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This article studies the optimal allocation of inpatient rooms for multiple types of patients in public hospitals and the patient scheduling problem with planned acceptance ratios (ARs). For public hospitals, it is important to allocate limited resources to multiple types of patients and manage patient access for maximizing hospital revenue and upholding service equity. The problem is formulated as two-stage models. Considering uncertainties in patients’ arrival and length of stay, we first propose a nonlinear stochastic programming (NSP) model for inpatient room allocation with the objective of maximizing revenue under the constraints of maintaining equity. To solve this problem, we transform the complex NSP model into a deterministic mixed-integer linear programming model, which is solved by CPLEX, by reformulating the chance constraints as knapsack constraints based on a linearization technique and a simulation model. Given the allocated capacity and planned AR, we further propose a two-stage stochastic mixed-integer program combined with a goal program model to optimize patient scheduling. To solve the model, a Benders decomposition based on the sample average approximation approach is proposed. The real data-based experimental results demonstrate the applicability and effectiveness of our models and approaches. The impacts of some parameters on the objective and decisions are also explored. A simulation procedure is developed to compare the performances of different patient scheduling methods, from which the results show that our proposed approach outperforms a benchmark policy. Note to Practitioners—Inpatient rooms are critical resources for hospitals. Against the background of aging populations and environmental problems, the twofold predicament—involving escalating healthcare demands and insufficient room-based resources—has led to the necessity for hospitals to operate more effectively and efficiently. Capacity management and patient scheduling are thus the two most important operations for hospital management. Because of the self-financing feature of hospitals and the quasi-public nature of medical services, it is important for public hospitals to judiciously allocate limited room capacities to multiple types of patients for balancing revenue and equity and schedule the arrival demands dynamically according to the planned capacity and acceptance ratio. In this article, we propose mathematical models and solution approaches for these two-stage problems, and their applicability and effectiveness are demonstrated by experiments based on real data. The managerial insights suggest that hospitals should improve the service equity gradually according to their financial situation because of the increasing marginal cost and should apply the scientific approaches and techniques, rather than by their experiences, to aid their management for better performance. By using approaches proposed in this article, hospital managers can be equipped with a decision support tool for effective capacity allocation and patient scheduling decisions. The parameters of our models could be tuned based on the preferences of different hospitals. Furthermore, these approaches can be applied to other settings with similar problems, such as government budget allocation considering both utility and equity, and service system management with waiting time requirement. [ABSTRACT FROM AUTHOR]

In this paper, a modified centralized algorithm based on particle swarm optimization (MCPSO) is presented to solve the task allocation problem in the search and rescue domain. The reason for this paper is to provide a benchmark against distributed algorithms in search and rescue application area. The hypothesis of this paper is that a centralized algorithm should perform better than distributed algorithms because it has all the available information at hand to solve the problem. Therefore, the centralized approach will provide a benchmark for evaluating how well the distributed algorithms are working and how much improvement can still be gained. Among the distributed algorithms, the consensus-based bundle algorithm (CBBA) is a relatively recent method based on the market auction mechanism, which is receiving considerable attention. Other distributed algorithms, such as PI and PI with softmax, have shown to perform better than CBBA. Therefore, in this paper, the three distributed algorithms mentioned earlier are compared against three centralized algorithms. They are particle swarm optimization, MCPSO, described in this paper, and genetic algorithms. Two experiments were conducted. The first involved comparing all the above-mentioned algorithms, both centralized and distributed, using the same set of application scenarios. It is found that MCPSO always outperforms the other five algorithms in time cost. Due to the high failure rate of CBBA and the other two centralized methods, the second experiment focused on carrying out more tests to compare MCPSO against PI and PI with softmax. All the results are shown and analyzed to determine the performance gaps between the distributed algorithms and the MCPSO. Note to Practitioners—This paper was motivated by the limitation of current distributed task allocation algorithms as they cannot achieve performances that are as good as the centralized ones. Therefore, a centralized algorithm is designed to evaluate the performance gap between the state-of-the-art distributed and centralized approaches. In the future research section, a new distributed particle swarm optimization (PSO) algorithm is proposed based on this paper as the research has shown that the proposed centralized PSO algorithm delivers the best results so it is potentially a strong candidate for adaptation. [ABSTRACT FROM AUTHOR]

The intensive care unit (ICU) delivers care to critically ill patients with high resource intensity. Stochastic patient arrival and uncertain length of stay in ICUs present tremendous challenge to establish the admission and discharge policy to maximize the number of surviving patients and improve operational efficiency. In practice, ICU schedulers reserve some beds for potential patients with most critical conditions. Moreover, they prematurely discharge current ICU patients who are in stable status to accommodate for new and more urgent arrivals. We develop an analytical framework to quantify the impact of the number of reserved beds and suggest when to prematurely discharge current patients. A Markov decision process model is established to strike a balance between the rejection of incoming patient and the premature discharge in the near future. Monotonicity, concavity, and structural properties of the optimal control policy are presented. Using the inpatient record at a tertiary-level hospital in China, we conduct a case study and propose an effective threshold policy. Extensive numerical experiments are performed to analyze the effect of each parameter on the total survival benefits and compare different policies. Note to Practitioners—The intensive care unit (ICU) is the most resource intensive unit in the hospital, which has a significant impact on patient safety and care quality. Due to the increasing and aging population, the contradiction between the increasing demand and the insufficient ICU resources puts eligible ICU candidates at risk of lacking timely ICU care. Given the important role of the ICU in surviving lives of most critical patients, it is of paramount significance to improve ICU resource utilization and maximize the number of ICU survivors. Therefore, the objective of this paper is to develop a mathematical model to study ICU admission policies and premature discharge decisions. Based on our analytical framework, the ICU admission director can evaluate performance measures of ICU admission control system and investigate the impact of different policies on care quality of patients. By using an effective threshold admission policy proposed in this paper, the ICU admission director can be equipped with a decision support tool for the ICU admission control decisions with quantitative measures of potential impact. [ABSTRACT FROM AUTHOR]

This paper addresses the dynamic assignment of a given set of surgeries to multiple identical operating rooms (ORs). Surgeries have random durations and planned surgeon arrival times. Surgeries are assigned dynamically to ORs at surgery completion events. The goal is to minimize the total expected cost incurred by surgeon waiting, OR idling, and OR overtime. We first formulate the problem as a multistage stochastic programming model. An efficient algorithm is then proposed by combining a two-stage stochastic programming approximation and some look-ahead strategies. A perfect information-based lower bound of the optimal expected cost is given to evaluate the optimality gap of the dynamic assignment strategy. Numerical results show that the dynamic scheduling and optimization with the proposed approach significantly improve the performance of static scheduling and First Come First Serve (FCFS) strategy. [ABSTRACT FROM AUTHOR]

This paper addresses the design of contract for reserving the capacity of a shared critical resource from the perspective of a given class of customers. The contract is composed of three parts: contracted time slots (CTSs) reserved by the service provider for the class of customers, advance cancellation of contracted time slots, and requests for regular time slots (RTSs). The problem of CTS cancellation and RTS assignment is formulated as an average cost Markov Decision Process in order to minimize the total cost including customer waiting times, unused CTS, and CTS cancellation. Structural properties of the optimal control policies are established via the discounted cost problem. A local optimization algorithm is proposed to improve a given initial contract. Numerical results show that advance CTS cancellation significantly reduces the ratio of unused CTS with slight increase of customer waiting time. [ABSTRACT FROM PUBLISHER]