Recent increases in prescription drug prices have drawn the attention of lawmakers and the media. A 2018 report from the US Department of Health and Human Services indicated that prices of brand drugs increased six times faster than inflation from 2011 to 2015. Even the presence of generics may not reduce prices effectively. Over the last five years, Medicare spending on brand drugs increased by 77% even as usage fell (NBC News, 2018). Several studies also indicate that certain therapeutic classes of drugs are becoming expensive and may result in a long-term financial impact on Medicare Part D. This research considers how coordinated, drug-specific partnerships between independent chain and mail-order pharmacies may be leveraged to reduce drug costs and/or increase supply chain profit. Specifically, we develop an optimization model to determine how many and which of a chain pharmacy's regions should be set up to handle mail-order demand of particular drugs under the objective of maximizing total profit to the partners. Computational results illustrate the value of such partnerships for drug cost reduction and highlight the extent to which a unified effort can generate additional profits for both parties and thus impact pricing for click-and-collect customers.(c) 2021 Elsevier Ltd. All rights reserved.

Customers increasingly expect companies to understand their wants and needs and to market to those desires. Unfortunately, such levels of personalisation can be difficult to accomplish for traditional brick-and-mortar retailers, particularly when the fixed cost of personalised marketing is significant. This paper considers methods for providing customised promotions to customers in the form of weekly flyers. We consider how multiple versions of a weekly flyer can be used by a retailer, which products should be included in each flyer version, and how customer preferences, markup, and inventory considerations impact these decisions. Specifically, transaction histories are used to estimate customer preferences for various product offerings using market basket analysis. These probabilities are then incorporated into an optimisation model for grouping customers into market segments and presenting option sets within each segment that maximise expected marginal profits across the flyers. A heuristic is proposed for solving larger problems and evaluated against lower and upper bounds on the optimal profit. Computational results indicate that leveraging customer transaction data to optimise the product selection and assignment of four unique flyers can increase profit by 7.7% over the optimal single-flyer solution.

Hospitals continue to face the challenge of providing high-quality patient care in an environment of rising healthcare costs. In response, a great deal of attention has been given to advance planning decisions such as nurse staffing, bed mix, scheduling, and patient flow. However, less attention has been given to incorporating quick-response methods in the nurse scheduling process by both anticipating and responding to patient demand fluctuations. Therefore, in this paper, we present a model that incorporates two classes of quick-response decisions in hospitals’ nurse scheduling: (i) adjustments to the unit assignments of cross-trained float nurses and (ii) transfers of patients between units and off-unit admissions. Analyzing three hospitals that are subject to different regulations with respect to patient-to-nurse ratios allows us to draw conclusions on how these hotly debated ratios impact hospital performance, nurse workload, and patient experience. We find that quick-response via cross-trained nurses may lead to higher total costs in settings where an upper limit on patient-to-nurse ratios is enforced. This result has significant managerial and political relevance in locations such as California. Another takeaway is that only a small number of patient transfers or off-unit admissions provides close to the full potential benefit, thus minimizing the negative impact on patient satisfaction and quality of care. Moreover, our proposed scheduling approach reduces the number of undesired assigned shifts. Finally, bed and nurse capacity utilization are shown to be important considerations when determining how and whether to use quick-response methods.

In this paper, we review a subset of the labor scheduling literature and discuss areas where additional research is warranted. The review, while not exhaustive, concludes that there are still rich research opportunities to contribute to an already large field. The areas that are still most attractive are those which provide models that solve more than one phase of the workforce management problem in an integrative fashion as has been called for by many researchers. We also present an example of an integrative model found in the literature. In addition, an application area for workforce management that has received much attention but still provides promising research opportunities is the area of nurse staffing. With continued nursing shortages, managers are faced with a difficult task of providing quality care while still maintaining costs.

The Internet and technology have changed how products are sold and delivered to consumers. Today, the most significant growth in online retailing comes from multichannel retailers that sell products both in stores and over the Internet. Recently, these retail/e-tail organizations have attempted to leverage their "brick" locations by allowing customers to pick up or return orders purchased online at retail store locations. Such options let online customers avoid both long carrier lead times and high shipping costs. However, these options come at a cost to the retailer. This paper develops a mathematical model for analytically examining the cost and value of providing in-store pickup and return options in multi-echelon retail/e-tail organizations. In this light, the model determines the optimal subset of a retailer/e-tailer's stores that should be set up to handle in-store pickups and online returns under stochastic channel demands. Computational results show that optimizing the set of pickup and return locations can reduce system cost by up to 20% on average over arbitrarily enabling all stores with Internet pickup/return capabilities, and firms can substantially increase customer value while maintaining cost minimization as an important selection criterion in choosing pickup and return locations.

Currently, retail data are both accessible and plentiful while the retail space has become increasingly competitive. When combined with technology like mobile computing and low cost analytic techniques, data can now be leveraged by companies to dynamically offer individualised promotions in real time. This paper considers the relative value of three retail information elements which can be used by retailers to dynamically identify a subset of product offerings to promote to their customers. The retail information elements considered are: (a) product markup, (b) customer preference estimates gleaned from purchase history and (c) retailer inventory positions. The importance of each element is evaluated singularly and in combination as is their effect on promotion success, inventory costs and average markup. Computational results show that, on average, dynamic promotion policies incorporating all retail information elements can increase expected profit by 14.5% over policies that consider only customer preference and by 8.4-9.1% over policies that consider only product margin or inventory. Results demonstrate that customer preference information alone does little to improve performance but provides substantial synergistic benefits when combined with either inventory or markup information elements. The most information intensive dynamic promotion policy is then extended to include price as a decision variable.

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.

