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We study the facility location problem with disruptions where the objective is to choose a set of locations that minimizes the sum of expected servicing and setup costs. Disruptions can affect multiple locations simultaneously and are caused by multiple factors like geography, supply chain characteristics, politics, and ownership. Accounting for the various factors when modeling disruptions is challenging due to a large number of required parameters, the lack of calibration methodologies, the sparsity of disruption data, and the number of scenarios to be considered in the optimization. Because of these reasons, existing models neglect dependence or prespecify the dependence structures. Using partially subordinated Markov chains, we present a comprehensive approach that starts from disruption data, models dependencies, calibrates the disruption model, and optimizes location choices. We construct a metric and a calibration algorithm that learns from the data the strength of dependence, the number of necessary factors (subordinators), and the locations each subordinator affects. We prove that our calibration approach yields consistent estimates of the model parameters. Then, we introduce a variant of the standard approach to the underlying optimization problem, which leverages partially subordinated Markov chains to solve it quickly and precisely. Finally, we demonstrate the efficacy of our approach using twelve different disruption data sets. Our calibrated parameters are robust, and our optimization algorithm performs better than the simulation-based algorithm. The solutions from our model for disruptions have lower costs than those from other disruption models. Our approach allows for better modeling of disruptions from historical data and can be adapted to other problems in logistics, like the hub location, capacitated facility location, and so on., with joint disruptions. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0103. [ABSTRACT FROM AUTHOR]

A sequential clinical scheduling method for patients with general service time distributions is developed in this paper. Patients call a medical clinic to request an appointment with their physician. During the call, the scheduler assigns the patient to an available slot in the physician's schedule. This is communicated to the patient before the call terminates and, thus, the schedule is constructed sequentially. In practice, there is very limited opportunity to adjust the schedule once the complete set of patients is known. Scheduled patients might not attend, that is, they might 'no-show,' and the service times of those attending are random. A myopic scheduling algorithm with an optimal stopping criteria for this problem assuming exponential service times already exists in the literature. This work relaxes this assumption and develops numerical techniques for general service time distributions. A special case in which service times are gamma distributed is considered and it is shown that computation is significantly reduced. Finally, exhaustive experimental results are provided along with discussions that provide insights into the practical aspects of the scheduling approach. Keywords: Overbooking, appointment scheduling, patient no-shows, outpatient clinics, 1. Introduction and literature review Between 80 and 90% of U.S. patient care is provided by approximately 200 000 non-psychiatric outpatient clinics (Bodenheimer and Grumback, 2002; United States Census, 2004). [...]

In this paper a stochastic overbooking model is formulated and an appointment scheduling policy is developed for outpatient clinics. The schedule is constructed for a single service period partitioned into time slots of equal length. A clinic scheduler assigns patients to slots through a sequential patient call-in process where the scheduler must provide each calling patient with an appointment time before the patient's call terminates. Once an appointment is added to the schedule, it cannot be changed. Each calling patient has a no-show probability, and overbooking is used to compensate for patient no-shows. The scheduling objective captures patient waiting time, staff overtime and patient revenue. Conditions under which the objective evolution is unimodal are derived and the behavior of the scheduling policy is investigated under a variety of conditions. Practical observations on the performance of the policy are presented. Keywords: Open access, appointment scheduling, patient no-shows, outpatient clinic operations, 1. Introduction Healthcare currently consumes 15% of the US Gross Domestic Product and is expected to reach 19% within the coming decade (Centers for Medicare and Office of the Actuary [...]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jedc.2006.04.005 Byline: Kumar Muthuraman Abstract: We consider the optimal investment - consumption strategy of an investor who can invest in a stock and a bank. We consider the case where proportional transaction costs are present and the objective is to maximize the discounted utility of consumption. We describe an efficient computational scheme that transforms the arising free-boundary problem to a sequence of fixed-boundary problems. We prove the convergence of the scheme and also show that the converged solution is the optimal value function. Finally, we compare and contrast the results obtained by our procedure with certain well-known results and approximations. The proposed scheme also lends itself to optimizing portfolios with multiple risky assets. Author Affiliation: School of Industrial Engineering, Purdue University, 315 N Grant St., West Lafayette, IN 47906, USA Article History: Received 15 March 2005; Accepted 5 April 2006

