Operations Research

最新文章

• On Hiring Secretaries with Stochastic Departures AI翻译
  作者：Kesselheim, Thomas; Psomas, Alexandros; Vardi, Shai
  作者单位：University of Bonn; Purdue University System; Purdue University; Purdue University System; Purdue University
  摘要：We study a generalization of the secretary problem, where decisions do not have to be made immediately upon applicants' arrivals. After arriving, each applicant stays in the system for some (random) amount of time and then leaves, whereupon the algorithm has to decide irrevocably whether to select this applicant or not. The arrival and waiting times are drawn from known distributions, and the decision maker's goal is to maximize the probabil-ity of selecting the best applicant overall. Our fir...
• Advances in MINLP to Identify Energy-Efficient Distillation Configurations AI翻译
  作者：Gooty, Radhakrishna Tumbalam; Agrawal, Rakesh; Tawarmalani, Mohit
  作者单位：Purdue University System; Purdue University; Purdue University System; Purdue University
  摘要：In this paper, we describe the first mixed-integer nonlinear programming (MINLP)-based solution approach that successfully identifies the most energy-efficient distillation configuration sequence for a given separation. Current sequence design strategies are largely heuristic. The rigorous approach presented here can help reduce the significant energy consumption and consequent greenhouse gas emissions by separation processes. First, we model discrete choices using a formulation that is provab...
• Shortest-Job-First Scheduling in Many-Server Queues with Impatient Customers and Noisy Service-Time Estimates AI翻译
  作者：Dong, Jing; Ibrahim, Rouba
  作者单位：Columbia University; University of London; University College London
  摘要：Size-based scheduling has been extensively studied yet almost exclusively in single-server queues with infinitely patient jobs and perfectly known service times. Much less is known about its performance in many-server queues, particularly under noisy service-time information. In this paper, we derive theoretical results that quantify the performance of the nonpreemptive shortest-job-first (SJF) policy in many-server queues with abandonment and noisy service-time estimates. In particular, we co...
• Online Learning for Constrained Assortment Optimization Under Markov Chain Choice Model AI翻译
  作者：Li, Shukai; Luo, Qi; Huang, Zhiyuan; Shi, Cong
  作者单位：Northwestern University; University of Iowa; Tongji University; University of Miami
  摘要：We study a dynamic assortment selection problem where arriving customers make purchase decisions among offered products from a universe of products under a Markov chain choice (MCC) model. The retailer only observes the assortment and the customer's single choice per period. Given limited display capacity, resource constraints, and no a priori knowledge of problem parameters, the retailer's objective is to sequentially learn the choice model and optimize cumulative revenues over a finite selli...
• Reshaping National Organ Allocation Policy AI翻译
  作者：Papalexopoulos, Theodore; Alcorn, James; Bertsimas, Dimitris; Goff, Rebecca; Stewart, Darren; Trichakis, Nikolaos
  作者单位：Massachusetts Institute of Technology (MIT); United Network for Organ Sharing; New York University
  摘要：The Organ Procurement & Transplantation Network (OPTN) initiated in 2018 a major overhaul of all U.S. deceased-donor organ allocation policies, aiming to gradually migrate them to a so-called continuous distribution model, with the goal of creating an allocation system that is more efficient, more equitable, and more inclusive. Development of policies within this model, however, represents a major challenge because multiple efficiency and fairness objectives need to be delicately balanced. We ...
• Proximal Reinforcement Learning: Efficient Off-Policy Evaluation in Partially Observed Markov Decision Processes AI翻译
  作者：Bennett, Andrew; Kallus, Nathan
  作者单位：Cornell University
  摘要：In applications of offline reinforcement learning to observational data, such as in healthcare or education, a general concern is that observed actions might be affected by unobserved factors, inducing confounding and biasing estimates derived under the assumption of a perfect Markov decision process (MDP) model. Here we tackle this by considering off-policy evaluation in a partially observed MDP (POMDP). Specifically, we consider estimating the value of a given target policy in an unknown POM...
• Demand Estimation Under Uncertain Consideration Sets AI翻译
  作者：Jagabathula, Srikanth; Mitrofanov, Dmitry; Vulcano, Gustavo
  作者单位：New York University; Boston College; Universidad Torcuato Di Tella
  摘要：To estimate customer demand, choice models rely both on what the individuals do and do not purchase. A customer may not purchase a product because it was not offered but also because it was not considered. To account for this behavior, existing literature has proposed the so-called consider-then-choose (CTC) models, which posit that customers sample a consideration set and then choose the most preferred product from the intersection of the offer set and the consideration set. CTC models have b...
• Unified Moment-Based Modeling of Integrated Stochastic Processes AI翻译
  作者：Kyriakou, Ioannis; Brignone, Riccardo; Fusai, Gianluca
  作者单位：City St Georges, University of London; University of Freiburg; University of Eastern Piedmont Amedeo Avogadro; City St Georges, University of London
  摘要：In this paper, we present a new method for simulating integrals of stochastic processes. We focus on the nontrivial case of time integrals, conditional on the state variable levels at the endpoints of a time interval through a moment-based probability distribution construction. We present different classes of models with important uses in finance, medicine, epidemiology, climatology, bioeconomics, and physics. The method is generally applicable in well-posed moment problem settings. We study i...
• The Fragility of Optimized Bandit Algorithms AI翻译
  作者：Fan, Lin; Glynn, Peter W.
  作者单位：Northwestern University; Stanford University
  摘要：Much of the literature on optimal design of bandit algorithms is based on minimization of expected regret. It is well known that algorithms that are optimal over certain exponential families can achieve expected regret that grows logarithmically in the number of trials at a rate specified by the Lai-Robbins lower bound. In this paper, we show that when one uses such optimized algorithms, the resulting regret distribution necessarily has a very heavy tail, specifically that of a truncated Cauch...
• Incentive-Aware Models of Financial Networks AI翻译
  作者：Jalan, Akhil; Chakrabarti, Deepayan; Sarkar, Purnamrita
  作者单位：University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin
  摘要：Financial networks help firms manage risk but also enable financial shocks to spread. Despite their importance, existing models of financial networks have several limitations. Prior works often consider a static network with a simple structure (e.g., a ring) or a model that assumes conditional independence between edges. We propose a new model where the network emerges from interactions between heterogeneous utility-maximizing firms. Edges correspond to contract agreements between pairs of fir...