Operations Research

最新文章

• Selection and Ordering Policies for Hiring Pipelines via Linear Programming AI翻译
  作者：Epstein, Boris; Ma, Will
  作者单位：Columbia University
  摘要：Motivated by hiring pipelines, we study three selection and ordering problems in which applicants for a finite set of positions are interviewed or sent offers. There is a finite time budget for interviewing/sending offers, and every interview/offer is followed by a stochastic realization of discovering the applicant's quality or acceptance decision, leading to computationally challenging problems. In the first problem, we study sequential interviewing and show that a computationally tractable,...
• Risk Sensitivity and Firm Power: Price Competition with Mean2-Variance Profit Objective Under Multinomial Logit Demand AI翻译
  作者：Li, Hongmin; Webster, Scott
  作者单位：Arizona State University; Arizona State University-Tempe
  摘要：This paper is the first in the literature to address a risk-sensitive price competition under the multinomial logit choice model, with each participating firm maximizing a riskadjusted profit objective. We find that, at equilibrium, a subset of firms earns a positive profit, whereas others are driven to zero profit, contrasting with the risk-neutral equilibrium in which all firms earn a positive profit regardless of quality and cost. We identify a power index-the ratio of effective product att...
• Optimal Dynamic Control of an Epidemic AI翻译
  作者：Kruse, Thomas; Strack, Philipp
  作者单位：University of Wuppertal; Yale University
  摘要：We analyze how to optimally engage in social distancing in order to minimize the spread of an infectious disease. We identify conditions under which any optimal policy is single peaked (i.e., first engages in increasingly more social distancing and subsequently decreases its intensity). We show that an optimal policy might substantially delay measures that decrease the transmission rate to create herd immunity and that engaging in social distancing suboptimally early can increase the number of...
• Robust Risk Quantification via Shock Propagation in Financial Networks AI翻译
  作者：Ahn, Dohyun; Chen, Nan; Kim, Kyoung-Kuk
  作者单位：Chinese University of Hong Kong; Korea Advanced Institute of Science & Technology (KAIST)
  摘要：Given limited network information, we consider robust risk quantification under the Eisenberg-Noe model for financial networks. To be more specific, motivated by the fact that the structure of the interbank network is not completely known in practice, we propose a robust optimization approach to obtain worst-case default probabilities and associated capital requirements for a specific group of banks (e.g., systemically important financial institutions) under network information uncertainty. Us...
• New Mixed-Integer Nonlinear Programming Formulations for the Unit Commitment Problems with Ramping Constraints AI翻译
  作者：Bacci, Tiziano; Frangioni, Antonio; Gentile, Claudio; Tavlaridis-Gyparakis, Kostas
  作者单位：Consiglio Nazionale delle Ricerche (CNR); Istituto di Analisi dei Sistemi ed Informatica Antonio Ruberti (IASI-CNR)
  摘要：The unit commitment (UC) problem in electrical power production requires to optimally operate a set of power generation units over a short time horizon. Operational constraints of each unit depend on its type and can be rather complex. For thermal units, typical ones concern minimum and maximum power output, minimum up-and down-time, startup and shut-down limits, ramp-up and ramp-down limits, and nonlinear objective function. In this work, we present the first mixed-integer nonlinear program f...
• Is Q-Learning Minimax Optimal? A Tight Sample Complexity Analysis AI翻译
  作者：Li, Gen; Cai, Changxiao; Chen, Yuxin; Wei, Yuting; Chi, Yuejie
  作者单位：University of Pennsylvania; University of Pennsylvania; Carnegie Mellon University
  摘要：Q-learning, which seeks to learn the optimal Q-function of a Markov decision process (MDP) in a model-free fashion, lies at the heart of reinforcement learning. When it comes to the synchronous setting (such that independent samples for all state-action pairs are drawn from a generative model in each iteration), substantial progress has been made toward understanding the sample efficiency of Q-learning. Consider a gamma-discounted infinite-horizon MDP with state space S and action space A: to ...
• Debiasing In-Sample Policy Performance for Small-Data, Large-Scale Optimization AI翻译
  作者：Gupta, Vishal; Huang, Michael; Rusmevichientong, Paat
  作者单位：University of Southern California
  摘要：Motivated by the poor performance of cross-validation in settings where data are scarce, we propose a novel estimator of the out-of-sample performance of a policy in data-driven optimization. Our approach exploits the optimization problem's sensitivity analysis to estimate the gradient of the optimal objective value with respect to the amount of noise in the data and uses the estimated gradient to debias the policy's in-sample performance. Unlike cross-validation techniques, our approach avoid...
• Parallel Adaptive Survivor Selection AI翻译
  作者：Pei, Linda; Nelson, Barry L.; Hunter, Susan R.
  作者单位：Northwestern University; Purdue University System; Purdue University
  摘要：We reconsider the ranking and selection (R&S) problem in stochastic simulation optimization in light of high-performance, parallel computing, where we take ???R&S??? to mean any procedure that simulates all systems (feasible solutions) to provide some statisti-cal guarantee on the selected systems. We argue that when the number of systems is very large, and the parallel processing capability is also substantial, then neither the standard statistical guarantees such as probability of correct se...
• Randomized Assortment Optimization AI翻译
  作者：Wang, Zhengchao; Peura, Heikki; Wiesemann, Wolfram
  作者单位：Imperial College London; Aalto University
  摘要：When a firm selects an assortment of products to offer to customers, it uses a choice model to anticipate their probability of purchasing each product. In practice, the estimation of these models is subject to statistical errors, which may lead to significantly suboptimal assortment decisions. Recent work has addressed this issue using robust optimization, where the true parameter values are assumed unknown and the firm chooses an assortment that maximizes its worst -case expected revenues ove...
• Harvesting Solar Power Foments Prices in a Vicious Cycle: Breaking the Cycle with Price Mechanisms AI翻译
  作者：Mamaghani, Fariba F.; Cakanyildirim, Metin
  作者单位：Tulane University; University of Texas System; University of Texas Dallas
  摘要：Distributed solar power generation is growing but not necessarily benefiting the utility firms. Reducing the demand, it hinders the coverage of utility costs with reasonable retail electricity prices. Utilities raise prices, unintentionally reducing both demand and affordability of electricity, and are said to be caught in a utility (death) spiral. The reduced affordability adversely affects consumers who cannot invest in solar generation. Environmentally desirable solar power paradoxically ca...