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Research problem: High turnover rates in long-term nursing facilities exacerbate the existing shortage of caregivers, a trend that will only worsen as the population of the United States ages. Part-time certified nursing assistants (CNAs) provide a significant amount of patient care in these facilities. CNAs also have high annual rates of turnover, which can harm health outcomes and increase the cost of care. In this study, we empirically analyze the effect of scheduling decisions on part-time CNA turnover. We explore three research questions that examine both how much and with whom a CNA is scheduled to work. We seek to empirically answer how (1) hours worked and (2) coworker variability influence turnover for part-time CNAs and if (3) coworker variability moderates the relationship between hours worked and CNA turnover. Methodology/results: Using novel data from one of the nation's largest nursing home organizations, which includes data for 6,221 part-time CNAs at 157 facilities in the United States over a 26-month period, we identify two scheduling levers managers may be able to use to influence turnover. As hours worked increase, turnover first decreases and then increases, demonstrating a nonlinear U-shaped relationship between hours worked and turnover. We also find that high coworker variability increases turnover while also moderating the effects of hours worked on turnover. In post hoc analyses, we demonstrate that high CNA turnover has negative impacts on patient health. Managerial implications: These findings suggest that managers may be able to leverage part-time CNA scheduling to reduce turnover, improving both the quality and cost of care. Specifically, we demonstrate that managers can reduce CNA turnover by increasing hours worked, scheduling coworkers together consistently and doing both simultaneously. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2021.0533. [ABSTRACT FROM AUTHOR]

The Internet has revolutionized the retailing landscape and how goods and services are sold and distributed to consumers. One avenue of significant growth in online selling comes from multichannel retailers who offer products in stores as well as over the Web. These hybrids may leverage their 'brick' locations by allowing customers to pick up or return orders purchased online at retail stores. This option lets Web-based buyers avoid added shipping costs and long package carrier lead times, albeit at a cost to retailers. To examine the viability of this strategy, we develop a mathematical model that examines the cost and value of providing in-store pickup and return. The model is used to determine the best subset of brick-and-mortar stores to handle in-store pickup and return demand. One of the principal takeaways is that not all retail stores should be offering in-store pickups and/or returns. Our computational results show optimizing the set of pickup and return locations may reduce system cost over baseline marketing policies where these services are set up at all or none of a retailer's stores. In addition, we show that retailers can significantly improve some consumer benefits at little extra cost. Keywords E-tailing * Internet marketing * Clicks and bricks * Optimization models, Orientation Internet marketing continues to account for an increasing portion of retail sales, and one area of significant growth involves multichannel retailers who sell goods and services using brick-and-mortar as [...]

To authenticate to the full-text of this article, please visit this link: http://dx.doi.org/10.1111/j.1540-5915.2010.00269.x Byline: P. Daniel Wright (1[dagger]), Kurt M. Bretthauer (2) Keywords: Coordinated Decision Making; Cross-training; Flexible Workforce Capacity; Health Care Applications; Nursing Shortage Abstract: ABSTRACT In this article, we present strategies to help combat the U.S. nursing shortage. Key considerations include providing an attractive work schedule and work environment-critical issues for retaining existing nurses and attracting new nurses to the profession-while at the same time using the set of available nurses as effectively as possible. Based on these ideas, we develop a model that takes advantage of coordinated decision making when managing a flexible workforce. The model coordinates scheduling, schedule adjustment, and agency nurse decisions across various nurse labor pools, each of differing flexibility levels, capabilities, and costs, allowing a much more desirable schedule to be constructed. Our primary findings regarding coordinated decision making and how it can be used to help address the nursing shortage include (i) labor costs can be reduced substantially because, without coordination, labor costs on average are 16.3% higher based on an actual hospital setting, leading to the availability of additional funds for retaining and attracting nurses, (ii) simultaneous to this reduction in costs, more attractive schedules can be provided to the nurses in terms of less overtime and fewer undesirable shifts, and (iii) the use of agency nurses can help avoid overtime for permanent staff with only a 0.7% increase in staffing costs. In addition, we estimate the cost of the shortage for a typical U.S. hospital from a labor cost perspective and show how that cost can be reduced when managers coordinate. Author Affiliation: (1)Department of Management and Operations, Villanova University, Villanova, PA 19085, e-mail:daniel.wright@villanova.edu (2)Department of Operations and Decision Technologies, Indiana University, Bloomington, IN 47405, e-mail:kbrettha@indiana.edu Article History: [Received: October 2008. Accepted: February 2010.] Article note: ([dagger]) Corresponding author.

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2006.03.043 Byline: Elliot Bendoly (a), Doug Blocher (b), Kurt M. Bretthauer (b), M.A. Venkataramanan (b) Keywords: Internet; Inventory; Supply chain management; Optimization Abstract: Traditional 'Brick-and-Mortar' operations face the challenge of adapting to a new set of competitive rules made necessary by consumers who want the option of ordering electronically via the Internet. To satisfy these customers, firms must develop strategies that integrate their standard retail in-store channel with this relatively new on-line channel. Therefore, this research is designed to provide insights into supply chain inventory management strategies relevant to 'Clicks-and-Mortar' firms trying to satisfy both on-line and in-store sales. Specifically, this work considers the total cost implications of various inventory allocation strategies while maintaining target customer service levels. Analysis focuses on the development of models capable of handling new operating strategies made possible by electronic commerce. The implications of inventory risk pooling are considered in depth, revealing the existence of characteristics that determine whether completely centralized or decentralized policies are preferable. Author Affiliation: (a) Department of Decision and Information Analysis, Goizueta Business School, Emory University, Atlanta, GA 30322, United States (b) Department of Operations and Decision Technologies, Kelley School of Business, Indiana University, Bloomington, IN 47405, United States Article History: Received 10 November 2004; Accepted 14 March 2006

