Knowledge that Transforms

The journal is pleased to publish the abstracts of the six finalists of the 2014 Manufacturing and Service Operations Management Society’s student paper competition. The 2014 prize committee was chaired by Göker Aydın (Kelley School of Business, Indiana University), Guillaume Roels (UCLA Anderson School of Management, University of California, Los Angeles), and Gilvan Souza (Kelley School of Business, Indiana University). The other committee members were Philipp Afèche, Vishal Agrawal, Gad Allon, Aydın Alptekinoğlu, Alessandro Arlotto, Mor Armony, Atalay Atasu, Opher Baron, Bob Batt, Kostas Bimpikis, Robert Bray, René Caldentey, Carri Chan, Li Chen, Xin Chen, Ying-Ju Chen, Soo-Haeng Cho, So Yeon Chun, Nicole DeHoratius, Sarang Deo, Lingxiu Dong, David Drake, Pnina Feldman, Santiago Gallino, Srinagesh Gavirneni, Karan Girotra, Manu Goyal, Itai Gurvich, Jonathan Helm, Ming Hu, Shanshan Hu, Foad Iravani, Srikanth Jagabathula, Ganesh Janakiraman, Fikri Karaesmen, Diwas KC, Saravanan Kesavan, Sang Kim, Song-Hee Kim, Mirko Kremer, Harish Krishnan, Mümin Kurtuluş, Guoming Lai, Cuihong Li, Jun Li, Ilan Lobel, Ruben Lobel, Lauren Lu, Adam Mersereau, Alex Mills, Toni Moreno, Anton Ovchinnikov, Rodney Parker, Ali Parlaktürk, Alfonso Pedraza-Martinez, Ramandeep Randhawa, Paat Rusmevichientong, Soroush Saghafian, Ozge Sahin, Burhaneddin Sandıkçı, Nicos Savva, Melvyn Sim, Amitabh Sinha, Larry Snyder, Greys Sošić, Brad Staats, Robert Swinney, Alireza Tahbaz-Salehi, Gustavo Vulcano, Owen Wu, Wenqiang Xiao, Nan Yang, Zhibin Yang, Fuqiang Zhang, Jiawei Zhang, Yao Zhao, Karen Zheng, and Leon Zhu. The 2014 prize winners are as follows: Two First Prizes Yonatan Gur, Stanford University “Optimization in Online Content Recommendation Services: Beyond Click-Through Rates” Jónas Oddur Jónasson, London Business School “Improving HIV Early Infant Diagnosis Supply Chains in Sub-Saharan Africa: Models and Application to Mozambique” Finalists Antoine Désir, Columbia University “Sparse Process Flexibility Designs: Is Long Chain Really Optimal?” Karthik Murali, University of Illinois at Urbana–Champaign “Municipal Groundwater Management: Optimal Allocation and Control of a Renewable Natural Resource” Yaron Shaposhnik, Massachusetts Institute of Technology “Scheduling with Testing” Linwei Xin, Georgia Institute of Technology “Optimality Gap of Constant-Order Policies Decays Exponentially in the Lead Time for Lost Sales Models”

We develop a qualitative analysis theory for the convex-cost dynamic multicommodity production planning problem, which can be used, without performing any computational work, to provide invaluable insight to managers when faced with the task of deciding how to respond to changes in problem environment. We first formulate the problem as a multicommodity flow problem with parameters associated with each arc–commodity pair. We then reduce the problem to an equivalent single-commodity flow problem and develop a complete characterization of conformality among production, sales, and inventory activities in various instances of the problem. By combining the conformality characterizations with the monotonicity theory of Granot and Veinott [Granot F, Veinott AF Jr (1985) Substitutes, complements and ripples in network flow. Math. Oper. Res. 10:471–497] for single-commodity problems, we study the effects of changes in problem environment on optimal production, sales, and inventory schedules in the multicommodity problem. Numerous applications are presented and analyzed.

Consider a buyer who would like to procure certain products for the current period and the underlying technologies so that he can become a supplier and compete with current suppliers in the future market. One potential procurement mechanism for such a buyer is to bundle the procurement project with technology acquisition. We propose a dynamic stochastic game-theoretic model that analyzes the optimal technology offer strategies of the asymmetric suppliers and highlights how the size of the current project, relative to the size of the future market, and supplier competition determine the effectiveness of the bundled procurement mechanism for the buyer. For the two-supplier case, we find that each supplier has a dominant technology offer strategy that is independent of the opponent’s strategy. When the relative size of the project is small, suppliers only offer obsolete technologies even if their technologies are perfect substitutes. While suppliers offer better technologies as the project size increases, their responses in technology offers are not continuous with respect to the project size—once the project size reaches some threshold, suppliers’ optimal responses jump to their best technologies. We also observe that the premium needed for technology acquisition under the bundled procurement mechanism can be negligible compared to the expected profit from the future market.

