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作者：Elmachtoub, Adam N.; Kim, Hyemi
作者单位：Columbia University; Columbia University
摘要：Vehicle sharing systems, such as those for bicycles, scooters, and cars, are fundamental to serve transportation needs. Companies that operate these systems set prices (or fares) using algorithms to determine how much a user must pay and display the fares through mobile applications. This may result in users from different locations paying different prices for a vehicle. Moreover, the overall accessibility of these systems may be very different depending on the user's location. Platforms and r...
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作者：El Housni, Omar; Goyal, Vineet; Hanguir, Oussama; Stein, Clifford
作者单位：Cornell University; Columbia University
摘要：Matching demand (riders) to supply (drivers) efficiently is a fundamental problem for ride-sharing platforms that need to match the riders (almost) as soon as the request arrives with only partial knowledge about future ride requests. A myopic approach that computes an optimal matching for current requests ignoring future uncertainty can be highly suboptimal. In this paper, we consider a two-stage robust optimization framework for this matching problem in which future demand uncertainty is mod...
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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：Benade, Gerdus; Procaccia, Ariel D.; Tucker-Foltz, Jamie
作者单位：Boston University; Harvard University; Yale University
摘要：The design of algorithms for political redistricting generally takes one of two approaches: optimize an objective such as compactness or, drawing on fair division, construct a protocol whose outcomes guarantee partisan fairness. We aim to have the best of both worlds by optimizing an objective subject to a binary fairness constraint. As a fairness constraint, we adopt the geometric target, which requires the number of seats won by each party to be at least the average (rounded down) of its out...
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作者：Najafi, Sajjad; Jasin, Stefanus; Uichanco, Joline; Zhao, Jinglong
作者单位：Hautes Etudes Commerciales (HEC) Paris; University of Michigan System; University of Michigan; New York University; New York University Tandon School of Engineering; Boston University
摘要：We study assortment and price optimization under the contextual concavity (CC) model introduced in the literature, which subsumes the well-known multiattribute loss aversion (MLA) model. Unlike context-independent choice models that assume product utilities are unaffected by other alternatives in the assortment, the CC model offers a context-dependent framework that incorporates reference points across multiple attributes and captures prominent context effects (e.g., the compromise effect) wel...
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作者：Zhou, Ziyan; Wang, Tong; Zhang, Tingwei
作者单位：Shanghai Jiao Tong University; City University of Hong Kong; The Chinese University of Hong Kong, Shenzhen
摘要：Stockout-based substitution creates complex stochastic dynamics in inventory systems even in highly symmetric settings. We study a joint assortment and inventory allocation problem in which a firm allocates a fixed total inventory of m units across n perfectly substitutable product types (e.g., colors or designs). Customers are indifferent among available types and arrive sequentially, each purchasing one unit chosen uniformly at random from the nonempty types. The sales process terminates whe...
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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...
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作者：Jia, Su; Li, Andrew; Ravi, R.; Oli, Nishant; Duff, Paul; Anderson, Ian
作者单位：Amazon.com; Carnegie Mellon University
摘要：Modern platforms leverage randomized experiments to make informed decisions from a given set of items (treatment arms). As a particularly challenging scenario, these items can (i) arrive in a high volume, with thousands of new items being released per hour, and (ii) have a short lifetime due to their transient nature. We study a Bayesian multiple-play bandit problem that encapsulates the key features of this scenario. In each round, a set of arms arrives. Each arm has a lifetime w and an unkno...
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作者：Jiang, Songchen; Li, Zhaolin; Bi, Sheng; Teo, Chung-Piaw; Huang, Min
作者单位：Northeastern University - China; National University of Singapore; University of Sydney; Shanghai University of Finance & Economics
摘要：We generalize Scarf's classical min-max newsvendor model from a singleperiod setting to a multiperiod inventory system with independent demand across periods. This extension leverages mean-variance analysis to capture the dynamic effects of lead times, yielding closed-form expressions for the optimal base-stock level. As a concrete application, we study a single-product, dual-sourcing system with constant lead times and backlogging. We show that the optimal tailored base-surge policy admits a ...