Abstract Background Non-pharmaceutical interventions (NPI) are the first line of defense against pandemic influenza. These interventions dampen virus spread by reducing contact between infected and susceptible persons. Because they curtail essential societal activities, they must be applied judiciously. Optimal control theory is an approach for modeling and balancing competing objectives such as epidemic spread and NPI cost. Methods We apply optimal control on an epidemiologic compartmental model to develop triggers for NPI implementation. The objective is to minimize expected person-days lost from influenza related deaths and NPI implementations for the model. We perform a multivariate sensitivity analysis based on Latin Hypercube Sampling to study the effects of input parameters on the optimal control policy. Additional studies investigated the effects of departures from the modeling assumptions, including exponential terminal time and linear NPI implementation cost. Results An optimal policy is derived for the control model using a linear NPI implementation cost. Linear cost leads to a "bang-bang" policy in which NPIs are applied at maximum strength when certain state criteria are met. Multivariate sensitivity analyses are presented which indicate that NPI cost, death rate, and recovery rate are influential in determining the policy structure. Further death rate, basic reproductive number and recovery rate are the most influential in determining the expected cumulative death. When applying the NPI policy, the cumulative deaths under exponential and gamma terminal times are close, which implies that the outcome of applying the "bang-bang" policy is insensitive to the exponential assumption. Quadratic cost leads to a multi-level policy in which NPIs are applied at varying strength levels, again based on certain state criteria. Results indicate that linear cost leads to more costly implementation resulting in fewer deaths. Conclusions The application of optimal control theory can provide valuable insight to developing effective control strategies for pandemic. Our findings highlight the importance of establishing a sensitive and timely surveillance system for pandemic preparedness.

Many parts of the healthcare system remain fragmented and outpatient surgical care is no exception. In this study, we develop a coordinated pre‐operative scheduling approach between Anesthesiology and Internal Medicine to optimize patients’ medical conditions prior to surgery. Coordinating these two services has conceptual appeal because any health issues discovered by the anesthesiologist can often be addressed by a general internist. We design a patient‐centered approach that allows the patient to see both providers, if necessary, on a single visit. This problem is modeled as a two‐station stochastic network, where each station (or clinic) may be staffed by multiple parallel providers and patients see the first available provider. To solve the scheduling problem, we formulate an optimization model to maximize the net benefit of scheduling patients. The objective balances benefits against patient waiting time and clinic overtime costs. We develop a scheduling method with a booking limit to create a balanced network schedule. Due to the complexity of this problem, the solution approach is myopic. In addition, we develop a hybrid method that combines analytical calculation and simulation‐based optimization. We demonstrate our approach on a healthcare network at The University of Texas Health Sciences Center in San Antonio. We compare our method against other policies and show that it yields high quality and robust results. Based on the level of generality of our model and results, the insights gained are not limited to the particular application, but can be applied to other patient‐centered models where scheduling coordination can be used. [ABSTRACT FROM AUTHOR]

This study analyzes optimal replenishment policies that minimize expected discounted cost of multi-product stochastic inventory systems. The distinguishing feature of the multi-product inventory system that we analyze is the existence of correlated demand and joint-replenishment costs across multiple products. Our objective is to understand the structure of the optimal policy and use this structure to construct a heuristic method that can solve problems set in real-world sizes/dimensions. Using an MDP formulation we first compute the optimal policy. The optimal policy can only be computed for problems with a small number of product types due to the curse of dimensionality. Hence, using the insight gained from the optimal policy, we propose a class of policies that captures the impact of demand correlation on the structure of the optimal policy. We call this class ( s, c, d, S)-policies, and also develop an algorithm to compute good policies in this class, for large multi-product problems. Finally using an exhaustive set of computational examples we show that policies in this class very closely approximate the optimal policy and can outperform policies analyzed in prior literature which assume independent demand. We have also included examples that illustrate performance under the average cost objective. [ABSTRACT FROM AUTHOR]