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作者：Hubner, Thomas; Hug, Gabriela
作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich
摘要：A key challenge in combinatorial auctions is designing bid formats that accurately capture agents' preferences while remaining computationally feasible. This is especially true for electricity auctions, where complex preferences complicate straightforward solutions. In this context, we examine the XOR package bid, the default choice in combinatorial auctions and adopted in European day-ahead and intraday auctions under the name exclusive group of block bids. Unlike parametric bid formats often...
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作者：Abada, Ibrahim; Ancel, Julien
作者单位：Grenoble Ecole Management; Universite Paris Saclay; Universite PSL; Universite Paris-Dauphine; Ecole Nationale des Ponts et Chaussees; Institut Polytechnique de Paris; Ecole Nationale des Ponts et Chaussees
摘要：In electricity systems, investment in generation capacity is subject to risk. The distribution of uncertain parameters on which investment decisions depend might not be fully observed in historical values. In Europe, this was recently illustrated by the crisis of exceptionally high power prices during the 2021-2023 period, which was subsequently followed by a regime of extremely low and even negative prices. In that vein, although some models of risk aversion modify the distribution of realiza...
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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：Lidbetter, Thomas; Lin, Kyle Y.
作者单位：Rutgers University New Brunswick; Rutgers University System; Rutgers University Newark; Rutgers University New Brunswick; United States Department of Defense; United States Navy; Naval Postgraduate School
摘要：This paper presents a booby trap game played between a defender and an attacker on a search space, which may be a Lebesque measurable subset of Euclidean space or a network. The defender has several booby traps and chooses where to plant them. The attacker, aware of the presence of these booby traps but not their locations, chooses a subset of the space and collects a reward equal to the Lebesgue measure of the subset. If the attacker does not encounter any booby traps, then the attacker keeps...
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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：Yuan, Quan; Du, Longyuan; Hu, Ming
作者单位：Zhejiang University; University of San Francisco; University of Toronto
摘要：We study dynamic pricing under a stochastic, nonlinear, self-exciting demand arrival process over a finite sales horizon. We adopt such a correlated demand process to capture the phenomenon that customers who have made a purchase can inform and excite future customers to arrive. Specifically, the stochastic arrival intensity is boosted immediately after any purchase and gradually decays between consecutive purchases. The arrival intensity is also affected by the market phase the seller operate...
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作者：Chen, Li; Chou, Mabel; Sun, Qinghe
作者单位：University of Sydney; National University of Singapore; National University of Singapore; National University of Singapore; Hong Kong Polytechnic University
摘要：Process flexibility has been a well-established supply chain strategy in both theory and practice for managing demand uncertainty. This study extends its application to mitigating supply disruptions by analyzing a long chain system. Specifically, we investigate the effectiveness of long chains in the face of random supply disruptions and demand uncertainty. We derive a closed-form, tight bound on the expected sales ratio of a long chain relative to full flexibility under random disruptions, th...
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作者：Lu, Haihao; Sturt, Bradley
作者单位：Massachusetts Institute of Technology (MIT); University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital
摘要：We consider a class of production-inventory problems with box uncertainty sets from the seminal work of Ben-Tal et al. [Ben-Tal A, Goryashko A, Guslitzer E, Nemirovski A (2004) Adjustable robust solutions of uncertain linear programs. Math. Programming 99(2):351-376] on linear decision rules in robust optimization. We prove that there always exists an optimal linear decision rule for this class of problems in which the number of nonzero parameters in the linear decision rule grows linearly in ...
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作者：Charlet, Nils; Van Houdt, Benny
作者单位：University of Antwerp
摘要：Recently it was shown that the response time of first-come-first-served (FCFS) scheduling can be stochastically and asymptotically improved upon by the Nudge scheduling algorithm in case of light-tailed job size distributions. Such improvements are feasible even when the jobs are partitioned into two types, and the scheduler only has information about the type of incoming jobs (but not their size). In this paper, we introduce Nudge*(M) scheduling, where basically any incoming type 1 job is all...
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作者：Correa, Jose; Cristi, Andres; Norouzi-Fard, Ashkan; Norouzi-Fard, Ashkan
作者单位：Universidad de Chile; Alphabet Inc.; Google Incorporated
摘要：There is growing awareness and concern about fairness in machine learning and algorithm design. This is particularly true in online selection problems, where decisions are often biased: for example, when assessing credit risks or hiring staff. We address the issues of fairness and bias in online selection by studying multicolor versions of the classic secretary and prophet problems. In the multicolor secretary problem, we consider that each candidate has a color, and we can only compare candid...
