Time‐Staged Overtime Staffing for Services with Updated Forecasts and Availabilities

This article develops a framework for staffing in a service environment when multiple opportunities exist for prescheduling overtime prior to the start of a shift. Demand forecasts improve as the shift approaches, while the availability of workers to be scheduled for overtime decreases. First, a single‐shift model is developed and used in computational studies to evaluate the benefits of time‐staged overtime staffing, which include slightly lower costs and significant reductions in unscheduled overtime and outside agents. A multishift model is then developed to consider constraints on consecutive hours worked and minimum rest intervals between shifts. A multishift computational study shows how the benefits of time‐staged overtime staffing depend on problem characteristics when interactions between shifts are considered. The article discusses how single‐shift and multishift models relate to each other and alternative ways the models may be used in practice, including decentralized open shift management and centralized overtime scheduling.     

A Model for Planning Resource Requirements in Health Care Organizations

In this paper we present a general model and solution methodology for planning resource requirements (i.e., capacity) in health care organizations. To illustrate the general model, we consider two specific applications: a blood bank and a health maintenance organization (HMO). The blood bank capacity planning problem involves determining the number of donor beds required and determining the size of the nursing and support staff necessary. Capacity must be sufficient to handle the expected number of blood donors without causing excessive donor waiting times. Similar staff, equipment, and service level decisions arise in the HMO capacity planning problem. To determine resource requirements, we develop an optimization/queueing network model that minimizes capacity costs while controlling customer service by enforcing a set of performance constraints, such as setting an upper limit on the expected time a patient spends in the system. The queueing network model allows us to capture the stochastic behavior of health care systems and to measure customer service levels within the optimization framework.     

Clinic Overbooking to Improve Patient Access and Increase Provider Productivity*

The problem of patient no‐shows (patients who do not arrive for scheduled appointments) is significant in many health care settings, where no‐show rates can vary widely. No‐shows reduce provider productivity and clinic efficiency, increase health care costs, and limit the ability of a clinic to serve its client population by reducing its effective capacity. In this article, we examine the problem of no‐shows and propose appointment overbooking as one means of reducing the negative impact of no‐shows. We find that patient access and provider productivity are significantly improved with overbooking, but that overbooking causes increases in both patient wait times and provider overtime. We develop a new clinic utility function to capture the trade‐offs between these benefits and costs, and we show that the relative values that a clinic assigns to serving additional patients, minimizing patient waiting times, and minimizing clinic overtime will determine whether overbooking is warranted. From the results of a series of simulation experiments, we determine that overbooking provides greater utility when clinics serve larger numbers of patients, no‐show rates are higher, and service variability is lower. Even with highly variable service times, many clinics will achieve positive net results with overbooking. Our analysis provides valuable guidance to clinic administrators about the use of appointment overbooking to improve patient access, provider productivity, and overall clinic performance.     

Reexamining the Nurse Scheduling Problem: Staffing Ratios and Nursing Shortages*

Legislators at the state and national levels are addressing renewed concerns over the adequacy of hospital nurse staffing to provide quality care and ensure patient safety. At the same time, the well‐known nursing shortage remains an ongoing problem. To address these issues, we reexamine the nurse scheduling problem and consider how recent health care legislation impacts nursing workforce management decisions. Specifically, we develop a scheduling model and perform computational experiments to evaluate how mandatory nurse‐to‐patient ratios and other policies impact schedule cost and schedule desirability (from the nurses' perspective). Our primary findings include the following: (i) nurse wage costs can be highly nonlinear with respect to changes in mandatory nurse‐to‐patient ratios of the type being considered by legislators; (ii) the number of undesirable shifts can be substantially reduced without incurring additional wage cost; (iii) more desirable scheduling policies, such as assigning fewer weekends to each nurse, have only a small impact on wage cost; and (iv) complex policy statements involving both single‐period and multiperiod service levels can sometimes be relaxed while still obtaining good schedules that satisfy the nurse‐to‐patient ratio requirements. The findings in this article suggest that new directions for future nurse scheduling models, as it is likely that nurse‐to‐patient ratios and nursing shortages will remain a challenge for health care organizations for some time.     

A New Multiperiod Multiple Traveling Salesman Problem with Heuristic and Application to a Scheduling Problem

A new multiperiod variation of the M-traveling salesman problem is introduced. The problem arises in efficient scheduling of optimal interviews among tour brokers and vendors at conventions of the tourism and travel industry. In classical traveling salesman problem vocabulary, a salesman is a tour broker at the convention and a city is a vendor's booth. In this problem, more than one salesman may be required to visit a city, but at most one salesman per time period can visit each city. The heuristic solution method presented is polynomial and is guaranteed to produce a nonconflicting set of salesmen's tours. The results of an implementation of the method for a recent convention are also reported.     

