2023

最新文章

• Bayesian Inventory Control: Accelerated Demand Learning via Exploration Boosts AI翻译
  作者：Chuang, Ya-Tang; Kim, Michael Jong
  作者单位：National Cheng Kung University; University of British Columbia
  摘要：We investigate Bayesian inventory control problems where parameters of the demand distribution are not known a priori but need to be learned using right-censored sales data. A Bayesian framework is adopted for demand learning, and the corresponding control problem is analyzed via Bayesian dynamic programming (BDP). In the Bayesian setting, it is known that the BDP-optimal decision is equal to the sum of the myopic-optimal decision plus a nonnegative exploration boost. The goal of this paper is...
• Work More Tomorrow: Resolving Present Bias in Project Management AI翻译
  作者：Shi, Yun; Hall, Nicholas G.; Cui, Xiangyu
  作者单位：East China Normal University; East China Normal University; University System of Ohio; Ohio State University; Shanghai University of Finance & Economics; Shanghai Institute of International Finance & Economics
  摘要：Project management is responsible for almost 30% of the world's economic activity, with an annual value of $27 trillion. Traditionally, the frequent late delivery of projects is attributed to Parkinson's Law, which incorporates laziness, procrastination, and self-protection against reduced deadlines in the future. Incentive schemes are widely designed and implemented to eliminate Parkinson's Law. Yet many projects are nonetheless delivered late. To explain this, we show computationally that a ...
• Contextual Search in the Presence of Adversarial Corruptions AI翻译
  作者：Krishnamurthy, Akshay; Lykouris, Thodoris; Podimata, Chara; Schapire, Robert
  作者单位：Microsoft; Massachusetts Institute of Technology (MIT); University of California System; University of California Berkeley
  摘要：We study contextual search, a generalization of binary search in higher dimensions, which captures settings such as feature-based dynamic pricing. Standard formulations of this problem assume that agents act in accordance with a specific homogeneous response model. In practice, however, some responses may be adversarially corrupted. Existing algorithms heavily depend on the assumed responsemodel being (approximately) accurate for all agents and have poor performance in the presence of even a f...
• Need for Speed: The Impact of In-Process Delays on Customer Behavior in Online Retail AI翻译
  作者：Gallino, Santiago; Karacaoglu, Nil; Moreno, Antonio
  作者单位：University of Pennsylvania; University System of Ohio; Ohio State University; Harvard University
  摘要：The impact of delays has been widely studied in various offline services. The focus of this study is online services, andwe explore the impact of in-process delays-measured by website speed-on customer behavior. We leverage novel retail and website speed data to investigate how delays impact online sales and how customer sensitivity to in-process delays varies across the different stages of a customer's shopping journey. We estimate sizable adverse effects of website slowdowns on online sales....
• Inventory Sharing for Perishable Products: Application to Platelet Inventory Management in Hospital Blood Banks AI翻译
  作者：Zhang, Can; Ayer, Turgay; White, Chelsea C.; Bodeker, Joy N.; Roback, John D.
  作者单位：Duke University; University System of Georgia; Georgia Institute of Technology; Emory University; Emory University
  摘要：Platelets are critical blood products. The management of platelet inventory is particularly challenging because of its perishable nature with a short shelf life. Motivated by a platelet inventory management problem at a two-location hospital system, we study how the wastage of platelets and, more broadly, perishable products can be reduced through inventory sharing. In particular, we consider a system with two locations and a single product (e.g., a two-hospital system sharing blood products, ...
• A Surrogate-Based Asynchronous Decomposition Technique for Realistic Security-Constrained Optimal Power Flow Problems AI翻译
  作者：Petra, Cosmin G.; Aravena, Ignacio
  作者单位：United States Department of Energy (DOE); Lawrence Livermore National Laboratory
  摘要：We present a decomposition approach for obtaining good feasible solutions for the security-constrained, alternating-current, optimal power flow (SC-AC-OPF) problem at an industrial scale and under real-world time and computational limits. The approach was designed while preparing and participating in ARPA-E's Grid Optimization Competition (GOC) Challenge 1. The challenge focused on a near-real-time version of the SC-AC-OPF problem, where a base operating point is optimized, taking into account...
• Computation of Systemic Risk Measures: A Mixed-Integer Programming Approach AI翻译
  作者：Ararat, Cagin; Meimanjan, Nurtai
  作者单位：Ihsan Dogramaci Bilkent University; Vienna University of Economics & Business
  摘要：Systemic risk is concerned with the instability of a financial system whose members are interdependent in the sense that the failure of a few institutions may trigger a chain of defaults throughout the system. Recently, several systemic risk measures have been proposed in the literature that are used to determine capital requirements for the members subject to joint risk considerations. We address the problem of computing systemic risk measures for systems with sophisticated clearing mechanism...
• Technical Note-New Bounds for Cardinality-Constrained Assortment Optimization Under the Nested Logit Model AI翻译
  作者：Kunnumkal, Sumit
  作者单位：Indian School of Business (ISB)
  摘要：We consider the cardinality-constrained assortment optimization problem under the nested logit model where there is a constraint that limits the number of products that can be offered within each nest. The problem is known to be intractable if the nest dissimilarity parameters are larger than one or there is a no-purchase alternative within each nest. Although these conditions often come up in practice, the existing solution approaches cannot handle them. We propose a solution method to obtain...
• Does the Prohibition of Trade-Through Hurt Liquidity Demanders? AI翻译
  作者：Chen, Ningyuan; Gao, Pin; Kou, Steven
  作者单位：University of Toronto; The Chinese University of Hong Kong, Shenzhen; Shenzhen Institute of Artificial Intelligence & Robotics for Society; Boston University
  摘要：The order protect rule (OPR) in the United States generally prohibits any trade through, that is, a market order that is not executed at the best possible price among fast (electronic and automated) trading venues. By deriving upper and lower bounds for the difference in the execution costs in a dynamic model, we find that, although trade-through allows for flexible trading strategies and may benefit the liquidity demander, the benefit is insignificant in most cases, especially for small trade...
• Distributionally Robust Losses for Latent Covariate Mixtures AI翻译
  作者：Duchi, John; Hashimoto, Tatsunori; Namkoong, Hongseok
  作者单位：Stanford University; Stanford University; Stanford University; Columbia University
  摘要：While modern large-scale data sets often consist of heterogeneous subpopulations-for example, multiple demographic groups or multiple text corpora-the standard practice of minimizing average loss fails to guarantee uniformly low losses across all sub-populations. We propose a convex procedure that controls the worst case performance over all subpopulations of a given size. Our procedure comes with finite-sample (nonparametric) convergence guarantees on the worst-off subpopulation. Empirically,...