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Now showing 1 - 10 of 297
  • Publication
    Capacity Management in Rental Businesses With Two Customer Bases
    (2005-07-01) Savin, Sergei; Cohen, Morris A; Gans, Noah; Katalan, Ziv
    We consider the allocation of capacity in a system in which rental equipment is accessed by two classes of customers. We formulate the problem as a continuous-time analogue of the one-shot allocation problems found in the more traditional literature on revenue management, and we analyze a queueing control model that approximates its dynamics. Our investigation yields three sets of results. First, we use dynamic programming to characterize properties of optimal capacity allocation policies. We identify conditions under which “complete sharing”—in which both classes of customers have unlimited access to the rental fleet—is optimal. Next, we develop a computationally efficient “aggregate threshold” heuristic that is based on a fluid approximation of the original stochastic model. We obtain closed-form expressions for the heuristic’s control parameters and show that the heuristic performs well in numerical experiments. The closed-form expressions also show that, in the context of the fluid approximation, revenues are concave and increasing in the fleet size. Finally, we consider the effect of the ability to allocate capacity on optimal fleet size. We show that the optimal fleet size under allocation policies may be lower, the same as, or higher than that under complete sharing. As capacity costs increase, allocation policies allow for larger relative fleet sizes. Numerical results show that, even in cases in which dollar profits under complete sharing may be close to those under allocation policies, the capacity reductions enabled by allocation schemes can help to lift profit margins significantly.
  • Publication
    Approximations for the Random Minimal Spanning Tree With Application to Network Provisioning
    (1988) Jain, Anjani; Mamer, John W
    This paper considers the problem of determining the mean and distribution of the length of a minimal spanning tree (MST) on an undirected graph whose arc lengths are independently distributed random variables. We obtain bounds and approximations for the MST length and show that our upper bound is much tighter than the naive bound obtained by computing the MST length of the deterministic graph with the respective means as arc lengths. We analyze the asymptotic properties of our approximations and establish conditions under which our bounds are asymptotically optimal. We apply these results to a network provisioning problem and show that the relative error induced by using our approximations tends to zero as the graph grows large.
  • Publication
    Learning to Collude Tacitly on Production Levels by Oligopolistic Agents
    (2009-02-01) Kimbrough, Steven. O; Murphy, Frederic H
    Classical oligopoly theory has strong analytical foundations but is weak in capturing the operating environment of oligopolists and the available knowledge they have for making decisions, areas in which the management literature is relevant. We use agent-based models to simulate the impact on firm profitability of policies that oligopolists can pursue when setting production levels. We develop an approach to analyzing simulation results that makes use of nonparametric statistical tests, taking advantage of the large amounts of data generated by simulations, and avoiding the assumption of normality that does not necessarily hold. Our results show that in a quantity game, a simple exploration rule, which we call Probe and Adjust, can find either the Cournot equilibrium or the monopoly solution depending on the measure of success chosen by the firms. These results shed light on how tacit collusion can develop within an oligopoly.
  • Publication
    Managing Patient Service in a Diagnostic Medical Facility
    (2006-01-01) Green, Linda V; Savin, Sergei; Wang, Ben
    Hospital diagnostic facilities, such as magnetic resonance imaging centers, typically provide service to several diverse patient groups: outpatients, who are scheduled in advance; inpatients, whose demands are generated randomly during the day; and emergency patients, who must be served as soon as possible. Our analysis focuses on two interrelated tasks: designing the outpatient appointment schedule, and establishing dynamic priority rules for admitting patients into service. We formulate the problem of managing patient demand for diagnostic service as a finite-horizon dynamic program and identify properties of the optimal policies. Using empirical data from a major urban hospital, we conduct numerical studies to develop insights into the sensitivity of the optimal policies to the various cost and probability parameters and to evaluate the performance of several heuristic rules for appointment acceptance and patient scheduling.
  • Publication
    Working With Capacity limitations: Operations Management in Critical Care
    (2011-01-01) Terwiesch, Christian; Kc, Diwas Singh; Kahn, Jeremy M
    As your hospital's ICU director, you are approached by the hospital's administration to help solve ongoing problems with ICU bed availability. The ICU seems to be constantly full, and trauma patients in the emergency department sometimes wait up to 24 hours before receiving a bed. Additionally, the cardiac surgeons were forced to cancel several elective coronary-artery bypass graft cases because there was not a bed available for postoperative recovery. The hospital administrators ask whether you can decrease your ICU length of stay, and wonder whether they should expand the ICU to include more beds For help in understanding and optimizing your ICU's throughput, you seek out the operations management researchers at your university.