A Multiperiod Audit Staff Planning Model Using Multiple Objectives: Development and Evaluation*

The audit staff planning problem, a specific type of manpower planning problem, has been modeled using goal programming and, more recently, multiple objective linear programming. Prior studies developed single-period models and did not go beyond the model building stage. This study develops a multiperiod audit staff planning model and evaluates the model using a test application involving actual decision makers (partners in public accounting firms). The multiperiod model includes seven objectives to be optimized: profit (to be maximized), late completion of work, work declined, staff augmentation, staff reduction, underutilization of the work force, and shortfall in meeting professional development targets (all to be minimized). Over a four-quarter planning horizon with one “busy season,” the model is subject to constraints with respect to the projected audit work load, ability to substitute personnel and to perform interim audit work, available staff hours (including overtime limitations), supervisory requirements, and professional development targets. Results of the test application showed that the model was capable of producing a range of values for each objective. The participants were exposed to much of that range when making their decisions. The results also showed that all objectives were important and that participants were consistent in choosing their preferred level of each objective over several runs of the model. These results and the reactions of the participants demonstrate that the model is usable by actual decision makers and has potential for a number of specific applications.     

Schedule Recovery: Unplanned Absences in Service Operations*

The U.S. service sector loses 2.3% of all scheduled labor hours to unplanned absences, but in some industries, the total cost of unplanned absences approaches 20% of payroll expense. The principal reasons for unscheduled absences (personal illness and family issues) are unlikely to abate anytime soon. Despite this, most labor scheduling systems continue to assume perfect attendance. This oversight masks an important but rarely addressed issue in services management: how to recover from short‐notice, short‐term reductions in planned capacity. In this article, we model optimal responses to unplanned employee absences in multi‐server queueing systems that provide discrete, pay‐per‐use services for impatient customers. Our goal is to assess the performance of alternate absence recovery strategies under various staffing and scheduling regimes. We accomplish this by first developing optimal labor schedules for hypothetical service environments with unreliable workers. We then simulate unplanned employee absences, apply an absence recovery model, and compute system profits. Our absence recovery model utilizes recovery strategies such as holdover overtime, call‐ins, and temporary workers. We find that holdover overtime is an effective absence recovery strategy provided sufficient reserve capacity (maximum allowable work hours minus scheduled hours) exists. Otherwise, less precise and more costly absence recovery methods such as call‐ins and temporary help service workers may be needed. We also find that choices for initial staffing and scheduling policies, such as planned overtime and absence anticipation, significantly influence the likelihood of successful absence recovery. To predict the effectiveness of absence recovery policies under alternate staffing/scheduling strategies and operating environments, we propose an index based on initial capacity reserves.     

Strategic Purchases of Bundled Products in a Health Care Supply Chain Environment*

Recent articles in the Wall Street Journal summarize the state of business practice in American hospitals by shedding light on the state of supply chain management practices and foci in today's health care supply chains. In health care, the single largest cost after labor is materials, and it has been documented that health care facilities can reduce the environmental impacts of the products and services they consume before regulatory problems arise or waste disposal costs increase by focusing on their upstream activities. Health care systems around the country consume significant quantities and varieties of products within the health delivery processes. Solving these environmental problems requires a much broader view involving collaborative efforts of professionals from different areas of health care to meet these challenges. The purchasing function bridges the gaps by providing a healthy dialogue on key environmental attributes within the health care supply chain. The concept of bundling new with refurbished products is gaining a lot of attention in the health care supply chain. This research describes a health care purchasing problem for bundling new and refurbished products of the type facing a growing number of large health care providers, and then proposes a methodology for evaluating the complex tradeoffs involved in bundling decisions for refurbished health care products. By exploiting some useful properties of the problem structure, our results provide buyers with useful insights for examining and selecting suppliers who are willing to offer bundles of new and refurbished products.     

Tour Scheduling and Task Assignment of a Heterogeneous Work Force: A Heuristic Approach

The dual problem of work tour scheduling and task assignment involving workers who differ in their times of availability and task qualifications is examined in this paper. The problem is presented in the context of a fast food restaurant, but applies equally well to a diverse set of service operations. Developing a week-long labor schedule is a nontrivial problem, in terms of complexity and importance, which a manager spends as much as a full workday solving. The primary scheduling objective (the manager's concern) is the minimization of overstaffing in the face of significant hourly and daily fluctuations in minimum staffing requirements. The secondary objective (the workers’ concern) is the minimization of the sum of the squared differences between the number of work hours scheduled and the number targeted for each employee. Contributing to scheduling complexity are constraints on the structure of work tours, including minimum and maximum shift lengths and a maximum number of workdays. A goal programming formulation of a representative problem is shown to be too large, for all practical purposes, to be solved optimally. Existing heuristic procedures related to this research possess inherent limitations which render them inadequate for our purposes. Subsequently, we propose and demonstrate a computerized heuristic procedure capable of producing a labor schedule requiring at most minor refinement by a manager.     

