Optimization of inpatient hemodialysis scheduling considering efficiency and treatment delays to minimize length of stay

Inpatient dialysis units face an uncertain daily demand of hemodialysis procedures for end-stage renal disease (ESRD) patients hospitalized for health conditions that may or may not be directly related to their renal disease. While hospitalized, these patients must receive hemodialysis in addition to any medical services needed for their primary diagnosis. As a result, when demand for inpatient dialysis is high, treatments and procedures required by these inpatients may be delayed increasing their length of stays (LOS). This research presents an optimization approach for daily scheduling of inpatient hemodialysis to maximize the efficiency of the dialysis unit while minimizing delays of other scheduled procedures that could extend the LOS of the inpatients. The optimization approach takes into account the dialysis protocols prescribed by a treating nephrologist for each dialysis patient, the variable duration of the dialysis treatments, the limited capacity of the dialysis equipment and personnel, as well as the isolation requirements used to mitigate the spread of healthcare-associated infections (HAI). In addition, a variant of the optimization approach is developed that considers uncertainty associated with rescheduling procedures that are delayed and the expected impact on LOS. An experimental performance evaluation illustrates the capability and effectiveness of the proposed scheduling methodologies. The results of this research indicate that the optimization-based scheduling approaches developed in this study could be used on a daily basis by an inpatient dialysis unit to create efficient dialysis schedules

Strategies for dynamic appointment making by container terminals

We consider a container terminal that has to make appointments with barges dynamically, in real-time, and partly automatic. The challenge for the terminal is to make appointments with only limited knowledge about future arriving barges, and in the view of uncertainty and disturbances, such as uncertain arrival and handling times, as well as cancellations and no-shows. We illustrate this problem using an innovative implementation project which is currently running in the Port of Rotterdam. This project aims to align barge rotations and terminal quay schedules by means of a multi-agent system. In this\ud paper, we take the perspective of a single terminal that will participate in this planning system, and focus on the decision making capabilities of its intelligent agent. We focus on the question how the terminal operator can optimize, on an operational level, the utilization of its quay resources, while making reliable appointments with barges, i.e., with a guaranteed departure time. We explore two approaches: (i) an analytical approach based on the value of having certain intervals within the schedule and (ii) an approach based on sources of exibility that are naturally available to the terminal. We use simulation to get insight in the benefits of these approaches. We conclude that a major increase in utilization degree could be achieved only by deploying the sources of exibility, without harming the waiting time of barges too much

A flexible and optimal approach for appointment scheduling in healthcare

Appointment scheduling is generally applied in outpatient clinics and other healthcare services. The challenge in scheduling is to find a strategy for dealing with variability and unpredictability in service duration and patient arrivals. The consequences of an ineffective strategy include long waiting times for patients and idle time for the healthcare provider. In turn, these have implications for the perceived quality, cost-efficiency, and capacity of healthcare services. The generation of optimal schedules is a notoriously intractable problem, and earlier attempts at designing effective strategies for appointment scheduling were based on approximation, simulation, or simplification. We propose a novel strategy for scheduling that exploits three tactical ideas to make the problem manageable. We compare the proposed strategy to other approaches, and show that it matches or outperforms competing methods in terms of flexibility, ease of use, and speed. More importantly, it outperforms competing approaches nearly uniformly in approaching the desired balance between waiting and idle times as specified in a chosen objective function. Therefore, the strategy is a good basis for further enrichments

Three Essays on Data-Driven Optimization for Scheduling in Manufacturing and Healthcare

This dissertation consists of three essays on data-driven optimization for scheduling in manufacturing and healthcare. In Chapter 1, we briefly introduce the optimization problems tackled in these essays. The first of these essays deals with machine scheduling problems. In Chapter 2, we compare the effectiveness of direct positional variables against relative positional variables computationally in a variety of machine scheduling problems and we present our results. The second essay deals with a scheduling problem in healthcare: the team primary care practice. In Chapter 3, we build upon the two-stage stochastic integer programming model introduced by Alvarez Oh (2015) to solve this challenging scheduling problem of determining patient appointment times to minimize a weighted combination of patient wait and provider idle times for the team practice. To overcome the computational complexity associated with solving the problem under the large set of scenarios required to accurately capture uncertainty in this setting, our approach relies on a lower bounding technique based on solving an exhaustive and mutually exclusive group of scenario subsets. Our computational results identify the structure of optimal schedules and quantify the impact of nurse flexibility, patient crossovers and no-shows. We conclude with practical scheduling guidelines for team primary care practices. The third essay deals with another scheduling problem observed in a manufacturing setting similar to first essay, this time in aerospace industry. In Chapter 4, we propose mathematical models to optimize scheduling at a tactical and operational level in a job shop at an aerospace parts manufacturer and implement our methods using real-life data collected from this company. We generalize the Multi-Level Capacitated Lot-Sizing Problem (MLCLSP) from the literature and use novel computational techniques that depend on the data structure observed to reduce the size of the problem and solve realistically-sized instances in this chapter. We also provide a sensitivity analysis of different modeling techniques and objective functions using key performance indicators (KPIs) important for the manufacturer. Chapter 5 proposes extensions of models and techniques that are introduced in Chapters 2, 3 and 4 and outlines future research directions. Chapter 6 summarizes our findings and concludes the dissertation