To purchase or authenticate to the full-text of this article, please visit this link: http://dx.doi.org/10.1111/j.1540-5414.2006.00109.x Byline: P. Daniel Wright (1[dagger]), Kurt M. Bretthauer (2), Murray J. Cote (3) Keywords: Health Care Policy; Labor and Staff Planning; Mathematical Programming/Optimization; Nurse Scheduling; and Planning and Scheduling Abstract: ABSTRACT 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. Author Affiliation: (1)Department of Decision and Information Technologies, Villanova University, Villanova, PA 19085, e-mail:daniel.wright@villanova.edu (2)Department of Operations and Decision Technologies, Indiana University, Bloomington, IN 47405, e-mail:kbrettha@indiana.edu (3)Department of Health Services Research, Management and Policy, University of Florida, Gainesville, FL 32610, e-mail:mjcote@phhp.ufl.edu Article History: [Received: September 2005. Accepted: January 2006.] Article note: ([dagger]) Corresponding author.

Service capacity management has been extensively studied and successfully applied in many industries, with an emphasis on tour/shift scheduling and assignment decisions. However, few studies have addressed real-time work schedule adjustment decisions made necessary by demand uncertainty and/or labor supply disruption. This case study deals with the real-time work schedule adjustment decision and investigates the association of managerial experience, workforce mix (full- and part-time staff), and information accuracy with managers' adjustment decisions. We designed an experiment that involved practicing service managers from a McDonald's franchise who provided their adjustment decisions for a given set of test scenarios. Their decisions were analyzed and evaluated via a controlled experiment. Using profitability as the primary performance measure, the study identifies the following outcomes: First, senior managers of a store with a higher proportion of part-time staff made more adjustments and attained slightly higher profitability than junior managers, when all employees accepted adjusted schedules. Second, managers of a store with a higher proportion of part-time staff were able to make slightly higher profits, particularly when capacity shortages occurred. And third, to achieve the majority of the benefits from schedule adjustments, it is sufficient to search for information that correctly identifies the direction of demand changes, rather than identifying the exact magnitude of the changes. Keywords: Service operations; Case study; Labor scheduling; Schedule adjustment decision, 1. Introduction Workforce scheduling is critical to the success of many service organizations because of its direct impact on customer service, costs, and profitability. For the past 3 decades, service [...]

An optimization/queueing network model was created to effectively plan resource requirements in health care organizations. The model, which was implemented in a blood bank and in a health maintenance organization, was able to reduce capacity expenditures while regulating customer service through imposition of performance constraints. It was also able to illustrate the stochastic behavior of health care systems and to assess customer service levels contained in the optimization framework.

Nonlinear programming methods for multiperiod capacity planning in a manufacturing system comprised of a set of machines or workstations producing multiple products are introduced. The manufacturing facility is an open network of queues where capacity at each work station may be changed in each time period. Two nonlinear programming approaches for determining the timing and size of capacity changes are proposed. Under one model, total capacity costs are reduced so that plant congestion is controlled via upper limits on work-in-process. In another, a weighted sum of product lead times is minimized according to budget limits on capacity costs.

A branch and bound algorithm for planning capacity in manufacturing and computer networks is proposed. The algorithm globally minimizes a concave cost function over a single convex linear performance constraint and lower/upper bounds on discrete capacity variables. Reoptimization procedures are used to solve the subproblems more efficiently. Computational tests on problems with up to 100 variables indicate the efficacy of the algorithm for capacity planning.

A general model for multi-item production and inventory management problems that include a resource limitation is introduced. The model's decision variables normally represent cycle times, production batch sizes, number of production runs or order quantities per item. Its objective function includes reciprocal, linear, and constant terms which often arise from setup costs, inventory holding costs and purchase costs.

Hospital readmissions present an increasingly important challenge for health-care organizations. Readmissions are expensive and often unnecessary, putting patients at risk and costing $15 billion annually in the United States alone. Currently, 17% of Medicare patients are readmitted to a hospital within 30 days of initial discharge with readmissions typically being more expensive than the original visit to the hospital. Recent legislation penalizes organizations with a high readmission rate. The medical literature conjectures that many readmissions can be avoided or mitigated by post-discharge monitoring. To develop a good monitoring plan it is critical to anticipate the timing of a potential readmission and to effectively monitor the patient for readmission causing conditions based on that knowledge. This research develops new methods to empirically generate an individualized estimate of the time to readmission density function and then uses this density to optimize a post-discharge monitoring schedule and staffing plan to support monitoring needs. Our approach integrates classical prediction models with machine learning and transfer learning to develop an empirical density that is personalized to each patient. We then transform an intractable monitoring plan optimization with stochastic discharges and health state evolution based on delay-time models into a weakly coupled network flow model with tractable subproblems after applying a new pruning method that leverages the problem structure. Using this multi-methodologic approach on two large inpatient datasets, we show that optimal readmission prediction and monitoring plans can identify and mitigate 40-70% of readmissions before they generate an emergency readmission. [ABSTRACT FROM AUTHOR]