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作者：Feng, Yiding; Tang, Wei; Xu, Haifeng
作者单位：Hong Kong University of Science & Technology; Chinese University of Hong Kong; University of Chicago
摘要：We introduce and study the online Bayesian recommendation problem for a recommender system platform. The platform has the privilege to privately observe a utilityrelevant state of a product at each round and uses this information to make online recommendations to a stream of myopic users. This paradigm is common in a wide range of scenarios in the current internet economy. The platform commits to an online recommendation policy that utilizes its information advantage on the product state to pe...
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作者：Yan, Chiwei; Yan, Julia; Shen, Yifan
作者单位：University of California System; University of California Berkeley; University of British Columbia; University of Washington; University of Washington Seattle
摘要：Shared rides, which pool individual riders into a single vehicle, are essential for mitigating congestion and promoting more sustainable urban transportation. However, major ridesharing platforms have long struggled to maintain a healthy and profitable shared rides product. To understand why shared rides have struggled, we analyze procedures commonly used in practice to set static prices for shared rides and discuss their pitfalls. We then propose a pricing policy that is adaptive to matching ...
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作者：Agarwal, Anish; Shah, Devavrat; Shen, Dennis
作者单位：Columbia University; Massachusetts Institute of Technology (MIT); University of Southern California
摘要：The synthetic controls (SC) methodology is a prominent tool for policy evaluation in panel data applications, typically grounded in a low-rank matrix factor model where potential outcomes are governed by low-dimensional latent factors over units and time. In this article, we present the synthetic interventions (SI) framework, an extension of the SC framework to accommodate multiple interventions. Fundamental to SI is a lowrank tensor factor model that builds on the matrix structure by embeddin...
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作者：Buke, Burak; dos Reis, Goncalo; Platonov, Vadim
作者单位：University of Edinburgh; Universidade Nova de Lisboa; Universidade de Lisboa
摘要：In most service systems, the servers are humans who desire to experience a certain level of idleness. In call centers, this manifests itself as call-avoidance behavior when servers strategically adjust their service rate to strike a balance between the idleness they receive and effort to work harder. Moreover, being human, each server values this tradeoff differently and has different capabilities. We develop a novel framework, relying on measure-valued processes and mean-field game theory, to...
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作者：Rath, Sandeep; Rajaram, Kumar; Hudson, Mark E.; Mahajan, Aman
作者单位：Indian School of Business (ISB); University of California System; University of California Los Angeles; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh
摘要：This paper presents the development, validation, and implementation of a datadriven optimization model designed to dynamically plan the assignment of anesthesiologists across multiple hospital locations within a large multispecialty healthcare system. We formulate the problem as a multistage robust mixed-integer program incorporating on-call flexibility to address demand uncertainty. In the first stage, anesthesiologists are assigned to specific locations or an on-call pool several weeks befor...
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作者：Fan, Jianqing; Lou, Zhipeng; Wang, Weichen; Yu, Mengxin
作者单位：Princeton University; University of California System; University of California San Diego; University of Hong Kong; Washington University (WUSTL)
摘要：This paper studies the performance of the spectral method in the estimation and uncertainty quantification of the unobserved preference scores of compared entities in a general and more realistic setup. Specifically, the comparison graph consists of hyperedges of possible heterogeneous sizes, and the number of comparisons can be as low as one for a given hyper-edge. Such a setting is pervasive in real applications, circumventing the need to specify the graph randomness and the restrictive homo...
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作者：Soh, Seung Bum; Gurvich, Itai
作者单位：Yonsei University; Northwestern University
摘要：Staffing problems are often formulated as satisfization problems, in which the cost of servers is minimized subject to quality of service constraints. These constraints indirectly capture customers' disutility from waiting or, at least, its structure. For the problem of staffing a single-class M/M/N queue with an average speed of answer (ASA) constraint, any work-conserving policy is optimal; the problem's formulation is, in that sense, ambiguous. One optimal solution is consistent with convex...
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作者：Ozel, Aysu; Smilowitz, Karen; Goldstein, Lila K. S.
作者单位：Northwestern University
摘要：Comprehensive community engagement in public school district design is essential to create equitable and effective enrollment policies reflective of community needs and values. We revisit the school district design problem with a focus on codesigning with community partners. We introduce a new compact formulation that incorporates multiple decisions simultaneously by assigning students in each geographic unit to a set of schools (e.g., elementary, middle, and high schools, and schools with spe...
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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：Cui, Titing; Hamilton, Michael L.
作者单位：University of Tulsa; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh
摘要：We study semipersonalized pricing policies in which a seller uses customer features to segment the market and offer segment-specific prices. Although such policies are common, determining the optimal segmentation and corresponding prices is computationally challenging, leading practitioners to rely on heuristic methods that first segment the market and then optimize the prices for each segment. We address this issue by studying the joint optimization of feature-based market segmentation and pr...
