Constant Job-Allowance Policies for Appointment Scheduling: Asymptotic Optimality and Numerical Analysis

This study focuses on determining the appointment scheduling for healthcare facilities with series patients. “Series” patients are patients who are scheduled for a series of appointments instead of a single appointment. Examples of healthcare services with series patients include radiotherapy/chemotherapy for cancer, physical therapy, kidney dialysis, diabetes treatment, etc. The aim of this study is to design appointment scheduling policies taking into account revenues per service per patient, costs of staffing, overtime, overbooking and delay. The appointment scheduling problem is formulated using an MDP model. However, due to the huge state space, computing the optimal policy is impractical. Hence, we propose the Index Policy (IP) based on a one‐step policy improvement algorithm applied to the MDP model. We study a further simplification obtained by approximating the distribution of the number of patient visits by a Geometric distribution. A key analytical contribution is to prove the MDP to be a uni‐chain, which implies that there exists an optimal policy that maximizes the long‐run average profit. We also find that the IP provides a significant improvement over the other policies. Especially with the Geometric approximation, the IP requires minimal effort in implementing, and works almost as well. To test the effectiveness of our proposed IP in a real‐world setting, we use the data from a local physical therapy center to compare its performance with two other commonly used policies, namely, the Next Available Day Policy and the Shortest Queue Policy. We recommend the IP with Geometric approximation for series patients’ scheduling, which is computationally efficient and can significantly increases profits by incorporating the series feature of the patients’ appointments. Finally, we provide analysis that incorporates several practical considerations such as accounting for patient heterogeneity in number of visits and inter‐visit times, the option to reject new patients when the system is at full capacity, and incorporating patients with known number of visits at the time of the scheduling decision.

Appointment Scheduling Problem under Fairness Policy in Healthcare Services: Fuzzy Ant Lion Optimizer

This study investigates patient appointment scheduling and examination room assignment problems involving patients who undergo ultrasound examination with considerations of multiple examination rooms, multiple types of patients, multiple body parts to be examined, and special restrictions. Following are the recommended time intervals based on the findings of three scenarios in this study: In Scenario 1, the time interval recommended for patients’ arrival at the radiology department on the day of the examination is 18 min. In Scenario 2, it is best to assign patients to examination rooms based on weighted cumulative examination points. In Scenario 3, we recommend that three outpatients come to the radiology department every 18 min to undergo ultrasound examinations; the number of inpatients and emergency patients arriving for ultrasound examination is consistent with the original time interval distribution. Simulation optimization may provide solutions to the problems of appointment scheduling and examination room assignment problems to balance the workload of radiological technologists, maintain high equipment utilization rates, and reduce waiting times for patients undergoing ultrasound examination.

A common task for all deterministic multithreading (DMT) systems is to enforce synchronization determinism. However, synchronization determinism has not been the focus of existing DMT research. Instead, most DMT systems focused on how to order data races remained after synchronization determinism is enforced. Consequently, existing scheduling policies for synchronization determinism all have limitations. They may either require performance annotations to achieve good performance or fail to provide schedule stability.

The problem of finding the optimal appointment schedule has received significant consideration in the literature on healthcare optimization. Due to randomness, determining when clients should be scheduled in order to strike a balance in terms of idle times, waiting times, and overtime is a non-trivial task. Furthermore, it is often solely studied in the dimension of scheduling, whereas the sequencing decision in which heterogeneous clients (e.g., new and return clients) are to arrive is at least of equal importance. The approach presented integrates an approximation method with a scheduling heuristic that tackles this joint objective of sequencing and scheduling.A moment-iteration method is employed to construct appointment schedules. This method permits analytical solutions and thereby is incredibly fast to solve and optimize. Next, a sequential optimization approach is applied which aligns well with the moment-iteration method and permits per-client control of costs. Moreover, within this approach, the classical surgery scheduling problem of minimizing earliness and tardiness by determining due dates is equivalent to the appointment scheduling problem, i.e., in a sequentially optimal schedule the inter-arrival time equals the previous client’s due date.Finally, for the case of clients having heterogeneous service-time distributions, rigorous sequencing rules are derived in terms of the first two moments. The focus lies on non-identical exponential service times and log-normal service times, as commonly found in healthcare modelling. The smallest-mean/variance-first rule is sequentially optimal. For cases in which the mean and variance move in opposite directions contour plots are provided to aid practitioners in sequencing clients.

Home Care Routing and Appointment Scheduling with Stochastic Service Durations

In banking where customers are served from various channels, branch is still one of the most preferred service channels as a face to face communication channel. Key performance indicator of branch services is to minimize waiting times so that customers are served in a timely fashion. With the advances in digitalization capabilities, reservation ticketing systems for branches has become much more disseminated. Remote queuematic tickets taken from digital channels has been evaluated to be a valuable opportunity for optimizing resource and capacity plan of branches and diminishing service times, hence a research and development study has been initiated. This paper describes our novel dynamic programming algorithm approach, which is branch density and customer priority sensitive, aims to balance number of customers each branch serves within a particular region and minimize waiting times of customers. Proposed approach links this problem in banking domain to assortment optimization and appointment scheduling problems in the literature and solves an optimization problem that maximizes the added value for bank at branches.

