2024

最新文章

• Risk-Averse Regret Minimization in Multistage Stochastic Programs AI翻译
  作者：Poursoltani, Mehran; Delage, Erick; Georghiou, Angelos
  作者单位：Universite de Montreal; HEC Montreal; Universite de Montreal; HEC Montreal; University of Cyprus
  摘要：Within the context of optimization under uncertainty, a well-known alternative to minimizing expected value or the worst-case scenario consists in minimizing regret. In a multistage stochastic programming setting with a discrete probability distribution, we explore the idea of risk-averse regret minimization, where the benchmark policy can only benefit from foreseeing increment steps into the future. The increment -regret model naturally interpolates between the popular ex ante and ex post reg...
• A Sequential Model for High-Volume Recruitment Under Random Yields AI翻译
  作者：Du, Lilun; Li, Qing; Yu, Peiwen
  作者单位：City University of Hong Kong; Hong Kong University of Science & Technology; Chongqing University
  摘要：We model a multiphase and high-volume recruitment process as a large-scale dynamic program. The success of the process is measured by a reward, which is the total assessment score of accepted candidates minus the penalty cost of the number of accepted candidates in the end deviating from a preset hiring target. For a recruiter, two questions are important: How many offers should be made in each phase? And how does the number of phases affect the reward? We consider an upper bound, which is obt...
• Testing, Voluntary Social Distancing, and the Spread of an Infection AI翻译
  作者：Acemoglu, Daron; Makhdoumi, Ali; Malekian, Azarakhsh; Ozdaglar, Asuman
  作者单位：Massachusetts Institute of Technology (MIT); Duke University; University of Toronto; Massachusetts Institute of Technology (MIT)
  摘要：We study the effects of testing policy on voluntary social distancing and the spread of an infection. Agents decide their social activity level, which determines a social network over which the virus spreads. Testing enables the isolation of infected individuals, slowing down the infection. However, greater testing also reduces voluntary social distancing or increases social activity, exacerbating the spread of the virus. We show that the effect of testing on infections is nonmonotone. This no...
• Lagrangian Dual Decision Rules for Multistage Stochastic Mixed-Integer Programming AI翻译
  作者：Daryalal, Maryam; Bodur, Merve; Luedtke, James R.
  作者单位：Universite de Montreal; HEC Montreal; University of Toronto; University of Wisconsin System; University of Wisconsin Madison
  摘要：Multistage stochastic programs can be approximated by restricting policies to follow decision rules. Directly applying this idea to problems with integer decisions is difficult because of the need for decision rules that lead to integral decisions. In this work, we introduce Lagrangian dual decision rules (LDDRs) for multistage stochastic mixed-integer programming (MSMIP), which overcome this difficulty by applying decision rules in a Lagrangian dual of the MSMIP. We propose two new bounding t...
• Learning in Inverse Optimization: Incenter Cost, Augmented Suboptimality Loss, and Algorithms AI翻译
  作者：Scroccaro, Pedro Zattoni; Atasoy, Bilge; Esfahani, Peyman Mohajerin
  作者单位：Delft University of Technology; Delft University of Technology
  摘要：This article may be used only for the purposes of research, teaching, and/or private study. Commercial use or systematic downloading (by robots or other automatic processes) is prohibited without explicit Publisher approval, unless otherwise noted. For more information, contact permissions@informs.org. The Publisher does not warrant or guarantee the article's accuracy, completeness, merchantability, fitness inclusion of an advertisement in this article, neither constitutes nor implies a guaran...
• Technical Note-Asymptotically Optimal Control of Omnichannel Service Systems with Pick-up Guarantees AI翻译
  作者：Gao, Xuefeng; Huang, Junfei; Zhang, Jiheng
  作者单位：Chinese University of Hong Kong; Chinese University of Hong Kong; Hong Kong University of Science & Technology
  摘要：Motivated by the recent popularity of omnichannel service systems, we analyze the joint admission and scheduling control of a queueing system with two classes of customers: online and walk-in. Unlike walk-in customers, online customers are given a target time for pick up upon placing an order. Thus, in addition to minimizing the waiting costs of walk-in customers and the rejection cost of both classes, we need to minimize the earliness and tardiness costs of online customers. Such a distinctiv...
• The Generalized Sethi Advertising Model AI翻译
  作者：Kennedy, Adrian P.; Sethi, Suresh P.; Siu, Chi Chung; Yam, Sheung Chi Phillip
  作者单位：Chinese University of Hong Kong; University of Texas System; University of Texas Dallas; Hang Seng University of Hong Kong
  摘要：We propose a flexible yet tractable dynamic advertising model called the generalized Sethi model to capture different market penetration rates across various media and markets via advertising. Specifically, the generalized Sethi model employs a Cobb-Douglas production function of advertising expenditure and the untapped market share with constant returns to scale. It encompasses some standard dynamic advertising models as particular cases. Moreover, the model's flexibility does not compromise ...
• Bonferroni-Free and Indifference-Zone-Flexible Sequential Elimination Procedures for Ranking and Selection AI翻译
  作者：Wang, Wenyu; Wan, Hong; Chen, Xi
  作者单位：North Carolina State University; Virginia Polytechnic Institute & State University
  摘要：This paper proposes two fully sequential procedures for selecting the best system with a guaranteed probability of correct selection (PCS). The main features of the proposed procedures include the following: (1) adopting a Bonferroni-free model that overcomes the conservativeness of the Bonferroni correction and delivers the exact probabilistic guarantee without overshooting; (2) conducting always valid and fully sequential hypothesis tests that enable continuous monitoring of each candidate s...
• Uncertainty Quantification and Exploration for Reinforcement Learning AI翻译
  作者：Zhu, Yi; Dong, Jing; Lam, Henry
  作者单位：Northwestern University; Columbia University; Columbia University
  摘要：We investigate statistical uncertainty quantification for reinforcement learning (RL) and its implications in exploration policy. Despite ever-growing literature on RL applications, fundamental questions about inference and error quantification, such as large-sample behaviors, appear to remain quite open. In this paper, we fill in the literature gap by studying the central limit theorem behaviors of estimated Q-values and value functions under various RL settings. In particular, we explicitly ...
• Online Learning and Pricing for Service Systems with Reusable Resources AI翻译
  作者：Jia, Huiwen; Shi, Cong; Shen, Siqian
  作者单位：University of Michigan System; University of Michigan
  摘要：We consider a price-based revenue management problem with finite reusable resources over a finite time horizon T. Customers arrive following a price-dependent Poisson process, and each customer requests one unit of c homogeneous reusable resources. If there is an available unit, the customer gets served within a price-dependent exponentially distributed service time; otherwise, the customer waits in a queue until the next available unit. In this paper, we assume that the firm does not know how...