 55455-0413, USA (c) Department of Information, Decision and Management, Bordeaux Business School, 680 cours de la Liberation Talence 33405, France Article History: Received 2 September 2003; Accepted 15 July 2004

We develop an approximation scheme for performance evaluation of serial supply systems when each stage operates like a single-server queue, and its planned inventories are managed according to a base-stock [...]

Integrated Steel Manufacturers (ISMs) perform all of the steps necessary to convert iron ore into finished products. As a result they are characterized by high capital expenditures and long cycle times. The custom nature of finished products and significant demand uncertainty explains why ISMs typically produce to order. However, recent increased competition from low cost mini-mills is causing some ISMs to compete by serving the needs of higher-paying customers who want exotic products, and faster and reliable deliveries. Consequently, ISMs are exploring the option of satisfying a portion of their demand by converting strategically placed semi-finished inventory into finished products, which helps to reduce both the time between order receipt and order dispatch, and its variability. In this study we propose a two-stage stochastic integer programming model that can be used to choose the semi-finished products that should be made to stock, and their target inventory levels. Properties of this model are exploited to develop a fast heuristic applicable to large-scale instances of the problem encountered in industry. Numerical experiments are used to validate the heuristic, and examples based on data from a particular ISM are used to illustrate important managerial insights., 1. Introduction Industries in which products are highly customized tend to operate primarily according to a Make-To-Order (MTO) policy. However, it is frequently the case that some proportion of the [...]

Delaying product differentiation is a hybrid strategy that strives to reconcile the dual needs of high variety and quick response time. A common product platform is built to stock in the first stage of production (called the Make-To-Stock (MTS) stage) which is then differentiated into different products after demand is known in the second stage (known as the Make-To-Order (MTO) stage). In this article, we develop models to compute the costs and benefits of delaying differentiation in series production systems when the order lead times are load dependent. The models are then used to gain insights through analytical and numerical comparisons. For example, we observe the following patterns in a large number of numerical experiments. The effect of congestion in the MTS and MTO stages is asymmetric with tighter capacity at the MTO stage having a greater detrimental effect on the desirability of delaying differentiation. If there is flexibility in choosing the point of differentiation, higher loading is observed to favor later differentiation. Also, if the sequence in which work is performed can be affected, then placing workstations that have a tighter capacity in the MTS stage lowers costs., 1. Introduction In today's business environment, a manufacturing firm that has the ability to fill customer orders quickly, as well as offer custom products, enjoys a competitive advantage. However, the [...]

This study is concerned with the determination of optimal appointment times for a sequence of jobs with uncertain durations. Such appointment systems are used in many customer service applications to increase the utilization of resources, match workload to available capacity, and smooth the flow of customers. We show that the problem can be expressed as a two-stage stochastic linear program that includes the expected cost of customer waiting, server idling, and a cost of tardiness with respect to a chosen session length. We exploit the problem structure to derive upper bounds that are independent of job duration distribution type. These upper bounds are used in a variation of the standard L-shaped algorithm to obtain optimal solutions via successively finer partitions of the support of job durations. We present new analytical insights into the problem as well as a series of numerical experiments that illustrate properties of the optimal solution with respect to distribution type, cost structure, and number of jobs., 1. Introduction Appointment systems are used in many customer service industries to increase the utilization of resources, match workload to available capacity, and smooth the flow of customers. A common [...]