Operations Research

最新文章

• Multiproduct Pricing Under the Generalized Extreme Value Models with Homogeneous Price Sensitivity Parameters AI翻译
  作者：Zhang, Heng; Rusmevichientong, Paat; Topaloglu, Huseyin
  作者单位：University of Southern California
  摘要：We consider unconstrained and constrained multiproduct pricing problems when customers choose according to an arbitrary generalized extreme value (GEV) model and the products have the same price sensitivity parameter. In the unconstrained problem, there is a unit cost associated with the sale of each product. The goal is to choose the prices for the products to maximize the expected profit obtained from each customer. We show that the optimal prices of the different products have a constant ma...
• Adjacency-Clustering and Its Application for Yield Prediction in Integrated Circuit Manufacturing AI翻译
  作者：Hochbaum, Dorit S.; Liu, Sheng
  作者单位：University of California System; University of California Berkeley
  摘要：Accurate yield prediction in integrated circuit manufacturing enables accurate estimation of production cost and early detection of processing problems. It is known that defects tend to be clustered and a chip is likely to be defective if its neighbors are defective. This neighborhood effect is not well captured in traditional yield modeling approaches. We propose a new yield prediction model, called adjacency-clustering which addresses, for the first time, the neighborhood effect, and deliver...
• Wasserstein Distance and the Distributionally Robust TSP AI翻译
  作者：Carlsson, John Gunnar; Behroozi, Mehdi; Mihic, Kresimir
  作者单位：University of Southern California; Northeastern University; Oracle
  摘要：Recent research on the robust and stochastic traveling salesman problem and the vehicle routing problem has used many different approaches for describing the region of ambiguity including taking convex combinations of observed demand vectors or imposing constraints on the moments of the spatial demand distribution. One approach that has been used outside the transportation sector is the use of statistical metrics that describe a distance function between two probability distributions. Motivate...
• Exact Solution of the Evasive Flow Capturing Problem AI翻译
  作者：Arslan, Okan; Jabali, Ola; Laporte, Gilbert
  作者单位：Universite de Montreal; HEC Montreal; Polytechnic University of Milan
  摘要：The Evasive Flow Capturing Problem is defined as the problem of locating a set of law enforcement facilities on the arcs of a road network to intercept unlawful vehicle flows traveling between origin-destination pairs, who in turn deviate from their route to avoid any encounter with such facilities. Such deviations are bounded by a given tolerance. We first propose a bilevel program that, in contrast to previous studies, does not require a priori route generation. We then transform this bileve...
• Static Routing in Stochastic Scheduling: Performance Guarantees and Asymptotic Optimality AI翻译
  作者：Balseiro, Santiago R.; Brown, David B.; Chen, Chen
  作者单位：Columbia University; Duke University
  摘要：We study the problem of scheduling a set of J jobs on M machines with stochastic job processing times when no preemptions are allowed and with a weighted sum of expected completion times objective. Our model allows for unrelated machines: the distributions of processing times may vary across both jobs and machines. We study static routing policies, which assign (or route) each job to a particular machine at the start of the problem and then sequence jobs on each machine according to the weight...
• The Approximability of Assortment Optimization Under Ranking Preferences AI翻译
  作者：Aouad, Ali; Farias, Vivek; Levi, Retsef; Segev, Danny
  作者单位：Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT); University of Haifa
  摘要：The main contribution of this paper is to provide best-possible approximability bounds for assortment planning under a general choice model, where customer choices are modeled through an arbitrary distribution over ranked lists of their preferred products, subsuming most random utility choice models of interest. From a technical perspective, we show how to relate this optimization problem to the computational task of detecting large independent sets in graphs, allowing us to argue that general...
• Online First-Order Framework for Robust Convex Optimization AI翻译
  作者：Ho-Nguyen, Nam; Kilinc-Karzan, Fatma
  作者单位：Carnegie Mellon University
  摘要：Robust optimization (RO) has emerged as one of the leading paradigms to efficiently model parameter uncertainty. The recent connections between RO and problems in statistics and machine learning domains demand for solving RO problems in ever larger scales. However, the traditional approaches for solving RO formulations based on building and solving robust counterparts or the iterative approaches utilizing nominal feasibility oracles can be prohibitively expensive and thus significantly hinder ...
• Tractable Sampling Strategies for Ordinal Optimization AI翻译
  作者：Shin, Dongwook; Broadie, Mark; Zeevi, Assaf
  作者单位：Hong Kong University of Science & Technology; Columbia University
  摘要：We consider a problem of ordinal optimization where the objective is to select the best of several competing alternatives (systems) when the probability distributions governing each system's performance are not known but can be learned via sampling. The objective is to dynamically allocate samples within a finite sampling budget to minimize the probability of selecting a system that is not the best. This objective does not possess an analytically tractable solution. We introduce a family of pr...
• Single Observation Adaptive Search for Continuous Simulation Optimization AI翻译
  作者：Kiatsupaibul, Seksan; Smith, Robert L.; Zabinsky, Zelda B.
  作者单位：Chulalongkorn University; University of Michigan System; University of Michigan; University of Washington; University of Washington Seattle
  摘要：Optimizing the performance of complex systems modeled by stochastic computer simulations is a challenging task, partly because of the lack of structural properties (e.g., convexity). This challenge is magnified by the presence of random error whereby an adaptive algorithm searching for better designs can at times mistakenly accept an inferior design. In contrast to performing multiple simulations at a design point to estimate the performance of the design, we propose a framework for adaptive s...
• Throughput Scalability Analysis of Fork-Join Queueing Networks AI翻译
  作者：Zeng, Yun; Chaintreau, Augustin; Towsley, Don; Xia, Cathy H.
  作者单位：University System of Ohio; Ohio State University; Columbia University; University of Massachusetts System; University of Massachusetts Amherst
  摘要：Parallel and distributed processing systems have expanded in size as technology advances in cloud computing and big data analytics. A critical issue concerns throughput scalability: whether throughput decreases to zero as the systems scale in size and capabilities. We model parallel and distributed processing systems as fork and join queueing networks with blocking (FJQN/Bs). Such networks can have arbitrary topology, arbitrary initial state, and generally distributed service times. We propose...