Sequential and Simultaneous Decision Making for Optimizing Health Care Resource Flexibilities

Health care administrators commonly employ two types of resource flexibilities (demand upgrades and staffing flexibility) to efficiently coordinate two critical internal resources, nursing staff and beds, and an external resource (contract nurses) to satisfy stochastic patient demand. Under demand upgrades, when beds are unavailable for patients in a less acute unit, patients are upgraded to a more acute unit if space is available in that unit. Under staffing flexibility, nurses cross‐trained to work in more than one unit are used in addition to dedicated and contract nurses. Resource decisions (beds and staffing) can be made at a single point in time (simultaneous decision making) or at different points in time (sequential decision making). In this article, we address the following questions: for each flexibility configuration, under sequential and simultaneous decision making, what is the optimal resource level required to meet stochastic demand at minimum cost? Is one type of flexibility (e.g., demand upgrades) better than the other type of flexibility (e.g., staffing flexibility)? We use two‐stage stochastic programming to find optimal resource levels for two nonhomogeneous hospital units that face stochastic demand following a continuous, general distribution. We conduct a full‐factorial numerical experiment and find that the benefit of using staffing flexibility on average is greater than the benefit of using demand upgrades. However, the two types of flexibilities have a positive interaction effect and they complement each other. The type of flexibility and decision timing has an independent effect on system performance (capacity and staffing costs). The benefits of cross‐training can be largely realized even if beds and staffing levels have been determined prior to the establishment of a cross‐training initiative.     

Every Minute Counts: Using Process Improvement and Performance Feedback to Improve Patient Flow in an Emergency Department

Prolonged emergency department (ED) visits are associated with a number of adverse outcomes for patients as well as lower patient satisfaction scores and increased costs. Several factors that influence the length of ED visits are out of the control of hospital employees, but some opportunities exist to improve performance. For this study, the ED department of a 150-bed hospital in the southeastern United States wanted to improve door-to-discharge time. To do so, a subprocess of door-to-discharge time was targeted, door-to-order. After analyzing the process, the team created standard orders for the 10 most common presenting conditions in the ER with preapproval, allowing nurses to submit the orders without the provider first visiting the patient. Following the process change, daily feedback was added to increase utilization of the preapproved orders. Reductions in door-to-order times and door-to-discharge were observed and patient satisfaction remained stable. Implications for future research in this area are discussed.     

Earliness/Tardiness Production Scheduling and Planning,and solutions

Earliness/Tardiness Production Planning (ETPSP) has attracted much attention in recent years. In practice, it provides an efficient way to integrate Manufacturing Resource Planning (MRP-II) Scheduling and with Just-In Time (JIT). Conventional research mainly focused on the condition of single-machine and parallel multi-machine. Capacity balance has not been considered. Some investigations of existing research on ETPSP are given in this paper. An extensive model of ETPSP with consideration of multi-process capacity balance for manufacturing systems is proposed. Two alternative solutions, key-process method and relaxation method, are both developed to solve ETPSP. Computational results identify that both methods are powerful to solve ETPSP which minimizes total earliness and tardiness penalties. Furthermore, some comparisons between these two methods and other conventional methods indicate the efficiency of the former.     

The Role of Clinical and Process Quality in Achieving Patient Satisfaction in Hospitals

Managers constantly struggle with where to allocate their resources and efforts in managing the complex service delivery system called a hospital. In the broadest sense, their decisions and actions focus on two important aspects of health care—clinical or technical medical care that emphasizes “what” the patient receives and process performance that emphasizes “how” health care services are delivered to patients. Here, we investigate the role of leadership, clinical quality, and process quality on patient satisfaction. A causal model is hypothesized and evaluated using structural equation modeling for a sample of 202 U.S. hospitals. Statistical results support the idea that leadership is a good exogenous construct and that clinical and process quality are good intermediate outcomes in determining patient satisfaction. Statistical results also suggest that hospital leadership has more influence on process quality than on clinical quality, which is predominantly the doctors' domain. Other results are discussed, such as that hospital managers must be mindful of the fact that process quality is at least as important as clinical quality in predicting patient satisfaction. The article concludes by proposing areas for future research.     