Scheduling in healthcare with multiple resources

The need for improving efficiency in healthcare is motivated largely by increasing global costs of healthcare. One possibility for improvement is in the optimization of the many schedules found within healthcare. This dissertation focuses on just that for two scheduling problems found within healthcare: the appointment scheduling problem and the master surgery scheduling problem. We first look at the appointment scheduling problem – the problem of assigning time slots to patients booking an appointment at a clinic – examining the various ways in which the randomness of this problem is accounted for, and generalising the problem so that its solutions may be used in a wider range of settings in practice. We consider the application of phase-type distributions as well as simulation and analytical approaches, and we optimize appointment schedules for settings both with multiple healthcare providers, and where patients may arrive in batches rather than one-by-one as is usual. Hereafter, we look at a practical scheduling issue, reporting upon the optimization – via mixed integer linear programming – and subsequent implementation of a surgery schedule for a medium sized hospital in the Netherlands. This problem requires assigning surgical specialties to operate in a given room at a given time during a four-week long repeating schedule; the number of possible combinations of which grows extraordinarily fast, even for a small number of specialties and rooms. In this dissertation, we present the method by which we handled the size of the problem, and pay particular attention to the matter of expectations management throughout the project

A queueing-based approach for integrated routing and appointment scheduling

This paper aims to address the integrated routing and appointment scheduling (RAS) problem for a single service provider. The RAS problem is an operational challenge faced by operators that provide services requiring home attendance, such as grocery delivery, home healthcare, or maintenance services. While considering the inherently random nature of service and travel times, the goal is to minimize a weighted sum of the operator's travel times and idle time, and the client's waiting times. To handle the complex search space of routing and appointment scheduling decisions, we propose a queueing-based approach to effectively deal with the appointment scheduling decisions. We use two well-known approximations from queueing theory: first, we use an approach based on phase-type distributions to accurately approximate the objective function, and second, we use an heavy-traffic approximation to derive an efficient procedure to obtain good appointment schedules. Combining these two approaches results in a fast and sufficiently accurate hybrid approximation, thus essentially reducing RAS to a routing problem. Moreover, we propose the use a simple yet effective large neighborhood search metaheuristic to explore the space of routing decisions. The effectiveness of our proposed methodology is tested on benchmark instances with up to 40 clients, demonstrating an efficient and accurate methodology for integrated routing and appointment scheduling.Comment: 25 pages, 10 figure

Planning Strategies for Home Health Care Delivery

In home health care, continuity of care, wherein a patient is always visited by the same nurse, can be just as important as cost, as it is closely correlated to quality of care. While a patient typically receives care for two to three months, such that assigning a nurse to a patient impacts operations for lengthy periods of time, previous research focusing on continuity of care uses planning horizons that are often a week or shorter. This paper computationally demonstrates that considering a long planning horizon in this setting has significant potential for savings. Initially, a deterministic setting is considered, with all patient requests during the planning horizon known a priori, and the routing cost of planning for two to three months is compared with the cost when planning is done on a weekly basis. With inherent uncertainty in planning for such a long time horizon, a methodology is presented that anticipates future patient requests that are unknown at the time of planning. Computational evidence shows that its use is superior to planning on a weekly basis under uncertainty

A stochastic programming approach for chemotherapy appointment scheduling

Chemotherapy appointment scheduling is a challenging problem due to the uncertainty in pre-medication and infusion durations. In this paper, we formulate a two-stage stochastic mixed integer programming model for the chemotherapy appointment scheduling problem under limited availability and number of nurses and infusion chairs. The objective is to minimize the expected weighted sum of nurse overtime, chair idle time, and patient waiting time. The computational burden to solve real-life instances of this problem to optimality is significantly high, even in the deterministic case. To overcome this burden, we incorporate valid bounds and symmetry breaking constraints. Progressive hedging algorithm is implemented in order to solve the improved formulation heuristically. We enhance the algorithm through a penalty update method, cycle detection and variable fixing mechanisms, and a linear approximation of the objective function. Using numerical experiments based on real data from a major oncology hospital, we compare our solution approach with several scheduling heuristics from the relevant literature, generate managerial insights related to the impact of the number of nurses and chairs on appointment schedules, and estimate the value of stochastic solution to assess the significance of considering uncertainty

A flexible and optimal approach for appointment scheduling in healthcare

Appointment scheduling is generally applied in outpatient clinics and other healthcare services. The challenge in scheduling is to find a strategy for dealing with variability and unpredictability in service duration and patient arrivals. The consequences of an ineffective strategy include long waiting times for patients and idle time for the healthcare provider. In turn, these have implications for the perceived quality, cost-efficiency, and capacity of healthcare services. The generation of optimal schedules is a notoriously intractable problem, and earlier attempts at designing effective strategies for appointment scheduling were based on approximation, simulation, or simplification. We propose a novel strategy for scheduling that exploits three tactical ideas to make the problem manageable. We compare the proposed strategy to other approaches, and show that it matches or outperforms competing methods in terms of flexibility, ease of use, and speed. More importantly, it outperforms competing approaches nearly uniformly in approaching the desired balance between waiting and idle times as specified in a chosen objective function. Therefore, the strategy is a good basis for further enrichments

Managing magnetic resonance imaging machines: support tools for scheduling and planning

We devise models and algorithms to estimate the impact of current and future patient demand for examinations on Magnetic Resonance Imaging (MRI) machines at a hospital radiology department. Our work helps improve scheduling decisions and supports MRI machine personnel and equipment planning decisions. Of particular novelty is our use of scheduling algorithms to compute the competing objectives of maximizing examination throughput and patient-magnet utilization. Using our algorithms retrospectively can help (1) assess prior scheduling decisions, (2) identify potential areas of efficiency improvement and (3) identify difficult examination types. Using a year of patient data and several years of MRI utilization data, we construct a simulation model to forecast MRI machine demand under a variety of scenarios. Under our predicted demand model, the throughput calculated by our algorithms acts as an estimate of the overtime MRI time required, and thus, can be used to help predict the impact of different trends in examination demand and to support MRI machine staffing and equipment planning