Nursing managers are faced with rising turnover and shortages of qualified nursing staff. At the same time they are under increased pressure to simultaneously increase patient care and satisfaction while reducing costs. In this study, we examine the impact of centralizing scheduling decisions across departments in a hospital. By pooling nurses from multiple units and scheduling them in one model, improved costs and reduced overtime result. Reduced overtime improves schedules for nurses. Improved satisfaction levels can positively impact turnover rates among nurses. Our results show that by using a centralized model, nursing managers in hospitals can improve the desirability of nurse schedules by approximately 34% and reduce overtime by approximately 80% while simultaneously reducing costs by just under 11%.

Each year, Eli Lilly and Company Lilly offers its worldwide employees the opportunity to participate in paid volunteer teams serving communities in impoverished countries. The company’s Connecting Hearts Abroad service program gives employees a unique opportunity to take part in service trips aimed at improving global health. Lilly annually offers about 23 trips, enabling employees to serve some of the world’s most resource-constrained regions where people lack basic resources or access to healthcare. A selection committee at Lilly manually forms volunteer teams from a large pool of applicants. Unfortunately, the manual selection process is time consuming and often fails to meet employee preference or adequately represent some applicant groups. This paper describes how we developed a mathematical programming model to improve Lilly’s process of volunteer selection. We incorporated the model into a decision support tool that assigns applicants to volunteer assignments and maximizes the chosen volunteers’ preferences under constraints that help ensure fair team compositions. Running the model against the prior year’s applicant data pool took less than two minutes to configure teams such that all volunteers received their first-choice assignment. The automated decision support system also provides a more consistent method of configuring teams that appears fair to the applicants.

A major development in online retailing is the significant increase in the number of traditional "offline" retailers extending their brands online. Many of these retail/e-tail firms are attempting to leverage channel synergies by allowing customers to purchase products over the internet and then pick their orders up at one of the firm's local stores. This paper proposes that the firm presents only a subset of its stores to online customers as available pickup locations, rather than simply listing all local stores with inventory. By doing so, the firm can protect stores with critically low inventory levels and thereby reduce backorder costs. Specifically, we develop and evaluate a dynamic pickup site inclusion policy that incorporates real-time information to specify which of the firm's e-fulfillment locations should be presented at online checkout. Computational results indicate that managing in-store demand via such policies can decrease total cost (holding, backorder, and lost or redirected pickup sale costs) by as much as 18% over allowing customers to pick online orders up from any site with available inventory. The percentage of pickup sales and customers' sensitivity to travel are critical in determining the magnitude of the benefit.

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.

Many retail/e-tail organizations assign responsibilities for online sales immediately and to the closest fulfillment location that has available stock. Unfortunately there is little research on the value of using such policies in retail/e-tail companies. To fill this gap, this paper examines two aspects of the online fulfillment assignment decision that differ from current practice. We propose that online sales should be accumulated before they are assigned to a fulfillment site and that more inventory position information should be leveraged into the fulfillment decision. Specifically, we develop and evaluate a "quasi-dynamic" allocation policy that assigns accumulated online sales to fulfillment locations based on expected inventory, shipping, and customer wait costs. Computational results show that our policy can reduce costs (i.e., holding, backorder, transportation cost) at the fulfillment locations by as much as 23% on average over a commonly used transportation cost policy. In addition, postponing the allocation decision and allowing sales to accumulate can reduce inventory costs at the fulfillment sites by 14% over common practice of instantaneously assigning online sales responsibilities. The magnitude of the benefit depends critically on the number of allocations made each period and the fraction of total sales coming from the online channel. Although postponement delays receipt of online sales, our findings suggest that explicitly incorporating customer service in the allocation decision can improve product availability at little or no additional cost.

This paper investigates cyclical inventory replenishment for a company's regional distribution center that supplies, distributes, and manages inventory of carbon dioxide (CO2) at over 900 separate customer sites in Indiana. The company previously experienced high labor costs with excessive overtime and maintained a regular back-log of customers experiencing stockouts. To address these issues we implemented a three-phase heuristic for the cyclical inventory routing problem encountered at one of the company's distribution centers. This heuristic determines regular routes for each of three available delivery vehicles over a 12-day delivery horizon while improving four primary performance measures: delivery labor cost, stockouts, delivery regularity, and driver–customer familiarity. It does so by first determining three sets of cities (one for each delivery vehicle) that must be delivered to each day based on customer requirements. Second, the heuristic assigns the remaining customers in other cities to one of the three “backbone routes” determined in phase 1. And third, it balances customer deliveries on each daily route over the schedule horizon. Through our methodology, we were able to significantly reduce overtime, driving time, and labor costs while improving customer service.

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.

Operations research has had a long and distinguished history of work in emergency preparedness and response, airline security, transportation of hazardous materials, and threat and vulnerability analysis. Since the attacks of September 11, 2001 and the formation of the US Department of Homeland Security, these topics have been gathered under the broad umbrella of homeland security. In addition, other areas of OR applications in homeland security are evolving, such as border and port security, cyber security, and critical infrastructure protection. The opportunities for operations researchers to contribute to homeland security remain numerous.

Over the last decade the Internet has changed the way that retailers sell and distribute products to consumers. Today the most significant growth in online retailing comes from multi-channel retailers that sell products in stores and over the Internet. Recently these retail/e-tail organizations have attempted to leverage their "bricks" locations by allowing customers to purchase products online and pick the orders up at or return products to retail store locations. Such options let online customers avoid high shipping costs, albeit at a cost to the retailer. This paper models in-store pickups and online returns in a multi- echelon supply chain and develops an analytic expression for the cost of goodwill incurred by companies that provide these options. The analytic cost is compared against simulation and shown to provide good results.