Outpatient clinics are integral to healthcare, offering vital services without the need for hospital admission. However, appointment scheduling in these settings remains challenging due to uncertainties such as patient no-shows and variable service times. This study proposes a data-driven approach to minimize physician idle time and patient waiting time by analyzing an eight-year (2016-2023) dataset from primary and specialized care for American veterans. Adopting the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology, four predictive models: Random Forest, Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Artificial Neural Networks (ANN), were developed for both classification (noshow) and regression (service time) tasks. The ANN model demonstrated superior predictive performance in both domains. The key predictors of no-shows included waiting time, type of care and care provider, while type of care, care provider, and veteran ZIP code were the most influential in forecasting service time. These findings highlight the potential of machine learning to improve appointment scheduling in outpatient clinics.

When faced with a medical problem, patients contact their primary care physician (PCP) first. Here mainly two types of patient requests occur: non-scheduled patients who are walk-ins without an appointment and scheduled patients with an appointment. Number and position of the scheduled appointments influence waiting times for patients, capacity for treatment and the utilization of PCPs. As the number of patient requests differs significantly between weekdays, the challenge is to match capacity with patient requests and provide as few appointment slots as necessary. In this way, capacity for walk-ins is maximized while overall capacity restrictions are met. Decisions as to the optimal appointment capacity per day on a tactical decision level has gained little attention in the literature. A mixed integer linear model is developed, where the minimum number of appointments scheduled for a weekly profile is determined. We are thus able to give the answer as to how many appointments to offer on each day in a week in order to create a schedule that takes patient preferences as well as PCP preferences into account. Appointment schedules are often influenced by uncertain demands due to the number of urgent patients, interarrivals and service times. Based on an exemplary case study, the advantages of the optimal appointment schedule on different performance criteria are shown by detailed stochastic simulations.

Appointment scheduling with multiple providers and stochastic service times

An appointment scheduling framework to balance the production of blood units from donation

Problem statement: Providing an ageing strategy for Shortest Job First, keeping the waiting time as efficient as possible better than any other algorithm of similar kind. Approach: Relative waiting time and burst time of each process are compared to get an effective scheduling policy in a constant scheduling overhead. Result: Based on experiments and calculations, our algorithm have provided better waiting time than Highest Response Ratio Next (HRRN) in some cases, equal waiting time in most cases, very negligible more waiting time in some cases and a constant scheduling overhead. HRRN was the best known improvement on Shortest Job First till date. Conclusion: Our algorithm can prove very effective for batch processing systems and situations where burst time can be easily predicted.

We study the scheduling of multiple tasks under varying processing costs and derive a priority rule for optimal scheduling policies. Each task has a due date, and a non‐completion penalty cost is incurred if the task is not completely processed before its due date. We assume that the task arrival process is stochastic and the processing rate is capacitated. Our work is motivated by both traditional and emerging application domains, such as construction industry and freelance consulting industry. We establish the optimality of Shorter Slack time and Longer remaining Processing time (SSLP) principle that determines the priority among active tasks. Based on the derived structural properties, we also propose an effective cost‐balancing heuristic policy and demonstrate the efficacy of the proposed policy through extensive numerical experiments. We believe our results provide operators/managers valuable insights on how to devise effective service scheduling policies under varying costs.

The OS scheduler's memory and runtime overheads form crucial design constraints for embedded systems implemented on low-cost hardware platforms. Table-driven scheduling can provide a high level of schedulability; however, it also consumes significant amounts of memory. By contrast, effective non-preemptive scheduling policies, such as the non-work-conserving Critical-Window EDF (CW-EDF), have low memory usage, but substantial runtime overheads. This paper aims to achieve efficient and effective non-preemptive scheduling by using a First-In-First-Out (FIFO) scheduling policy combined with a novel offset tuning technique. This technique enables the FIFO policy to reproduce a given feasible schedule, such as that followed by CW-EDF, resulting in a high level of schedulability, combined with comparatively low runtime overheads. Further, by using a small number of offsets per task, memory overheads are also tightly constrained. The proposed solution is evaluated in terms of runtime overhead, memory consumption, and schedulability ratio, using a prototype implementation on an Arduino board. This shows that FIFO with offset tuning can match the schedulability ratio of CW-EDF, while typically exhibiting lower scheduling overheads and memory consumption than the state-of-the-art Offline Equivalence technique, which is based on Non-Preemptive Fixed Priority (NP-FP) scheduling.

Designing the appointment scheduling is a challenging task for the development of healthcare system. The efficient solution approach can provide high-quality healthcare service between care providers (CP)s and care receivers (CR)s. In this paper, we consider the healthcare system with the heterogeneous CRs in terms of urgent and routine CRs. Our suggested model assumes that the system gives the service priority to the urgent CRs by allowing them to interrupt the ongoing routine appointments. An appointment handoff scheme is suggested for the interrupted routine appointments, and thus the routine CRs can attempt to re-establish the appointment scheduling with other available CPs. With these considerations, we study the scheduling performance of the system by using the Markov chains based modeling approach. The numerical analysis is reported and the simulation experiment is conducted to validate the numerical results.