Knowledge that Transforms

A is the tradition at the journal, we are pleased to publish the extended abstracts from the winners of the 2005 MSOM Student Paper Competition. We do this to celebrate the achievements of these young scholars and to provide you with the opportunity to learn about their work in more detail. The 2005 prize committee was chaired by Serguei Netessine (University of Pennsylvania). The other committee members were Dan Adelman (University of Chicago), Philipp Afeche (University of Chicago), Yossi Aviv (Washington University), Fernando Bernstein (Duke University), Rene Caldentey (New York University), Jiri Chod (Boston College), Francis de Vericourt (Duke University), Laurens Debo (Carnegie Mellon University), Vinayak Deshpande (Purdue University), Wedad Elmaghraby (University of Maryland), Jeremie Gallien (Massachusetts Institute of Technology), Noah Gans (University of Pennsylvania), Vishal Gaur (New York University), Roman Kapuscinski (University of Michigan), Costis Maglaras (Columbia University), Ozalp Ozer (Stanford University), Rodney Parker (Yale University), Erica Plambeck (Stanford University), Ioana Popescu (INSEAD), Nils Rudi (INSEAD), Sergei Savin (Columbia University), Alan Scheller-Wolf (Carnegie Mellon University), Nicola Secomandi (Carnegie Mellon University), Xuanming Su (University of California, Berkeley), Julie Swann (Georgia Tech), Beril Toktay (Georgia Tech), Brian Tomlin (University of North Carolina), Tunay Tunca (Stanford University), Senthil Veeraraghavan (University of Pennsylvania), and Assaf Zeevi (Columbia University). The 2005 prize winners are:

Sharing demand forecast information has been recog-nized as a key element in supply-chain coordination (Cachon and Lariviere 2001). Over the last decade, companies have engaged in various forecast-sharing

We consider product and process design problems (hereafter collectively called process design problems) that address issues associated with the assessment of optimum levels for process inputs that influence multiple-process performance measures. While this problem context encompasses many possible applications, we focus primarily on multiple-response design problems that have been widely studied in the quality improvement and quality management literature. For such problems, several optimization criteria have been proposed, including maximization of process yield, maximization of process capability, minimization of process costs, etc. In this research, we propose a method that accounts for many of these criteria via a procedure that interacts with and relies on the preferences of a decision maker (DM). The interactive procedure evolves from the convergence of three areas of research: notably, the research in multiple-response design, the research in multicriteria optimization, and recent developments in global optimization. The proposed interactive method is illustrated and comparatively assessed via two well-known problems in multiple-response design. Although the interactive procedure is developed for application in multiple-response design, it is not limited to this problem context. The concepts and methods developed in this research have applicability to problems that can be characterized by process inputs and process performance, such as supply chain management and multidisciplinary design optimization.

To control a production-inventory system, a manager has to consider the variability in demand as well as variability in her production process. Both types of variability corrupt system performance and by alleviating either of them, the manager can improve the performance of the system. There has been a recent trend towards investing in better information systems to provide better advance demand information. Also, many firms have focused on having safety capacity (e.g., outsourcing or overtime) that they can rely on as needed to protect themselves against uncertainty in demand and production. In this paper, we first address the tactical decision of how a firm decides on production-inventory-safety capacity levels when faced with production and demand uncertainty. We use a multi-period production-inventory model with backordering to fully character-ize the structure of optimal policies. We explore the sensitivity of optimal policies and costs to parameters such as demand and production variability, service level, and utiliza-tion. We also analytically show that uncertainty in capacity may result in nonintuitive behavior, such as more variable capacity resulting in less inventory. Using derived policy structure, through a computational study, we address the strategic decision of investing in better information or creating sources of safety ca-pacity. Our study shows that reductions in costs are significant, with averages up to 30 % for advance demand information, and up to 85 % for outsourcing. Furthermore, conditions that make demand information more valuable tend to make safety capacity less valuable and vice versa and we identify when either will be more valuable. We also show that the benefits from both can exceed the sum of the benefits from either safety capacity or better information.

We consider an assemble-to-order (ATO) system: Components are made to stock by production facilities with finite capacities, and final products are assembled only in response to customers' orders. The key performance measures in this system, such as order fill rates, involve evaluation of multivariate probability distributions, which is computationally demanding if not intractable. The purpose of this paper is to develop computationally efficient performance estimates. We examine several ideas scattered in diverse literatures on approximations for multivariate probability distributions, and determine which approach is most effective in the ATO application. To do so, we first tailor different approximation ideas to the ATO setting to derive performance bounds, and then compare these bounds theoretically and numerically. The bounds also allow us to make connections between capacitated and uncapacitated ATO systems and gain various insights.

The idea of price-directed control is to use an operating policy that exploits optimal dual prices from a mathematical programming relaxation of the underlying control problem. We apply it to the problem of replenishing inventory to subsets of products/locations, such as in the distribution of industrial gases, so as to minimize long-run time average replenishment costs. Given a marginal value for each product/location, whenever there is a stockout the dispatcher compares the total value of each feasible replenishment with its cost, and chooses one that maximizes the surplus. We derive this operating policy using a linear functional approximation to the optimal value function of a semi-Markov decision process on continuous spaces. This approximation also leads to a math program whose optimal dual prices yield values and whose optimal objective value gives a lower bound on system performance. We use duality theory to show that optimal prices satisfy several structural properties and can be interpreted as estimates of lowest achievable marginal costs. On real-world instances, the price-directed policy achieves superior, near optimal performance as compared with other approaches.