3期

最新文章

• Conflicting Congestion Effects in Resource Allocation Games AI翻译
  作者：Feldman, Michal; Tamir, Tami
  作者单位：Hebrew University of Jerusalem; Hebrew University of Jerusalem; Reichman University
  摘要：We study strategic resource allocation settings, where jobs correspond to self-interested players who choose resources with the objective of minimizing their individual cost. Our framework departs from the existing game-theoretic models mainly in assuming conflicting congestion effects, but also in assuming an unlimited supply of resources. In our model, a job's cost is composed of both its resource's load (which increases with congestion) and its share in the resource's activation cost (which...
• Technical Note-A Sampling-Based Approach to Appointment Scheduling AI翻译
  作者：Begen, Mehmet A.; Levi, Retsef; Queyranne, Maurice
  作者单位：Western University (University of Western Ontario); Massachusetts Institute of Technology (MIT); University of British Columbia
  摘要：We consider the problem of appointment scheduling with discrete random durations but under the more realistic assumption that the duration probability distributions are not known and only a set of independent samples is available, e.g., historical data. For a given sequence of appointments (jobs, tasks), the goal is to determine the planned starting time of each appointment such that the expected total underage and overage costs due to the mismatch between allocated and realized durations is m...
• Technical Note-Branch-and-Price-and-Cut Approach to the Robust Network Design Problem Without Flow Bifurcations AI翻译
  作者：Lee, Chungmok; Lee, Kyungsik; Park, Kyungchul; Park, Sungsoo
  作者单位：Electronics & Telecommunications Research Institute - Korea (ETRI); Korea Advanced Institute of Science & Technology (KAIST); Hankuk University Foreign Studies; Myongji University
  摘要：This paper presents a robust optimization approach to the network design problem under traffic demand uncertainty. We consider the specific case of the network design problem in which there are several alternatives in edge capacity installations and the traffic cannot be split over several paths. A new decomposition approach is proposed that yields a strong LP relaxation and enables traffic demand uncertainty to be addressed efficiently through localization of the uncertainty to each edge of t...
• Optimal Market-Making with Risk Aversion AI翻译
  作者：Huang, Kan; Simchi-Levi, David; Song, Miao
  作者单位：Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT); University of Hong Kong
  摘要：Market-makers have the obligation to trade any given amount of assets at quoted bid or ask prices, and their inventories are exposed to the potential loss when the market price moves in an undesirable direction. One approach to reduce the risk brought by price uncertainty is to adjust the inventory at the price of losing potential spread gain. Using stochastic dynamic programming, we show that a threshold inventory control policy is optimal with respect to an exponential utility criterion and ...
• Habit Formation from Correlation Aversion AI翻译
  作者：Lichtendahl, Kenneth C., Jr.; Chao, Raul O.; Bodily, Samuel E.
  作者单位：University of Virginia
  摘要：Making plans about how much to consume and how much to invest in risky assets over an uncertain lifetime is a fundamental economic challenge. The leading models of this planning problem use either additive or habit-forming preferences. For the most part, these models assume an individual is either correlation neutral or correlation seeking in consumption, respectively. In this paper, we introduce two habit-forming, correlation-averse preference models. With these preferences, we find closed-fo...
• Approximate Dynamic Programming via a Smoothed Linear Program AI翻译
  作者：Desai, Vijay V.; Farias, Vivek F.; Moallemi, Ciamac C.
  作者单位：Columbia University; Massachusetts Institute of Technology (MIT); Columbia University
  摘要：We present a novel linear program for the approximation of the dynamic programming cost-to-go function in high-dimensional stochastic control problems. LP approaches to approximate DP have typically relied on a natural projection of a well-studied linear program for exact dynamic programming. Such programs restrict attention to approximations that are lower bounds to the optimal cost-to-go function. Our program-the smoothed approximate linear program-is distinct from such approaches and relaxe...
• Introduction to the OR Forum Article: Modeling the Impacts of Electricity Tariffs on Plug-in Hybrid Electric Vehicle Charging, Costs, and Emissions by Ramteen Sioshansi AI翻译
  作者：Pinker, Edieal J.
  作者单位：University of Rochester