Operations Research

最新文章

• Menu Costs and the Bullwhip Effect: Supply Chain Implications of Dynamic Pricing AI翻译
  作者：Bray, Robert L.; Stamatopoulos, Ioannis
  作者单位：Northwestern University; University of Texas System; University of Texas Austin
  摘要：We study the supply chain implications of dynamic pricing. Specifically, we estimate how reducing menu costs-the operational burden of adjusting prices-would affect supply chain volatility. Fitting a structural econometric model to data from a large Chinese supermarket chain, we estimate that removing menu costs would (i) reduce the mean shipment coefficient of variation by 7.2 percentage points (pp), (ii) reduce the mean sales coefficient of variation by 4.3 pp, and (iii) reduce the mean bull...
• Separation of Perishable Inventories in Offline Retailing Through Transshipment AI翻译
  作者：Li, Qing; Yu, Peiwen; Du, Lilun
  作者单位：Hong Kong University of Science & Technology; Chongqing University
  摘要：Transshipment in retailing is a practice where one outlet ships its excess inventory to another outlet with inventory shortages. By balancing inventories, transshipment can reduce waste and increase fill rate at the same time. In this paper, we explore the idea of transshipping perishable goods with a fixed finite lifetime in offline grocery retailing. In the offline retailing of perishable goods, customers typically choose the newest items first, which can lead to substantial waste. We show t...
• Naive Learning Through Probability Overmatching AI翻译
  作者：Arieli, Itai; Babichenko, Yakov; Mueller-Frank, Manuel
  作者单位：Technion Israel Institute of Technology; University of Navarra; IESE Business School
  摘要：We analyze boundedly rational updating in a repeated interaction network model with binary actions and binary states. Agents form beliefs according to discretized DeGroot updating and apply a decision rule that assigns a (mixed) action to each belief. We first show that under weak assumptions, random decision rules are sufficient to achieve agreement in finite time in any strongly connected network. Ourmain result establishes that naive learning can be achieved in any large strongly connected ...
• Stochastic Knapsack Revisited: The Service Level Perspective AI翻译
  作者：Lyu, Guodong; Chou, Mabel C.; Teo, Chung-Piaw; Zheng, Zhichao; Zhong, Yuanguang
  作者单位：National University of Singapore; National University of Singapore; Singapore Management University; South China University of Technology
  摘要：A key challenge in the resource allocation problem is to find near-optimal policies to serve different customers with random demands/revenues, using a fixed pool of capacity (properly configured). In this paper, we study the properties of three classes of allocation policies-responsive (with perfect hindsight), adaptive (with information updates), and anticipative (with forecast information) policies. These policies differ in how the information on actual demand and revenue of each customer is...
• Computing Large Market Equilibria Using Abstractions AI翻译
  作者：Kroer, Christian; Peysakhovich, Alexander; Sodomka, Eric; Stier-Moses, Nicolas E.
  作者单位：Columbia University; Facebook Inc; Facebook Inc
  摘要：Computing market equilibria is an important practical problem for market design, for example, in fair division of items. However, computing equilibria requires large amounts of information (typically the valuation of every buyer for every item) and computing power. We consider ameliorating these issues by applying a method used for solving complex games: constructing a coarsened abstraction of a given market, solving for the equilibrium in the abstraction, and lifting the prices and allocation...
• Bootstrap-based Budget Allocation for Nested Simulation AI翻译
  作者：Zhang, Kun; Liu, Guangwu; Wang, Shiyu
  作者单位：Renmin University of China; City University of Hong Kong
  摘要：Simulation budget allocation is at the heart of a nested (also referred to as two level) simulation approach to estimating functionals of a conditional expectation. In this paper, we propose a sample-driven budget allocation rule under a unified nested simulation framework that allows for different forms of functionals. The proposed method employs bootstrap sampling to guide an effective choice of outer-and inner-level sample sizes. Furthermore, we establish a central limit theorem for nested ...
• Network Revenue Management Under a Spiked Multinomial Logit Choice Model AI翻译
  作者：Cao, Yufeng; Kleywegt, Anton J.; Wang, He
  作者单位：Shanghai Jiao Tong University; University System of Georgia; Georgia Institute of Technology
  摘要：Airline booking data have shown that the fraction of customers who choose the cheapest available fare class often is much greater than that predicted by the multinomial logit choice model calibrated with the data. For example, the fraction of customers who choose the cheapest available fare class is much greater than the fraction of customers who choose the next cheapest available one, even if the price difference is small. To model this spike in demand for the cheapest available fare class, a...
• Distributionally Robust Linear and Discrete Optimization with Marginals AI翻译
  作者：Chen, Louis; Ma, Will; Natarajan, Karthik; Simchi-Levi, David; Yan, Zhenzhen
  作者单位：United States Department of Defense; United States Navy; Naval Postgraduate School; Columbia University; Singapore University of Technology & Design; Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT); Nanyang Technological University
  摘要：In this paper, we study linear and discrete optimization problems in which the objective coefficients are random, and the goal is to evaluate a robust bound on the expected optimal value, where the set of admissible joint distributions is assumed to be specified only up to the marginals. We study a primal-dual formulation for this problem, and in the process, unify existing results with new results. We establish NP-hardness of computing the bound for general polytopes and identify two sufficie...
• Smoothness-Adaptive Contextual Bandits AI翻译
  作者：Gur, Yonatan; Momeni, Ahmadreza; Wager, Stefan
  作者单位：Stanford University; Stanford University
  摘要：We study a nonparametric multiarmed bandit problem with stochastic covariates, where a key complexity driver is the smoothness of payoff functions with respect to covariates. Previous studies have focused on deriving minimax-optimal algorithms in cases where it is a priori known how smooth the payoff functions are. In practice, however, the smoothness of payoff functions is typically not known in advance, and misspecification of smoothness may severely deteriorate the performance of existing m...
• Extremizing and Antiextremizing in Bayesian Ensembles of Binary-Event Forecasts AI翻译
  作者：Lichtendahl, Kenneth C., Jr.; Grushka-Cockayne, Yael; Jose, Victor Richmond; Winkler, Robert L.
  作者单位：University of Virginia; Georgetown University; Duke University
  摘要：Probability forecasts of binary events are often gathered from multiple models or experts and averaged to provide inputs regarding uncertainty in important decision-making problems. Averages of well-calibrated probabilities are underconfident, and methods have been proposed to make them more extreme. To aggregate probabilities, we introduce a class of ensembles that are generalized additive models. These ensembles are based on Bayesian principles and can help us learn why and when extremizing ...