Appointment Overbooking in Health Care Clinics to Improve Patient Service and Clinic Performance

The problem of no‐shows (patients who do not arrive for scheduled appointments) is particularly significant for health care clinics, with reported no‐show rates varying widely from 3% to 80%. No‐shows reduce revenues and provider productivity, increase costs, and limit patient access by reducing effective clinic capacity. In this article, we construct a flexible appointment scheduling model to mitigate the detrimental effects of patient no‐shows, and develop a fast and effective solution procedure that constructs near‐optimal overbooked appointment schedules that balance the benefits of serving additional patients with the potential costs of patient waiting and clinic overtime. Computational results demonstrate the efficacy of our model and solution procedure, and connect our work to prior research in health care appointment scheduling.     

Health Care Supply Chain Design: Toward Linking the Development and Delivery of Care Globally*

This article is motivated by the gap between the growing demand and available supply of high‐quality, cost‐effective, and timely health care, a problem faced not only by developing and underdeveloped countries but also by developed countries. The significance of this problem is heightened when the economy is in recession. In an attempt to address the problem, in this article, first, we conceptualize care as a bundle of goods, services, and experiences—including diet and exercise, drugs, devices, invasive procedures, new biologics, travel and lodging, and payment and reimbursement. We then adopt a macro, end‐to‐end, supply chain–centric view of the health care sector to link the development of care with the delivery of care. This macro, supply chain–centric view sheds light on the interdependencies between key industries from the upstream to the downstream of the health care supply chain. We propose a framework, the 3A‐framework, that is founded on three constructs—affordability, access, and awareness—to inform the design of supply chain for the health care sector. We present an illustrative example of the framework toward designing the supply chain for implantable device–based care for cardiovascular diseases in developing countries. Specifically, the framework provides a lens for identifying an integrated system of continuous improvement and innovation initiatives relevant to bridging the gap between the demand and supply for high‐quality, cost‐effective, and timely care. Finally, we delineate directions of future research that are anchored in and follow from the developments documented in the article.     

Barriers to Swift,Even Flow in the Internal Supply Chain of Perioperative Surgical Services Department: A Case Study*

This research analyzes the internal service supply chain of a perioperative surgical services department of a nonacademic community hospital using the theory of swift and even flow as a theoretical lens. Performance data were gathered using an in‐depth case study and it was determined that the swift and even flow constructs did affect flow speed and variance, but did not account for all the effects observed. To understand the need for departments to coordinate their efforts with each other and how this affected flow, further analysis was conducted using insights from Pagell's (2004) theory of internal integration and Gittell's (2002) research about coordinating mechanisms. The findings suggest the need to incorporate a variable measuring supply chain coordination into the theory of swift and even flow.     

Modeling Pathology Workload and Complexity to Manage Risks and Improve Patient Quality and Safety

Anatomic pathology (AP) laboratories provide critical diagnostic information that help determine patient treatments and outcomes, but the risks of AP operations and their impact on patient safety and quality of care remain poorly recognized and undermanaged. Hospital-based laboratories face an operational and risk management challenge because clinical work of unknown quantity and complexity arrives with little advance notice, which results in fluctuations in workload that can push operations beyond planned capacity, leading to diagnostic delays and potential errors. Modeling the dynamics of workload and complexity in AP offers the opportunity to better use available information to manage risks. We developed a stock-and-flow model of a typical AP laboratory operation and identified key exogenous inputs that drive AP work. To test the model, we generated training and validations data sets by combining data from the electronic medical records and laboratory information systems over multiple years. We demonstrate the implementation of 10-day AP work forecast generated on a daily basis, and show its performance in comparison with actual work. Although the model somewhat underpredicts work as currently implemented, it provides a framework for prospective management of resources to ensure quality during workload surges. Although full implementation requires additional model development, we show that AP workload largely depends on few and accessible clinical inputs. Recognizing that level loading of work in a hospital is not practical, predictive modeling of work can empower laboratories to triage, schedule, or mobilize resources more effectively and better manage risks that reduce the quality or timeliness of diagnostic information.     

Designing Referral Network Structures and Decision Rules to Streamline Provision of Urgent Health and Human Services*

This study uses fully factorial computer simulation to identify referral network attributes and referral decision rules that streamline the routing of people to urgent, limited services. As an example of a scenario, the model represents vaccine delivery in a city of 100,000 people during the first 30 days of a pandemic. By modeling patterns of communication among health care providers and daily routing of overflow clients to affiliated organizations, the simulations determine cumulative effects of referral network designs and decision rules on citywide delivery of available vaccines. Referral networks generally improve delivery rates when compared with random local search by clients. Increasing the health care organizations’ tendencies to form referral partnerships from zero to about four partners per organization sharply increases vaccine delivery under most conditions, but further increases in partnering yield little or no gain in system performance. When making referrals, probabilistic selection among partner organizations that have any capacity to deliver vaccines is more effective than selection of the highest‐capacity partner, except when tendencies to form partnerships are very low. Implications for designing health and human service referral networks and helping practitioners optimize their use of the networks are discussed. Suggestions for using simulations to model comparable systems are provided.