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作者：Dai, J. G.; Glynn, Peter W.; Xu, Yaosheng
作者单位：Cornell University; Stanford University; University of Chicago
摘要：We prove that under a multiscale heavy traffic condition, the stationary distribution of the scaled queue length vector process in any generalized Jackson network has a product-form limit. Each component in the product form follows an exponential distribution, corresponding to the Brownian approximation of a single station queue. The single station can be constructed precisely, and its parameters have a good intuitive interpretation.
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作者：Peng, Chengyuan; Stachurski, John
作者单位：Capital University of Economics & Business; National Graduate Institute for Policy Studies
摘要：New approaches to the theory of dynamic programming view dynamic programs as families of policy operators acting on partially ordered sets. In this paper, we extend these ideas by shifting from arbitrary partially ordered sets to ordered vector spaces. The integrated algebraic and order structure in such spaces leads to sharper fixed-point results. These fixed-point results can then be exploited to obtain optimality properties. We illustrate our results through applications ranging from firm m...
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作者：Olver, Neil; Sering, Leon; Koch, Laura Vargas
作者单位：University of London; London School Economics & Political Science
摘要：We consider a dynamic model of traffic that has received a lot of attention in the past few years. Users control infinitesimal flow particles aiming to travel from an origin to a destination as quickly as possible. Flow patterns vary over time, and congestion effects are modeled via queues, which form whenever the inflow into a link exceeds its capacity. Despite lots of interest, some very basic questions remain open in this model. We resolve a number of them in the single-commodity setting: (...
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作者：Tobey, Margaret; Mayorga, Maria E.; Bosisto, Sherrie; Ozaltin, Osman Y.
作者单位：North Carolina State University
摘要：Human trafficking investigators face challenges when processing the sheer volume of publicly available online data. Natural language processing (NLP) models can assist in identifying evidence of exploitation in text data, such as business reviews. However, the scarcity of large and accurately labeled training data sets hinders the potential for NLP-based detection algorithms. Labeling data sets related to human trafficking is challenging because identifying indicators of trafficking requires d...
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作者：Gao, Wenzhi; Ge, Dongdong; Sun, Chunlin; Xue, Chenyu; Ye, Yinyu
作者单位：Stanford University; Shanghai Jiao Tong University; Shanghai Institute for Mathematics & Interdisciplinary Sciences; East China University of Science and Technology
摘要：Online linear programming plays an important role in both revenue management and resource allocation, and recent research has focused on developing efficient firstorder online learning algorithms. Despite the empirical success of first-order methods, they root ffiffiffi typically achieve a regret no better than O( T ), which is suboptimal compared with the O(log T) bound guaranteed by the state-of-the-art linear programming (LP)-based online root ffiffiffi algorithms. This paper establishes a ...
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作者：Che, Ethan; Dong, Jing; Tong, Xin T.
作者单位：Columbia University; National University of Singapore
摘要：Stochastic gradient descent (SGD) is a powerful optimization technique that is particularly useful in online learning scenarios. Its convergence analysis is relatively well understood under the assumption that the data samples are independent and identically distributed (iid). However, applying SGD to policy optimization problems in operations research involves a distinct challenge: the policy changes the environment and thereby affects the data used to update the policy. The adaptively genera...
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作者：Shamsi, Davood; Luenberger, Robert; Ye, Yinyu
作者单位：Shanghai Jiao Tong University; Shanghai Institute for Mathematics & Interdisciplinary Sciences
摘要：This research note revisits the framework proposed in our earlier work and explores its conceptual and algorithmic connection to recent advances in dual-based online resource allocation-particularly the dual mirror descent method introduced previously. Both approaches address the challenge of making real-time sequential allocation decisions under dynamically revealed constraints. Although the dual mirror descent method relies on Bregman divergence to guide dual updates, our framework derives n...
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作者：Yang, Jincheng; Zhang, Luhao; Chen, Ningyuan; Gao, Rui; Hu, Ming
作者单位：Johns Hopkins University; University of Toronto; University Toronto Mississauga; University of Toronto; University of Texas System; University of Texas Austin
摘要：We consider stochastic optimization with side information where, prior to decision making, covariate data are available to inform better decisions. To hedge against data uncertainty while capturing the information structure revealed from the conditional distribution of random problem parameters given the covariate values, we propose a distributionally robust formulation based on causal transport distance. We derive a dual reformulation for evaluating the worst-case expected cost and show that ...
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作者：Chen, Xinyun; Hong, Guiyu; Liu, Yunan
作者单位：The Chinese University of Hong Kong, Shenzhen; Shanghai University of Finance & Economics; Amazon.com; North Carolina State University
摘要：We investigate an optimization problem in a queueing system where the service provider selects the optimal service fee p and service capacity & micro; to maximize the cumulative expected profit (the service revenue minus the capacity cost and delay penalty). The conventional predict-then-optimize (PTO) approach takes two steps: First, it estimates the model parameters (e.g., arrival rate and service-time distribution) from data; second, it optimizes a model taking these parameters as input. A ...
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作者：Grand-Clement, Julien; Petrik, Marek; Vieille, Nicolas
作者单位：University System Of New Hampshire; University of New Hampshire
摘要：Robust Markov decision processes (RMDPs) are a widely used framework for sequential decision-making under parameter uncertainty. RMDPs have been studied extensively when the objective was to maximize the discounted return, but little is known for average optimality (optimizing the long-run average of the rewards obtained over time) and Blackwell optimality (remaining discount optimal for all discount factors sufficiently close to 1). In this paper, we prove several foundational results for RMD...
