Operations Research

最新文章

• Heuristic (S, T) Solutions via an FPTAS for a One-Warehouse Multiretailer Problem AI翻译
  作者：Najy, Waleed; Diabat, Ali; Elbassioni, Khaled
  作者单位：New York University; New York University Abu Dhabi; New York University; New York University Tandon School of Engineering
  摘要：The difficulty of analyzing and optimizing the stochastic one-warehouse multiretailer problem under the (S, T) policy motivates the need to consider approximate but high-fidelity systems that are easier to scrutinize. We consider one such model in the setting in which retailers face independent normally distributed demand with given (nonidentical) means and variances. Safety stock is computed via a type-I service-level formula that ignores allocation issues, and the cost function is computed b...
• Distributionally Robust Optimal Allocation with Costly Verification AI翻译
  作者：Bayrak, Halil Ibrahim; Kocyigit, Cagil; Kuhn, Daniel; Pinar, Mustafa Celebi
  作者单位：Ihsan Dogramaci Bilkent University; University of Luxembourg; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne
  摘要：We consider the mechanism design problem of a principal allocating a single good to one of several agents without monetary transfers. Each agent desires the good and uses it to create value for the principal. We designate this value as the agent's private type. Even though the principal does not know the agents' types, she can verify them at a cost. The allocation of the good thus depends on the agents' self-declared types and the results of any verification performed, and the principal's payo...
• Coordinated Inventory Stocking and Assortment Customization AI翻译
  作者：Bai, Yicheng; El Housni, Omar; Rusmevichientong, Paat; Topaloglu, Huseyin
  作者单位：University of Southern California
  摘要：We study a joint inventory stocking and assortment customization problem. We have access to a set of products that can be used to stock a storage facility with limited capacity. At the beginning of the selling horizon, we decide how many units of each product to stock. Customers of different types with type-dependent preferences for the products arrive over the selling horizon. Depending on the remaining product inventories and the type of the customer, we offer a product assortment to the arr...
• Temporal Fairness in Learning and Earning: Price Protection Guarantee and Phase Transitions AI翻译
  作者：Feng, Qing; Zhu, Ruihao; Jasin, Stefanus
  作者单位：Cornell University; Cornell University; University of Michigan System; University of Michigan
  摘要：Motivated by the prevalence of price protection guarantee which helps to promote temporal fairness in dynamic pricing, we study the impact of such policy on the design of online learning algorithms for data-driven dynamic pricing with initially unknown customer demand. Under the price protection guarantee, a customer who purchased a product in the past can receive a refund from the seller during the so-called price protection period (typically defined as a certain time window after the purchas...
• Slowly Varying Regression Under Sparsity AI翻译
  作者：Bertsimas, Dimitris; Digalakis Jr, Vassilis; Li, Michael Lingzhi; Lami, Omar Skali
  作者单位：Massachusetts Institute of Technology (MIT); Hautes Etudes Commerciales (HEC) Paris; Harvard University; McKinsey & Company
  摘要：We introduce the framework of slowly varying regression under sparsity, which allows sparse regression models to vary slowly and sparsely. We formulate the problem of parameter estimation as a mixed -integer optimization problem and demonstrate that it can be reformulated exactly as a binary convex optimization problem through a novel relaxation. The relaxation utilizes a new equality on Moore -Penrose inverses that convexifies the nonconvex objective function while coinciding with the origina...
• Multivariate Almost Stochastic Dominance: Transfer Characterizations and Sufficient Conditions Under Dependence Uncertainty AI翻译
  作者：Mueller, Alfred; Scarsini, Marco; Tsetlin, Ilia; Winkler, Robert L.
  作者单位：Universitat Siegen; Luiss Guido Carli University; INSEAD Business School; Duke University
  摘要：Most often, important decisions involve several unknown attributes. This produces a double challenge in the sense that both assessing the individual multiattribute preferences and assessing the joint distribution of the attributes can be extremely hard. To handle the first challenge, we suggest multivariate almost stochastic dominance, a relation based on bounding marginal utilities. We provide necessary and sufficient characterizations in terms of simple transfers, which are easily communicat...
• Technical Note-A New Rate-Optimal Sampling Allocation for Linear Belief Models AI翻译
  作者：Zhou, Jiaqi; Ryzhov, Ilya O.
  作者单位：University System of Maryland; University of Maryland College Park; University System of Maryland; University of Maryland College Park
  摘要：We derive a new optimal sampling budget allocation for belief models based on linear regression with continuous covariates, where the expected response is interpreted as the value of the covariate vector, and an error occurs if a lower-valued vector is falsely identified as being better than a higher-valued one. Our allocation optimizes the rate at which the probability of error converges to zero using a large deviations theoretic characterization. This is the first large deviations-based opti...
• Fairness and Bias in Online Selection AI翻译
  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...
• Fairness-Aware Online Price Discrimination with Nonparametric Demand Models AI翻译
  作者：Chen, Xi; Lyu, Jiameng; Zhang, Xuan; Zhou, Yuan
  作者单位：New York University; Fudan University; University of Illinois System; University of Illinois Urbana-Champaign; Tsinghua University
  摘要：Price discrimination, which refers to the strategy of setting different prices for different customer groups, has been widely used in online retailing. Although it helps boost the collected revenue for online retailers, it might create serious concerns about fairness, which even violates regulations and laws. This paper studies the problem of dynamic discriminatory pricing under a relative price fairness constraint in the pricing literature. We first establish a regret lower bound of ohm(T4=5)...
• Blind Network Revenue Management and Bandits with Knapsacks Under Limited Switches AI翻译
  作者：Simchi-Levi, David; Xu, Yunzong; Zhao, Jinglong
  作者单位：Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT); University of Illinois System; University of Illinois Urbana-Champaign; Boston University
  摘要：This paper studies the impact of limited switches on resource-constrained dynamic pricing with demand learning. We focus on the classical price-based blind network revenue management problem and extend our results to the bandits with knapsacks problem. In both settings, a decision maker faces stochastic and distributionally unknown demand, and must allocate finite initial inventory across multiple resources over time. In addition to standard resource constraints, we impose a switching constrai...