Which vascular access to use is considered one of the most important questions in the care of patients on hemodialysis (HD). An arteriovenous fistula (AVF) is often considered the gold standard for delivering HD due to better patient survival, higher quality of life, and fewer complications. However, AVFs have some limitations: they require surgery, it takes approximately three months to know whether the surgery was successful, and a majority of these surgeries end in failure. Conversely, another common vascular access, the central venous catheter, can be inserted via a simple procedure and used immediately after placement. In this research, we address the question of whether and when to perform AVF surgery on incident and established HD patients, with the aim of finding individualized policies that maximize a patient’s probability of survival and remaining quality-adjusted life expectancy. Using a continuous-time dynamic programming model and under certain data-driven assumptions, we establish structural properties of the optimal policy for each objective. We provide further insights for policy makers through our numerical experiments.

We analyze a model of rental and return process where limited inventory of a product is rented to two customer classes that differ in their return behavior and penalty costs. The rental demand is a decreasing function of time. We consider two cases: where a demand that is not met is lost and where an unmet demand returns. We show that to minimize penalty cost, the optimal allocation policy may give priority to different classes at different points in time and may decline lower-class demand for some time. Computational results show the benefit of the optimal allocation policy over a priority scheme reportedly used in practice.

Store liquidation is the time-constrained divestment of retail outlets through an in-store sale of inventory. The retail industry depends extensively on store liquidation, both to allow managers of going concerns to divest stores in efforts to enhance performance and as a means for investors to recover capital from failed ventures. Retailers sell billions of dollars of inventory annually during store liquidations. This paper introduces the store liquidation problem to the literature and presents a technique for optimizing key store liquidation decisions, including markdowns, inventory transfers, and the timing of store closings. We propose a heuristic for solving the store liquidation problem and evaluate the performance of this method. Through applications, we show that our approach could improve net recovery on cost (i.e., the profit obtained during a liquidation stated as a percentage of the cost value of liquidated inventory) by two to five percentage points in the cases we examined. Further, we discuss ways in which current practice in store liquidation differs from the decisions identified by our method, and we trace the consequences of these differences.

We present a modeling framework for facilities that provide both screening (preventive) and diagnostic (repair) services. The facility operates in a random environment that represents the condition of the population that needs screening and diagnostic services, such as the disease prevalence level. We model the environment as a partially endogenous process: the population’s health can be improved by providing screening services, which reduces future demand for diagnostic services. We use event-based dynamic programming to build a framework for modeling different kinds of these facilities. This framework contains a number of service priority policies that are concerned with prioritizing screening versus diagnostic services. The main trade-off is between serving urgent diagnostic needs and providing screening services that may decrease future diagnostic needs. Under certain conditions, this trade-off reverses the famous cμ rule; i.e., the patients with lower waiting cost are given priority over the others. We define appropriate event operators and specify the properties preserved by these operators. These characterize the structure of optimal policies for all models that can be built within this framework. A numerical study on colonoscopy services illustrates how the framework can be used to gain insights on developing good screening policies.

This paper focuses on salespeople behavior in business-to-business transactions. The paper investigates how salespeople use the information provided to them to set prices; of particular interest is how salespeople use price recommendations from a decision support tool. The investigation builds reduced-form models and tests them on a data set obtained by a grocery products distributor. The analysis shows that salespeople’s decisions are explained well by a two-stage decision model whereby salespeople make an initial decision on whether or not to change the price (a binary decision) and then decide on the magnitude of change (a continuous response). We find that salespeople in our data set do not blindly adopt the recommended price change generated by the pricing tool. Rather, our two-stage model allows for us to uncover a nuanced association between the recommended price and the actual price change by identifying customer-specific and salesperson-specific market factors that moderate the influence of price recommendations.

We consider the problem of selecting the number of advanced life support (ALS) and basic life support (BLS) ambulances—the vehicle mix—to deploy in an emergency medical service (EMS) system, given a budget constraint. ALS ambulances can treat a wider range of emergencies, whereas BLS ambulances are less expensive to operate. To this end, we develop a framework under which the performance of a system operating under a given vehicle mix can be evaluated. Because the choice of vehicle mix affects how ambulances are dispatched to incoming calls, as well as how they are deployed to base locations, we adopt an optimization-based approach. We construct two models—one a Markov decision process, the other an integer program—to study the problems of dispatching and deployment in a tiered system, respectively. In each case, the objective function value attained by an optimal decision serves as our performance measure. Numerical experiments performed with both models suggest that, under reasonable choices of inputs, a wide range of tiered systems perform comparably to all-ALS fleets.

We develop a model to understand and predict customers’ observed multichannel behavior in a customer support setting. Using individual-level data from a U.S.-based health insurance firm, we model a customer’s query frequency and choice of using the telephone or web channel for resolving queries as a stochastic function of her latent “information stock.” The information stock is a function of the customer’s “information needs” (which arise when customers file health insurance claims) and “information gains” (which customers obtain when they resolve their queries through the telephone and web support channels), and other factors such as seasonal effects (for instance, queries that arise at the time of annual contract renewal). We find that average information gain from a telephone call is twice as much as that from visiting the web portal; customers prefer the telephone channel for health event-related information but prefer the web portal for structured seasonal information; and customers are polarized in their propensities of using the web channel and can be broadly classified into “web avoiders” and “web seekers.” Our model provides superior in-sample and out-of-sample fit than multiple benchmark models for aggregate and individual-level customer activity and has several managerial uses, such as capacity planning.