  • Publication
    An Algorithm for the Generalized Quadratic Assignment Problem
    (2008-07-01) Hahn, Peter M; Kim, Bum-jin; Monique, Guignard-Spielberg; Smith, John MacGregor; Zhu, Yi-Rong
    This paper reports on a new algorithm for the Generalized Quadratic Assignment problem (GQAP). The GQAP describes a broad class of quadratic integer programming problems, wherein M pair-wise related entities are assigned to N destinations constrained by the destinations’ ability to accommodate them. This new algorithm is based on a Reformulation Linearization Technique (RLT) dual ascent procedure. Experimental results show that the runtime of this algorithm is as good or better than other known exact solution methods for problems as large as M=20 and N=15.
  • Publication
    On Adaptive Emergence of Trust Behavior in the Game of Stag Hunt
    (2002-11-01) Fang, Christina H; Kimbrough, Steven. O; Pace, Stefano; Valluri, Annapurna; Zheng, Zhiqiang
    We study the emergence of trust behavior at both the individual and the population levels. At the individual level, in contrast to prior research that views trust as fixed traits, we model the emergence of trust or cooperation as a result of trial and error learning by a computer algorithm borrowed from the field of artificial intelligence (Watkins 1989). We show that trust can indeed arise as a result of trial and error learning. Emergence of trust at the population level is modeled by a grid-world consisting of cells of individual agents, a technique known as spatialization in evolutionary game theory. We show that, under a wide range of assumptions, trusting individuals tend to take over the population and trust becomes a systematic property. At both individual and population levels, therefore, we argue that trust behaviors will often emerge as a result of learning.
  • Publication
    Cooperative Agent Systems: Artificial Agents Play the Ultimatum Game
    (2002-11-01) Zhong, Fang; Kimbrough, Steven. O; Wu, D.J.
    We explore computational approaches for artificial agents to play the ultimatum game. We compare our agents' behavior with that predicted by classical game theory, as well as behavior found in experimental (or behavioral) economics investigations. In particular, we study the following questions: How do artificial agents perform in playing the ultimatum game against fixed rules, dynamic rules, and rotating rules? How do coevolving artificial agents perform? Will learning software agents do better? What is the value of intelligence? What will happen when smart learning agents play against dumb (no-learning) agents? What will be the impact of agent memory size on performance? This exploratory study provides experimental results pertaining to these questions.
  • Publication
    Constraint-Based Ontology Induction From Online Customer Reviews
    (2007-05-01) Lee, Thomas
    We present an unsupervised, domain-independent technique for inducing a product-specific ontology of product features based upon online customer reviews. We frame ontology induction as a logical assignment problem and solve it with a bounds consistency constrained logic program. Using shallow natural language processing techniques, reviews are parsed into phrase sequences where each phrase refers to a single concept. Traditional document clustering techniques are adapted to collect phrases into initial concepts. We generate a token graph for each initial concept cluster and find a maximal clique to define the corresponding logical set of concept sub-elements. The logic program assigns tokens to clique sub-elements. We apply the technique to several thousand digital camera customer reviews and evaluate the results by comparing them to the ontologies represented by several prominent online buying guides. Because our results are drawn directly from customer comments, differences between our automatically induced product features and those in extant guides may reflect opportunities for better managing customer-producer relationships rather than errors in the process.
  • Publication
    p-Curve and Effect Size Correcting for Publication Bias Using Only Significant Results
    (2014-11-01) Simonsohn, Uri; Nelson, Leif D; Simmons, Joseph P
    Journals tend to publish only statistically significant evidence, creating a scientific record that markedly overstates the size of effects. We provide a new tool that corrects for this bias without requiring access to nonsignificant results. It capitalizes on the fact that the distribution of significant p values, p-curve, is a function of the true underlying effect. Researchers armed only with sample sizes and test results of the published findings can correct for publication bias. We validate the technique with simulations and by reanalyzing data from the Many-Labs Replication project. We demonstrate that p-curve can arrive at conclusions opposite that of existing tools by reanalyzing the meta-analysis of the “choice overload” literature.