Operations Research

最新文章

• A Booby Trap Game AI翻译
  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...
• Assortment and Price Optimization Under a Multiattribute (Contextual) Choice Model AI翻译
  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...
• Dynamic Pricing Under Self-Exciting Arrival Processes AI翻译
  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...
• Proactive Policing: Resource Allocation for Crime Prevention with Deterrence Effect AI翻译
  作者：He, Long; Li, Xiaobo; Zhao, Yue
  作者单位：George Washington University; National University of Singapore; Peking University Shenzhen Graduate School (PKU Shenzhen); Peking University
  摘要：This paper addresses police resource allocation across multiple locations, aiming to minimize the overall cost of potential crimes. Unlike previous literature focused on reactive police tasks, we propose a proactive approach that emphasizes crime prevention through deterrence. To account for the deterrence effect of police resources on crime, we employ the multinomial logit model to calibrate the distribution of crime locations. Our model sheds light on two facets of the deterrence effect in p...
• Learning in Inverse Optimization: Incenter Cost, Augmented Suboptimality Loss, and Algorithms AI翻译
  作者：Scroccaro, Pedro Zattoni; Atasoy, Bilge; Esfahani, Peyman Mohajerin
  作者单位：Delft University of Technology; Delft University of Technology
  摘要：In inverse optimization (IO), an expert agent solves an optimization problem parametric in an exogenous signal. From a learning perspective, the goal is to learn the expert's cost function given a data set of signals and corresponding optimal actions. Motivated by the geometry of the IO set of consistent cost vectors, we introduce the incenter concept, a new notion akin to the recently proposed circumcenter concept. Discussing the geometric and robustness interpretation of the incenter cost ve...
• Process Flexibility: A Distribution-Free Approach to Long Chain Resilience AI翻译
  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...
• Joint Capacity Allocation and Job Assignment Under Uncertainty AI翻译
  作者：Wang, Peng; Lim, Yun Fong; Loke, Gar Goei
  作者单位：Singapore University of Social Sciences (SUSS); Singapore Management University; Durham University
  摘要：In this paper, we consider the multiperiod joint capacity allocation and job assignment problem. The goal of the planner is to simultaneously decide on allocating resources across the J different supply nodes and assigning jobs of I different demand origins to these J nodes, so as to maximize the reward for matching or minimize the cost of failure to match. We furthermore consider three features: (i) supply is replenishable after random time, (ii) demand is random, and (iii) demand can wait an...
• Dynamic Pricing with Fairness Constraints AI翻译
  作者：Cohen, Maxime C.; Miao, Sentao; Wang, Yining
  作者单位：McGill University; University of Colorado System; University of Colorado Boulder; University of Texas System; University of Texas Dallas
  摘要：Following the increasing popularity of personalized pricing, there is a growing concern from customers and policymakers regarding fairness considerations. This paper studies the problem of dynamic pricing with unknown demand under two types of fairness constraints: price fairness and demand fairness. For price fairness, the retailer is required to (i) set similar prices for different customer groups (called group fairness) and (ii) ensure that the prices over time for each customer group are r...
• On the Sparsity of Optimal Linear Decision Rules for a Class of Robust Optimization Problems with Box Uncertainty Sets AI翻译
  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 ...
• Technical Note-A Data-Driven Approach to Beating SAA Out of Sample AI翻译
  作者：Gotoh, Jun-ya; Kim, Michael Jong; Lim, Andrew E. B.
  作者单位：Chuo University; University of British Columbia; National University of Singapore; National University of Singapore
  摘要：Whereas solutions of distributionally robust optimization (DRO) problems can sometimes have a higher out-of-sample expected reward than the sample average approximation (SAA), there is no guarantee. In this paper, we introduce a class of distributionally optimistic optimization (DOO) models and show that it is always possible to beat SAA out-of-sample if we consider not just worst case (DRO) models but also best case (DOO) ones. We also show, however, that this comes at a cost: optimistic solu...