Operations Research

最新文章

• Operations Research: The Next Three Years AI翻译
  作者：Zenios, Stefanos
  作者单位：Stanford University
• Optimizing Long-Term Production Plans in Underground and Open-Pit Copper Mines AI翻译
  作者：Epstein, Rafael; Goic, Marcel; Weintraub, Andres; Catalan, Jaime; Santibanez, Pablo; Urrutia, Rodolfo; Cancino, Raul; Gaete, Sergio; Aguayo, Augusto; Caro, Felipe
  作者单位：Universidad de Chile; University of California System; University of California Los Angeles
  摘要：We present a methodology for long-term mine planning based on a general capacitated multicommodity network flow formulation. It considers underground and open-pit ore deposits sharing multiple downstream processing plants over a long horizon. The purpose of the model is to optimize several mines in an integrated fashion, but real size instances are hard to solve due to the combinatorial nature of the problem. We tackle this by solving the relaxation of a tight linear formulation, and we round ...
• Approximation Algorithms for VRP with Stochastic Demands AI翻译
  作者：Gupta, Anupam; Nagarajan, Viswanath; Ravi, R.
  作者单位：Carnegie Mellon University; International Business Machines (IBM); IBM USA; Carnegie Mellon University
  摘要：We consider the vehicle routing problem with stochastic demands (VRPSD). We give randomized approximation algorithms achieving approximation guarantees of 1 + alpha for split-delivery VRPSD, and 2 + alpha for unsplit-delivery VRPSD; here alpha is the best approximation guarantee for the traveling salesman problem. These bounds match the best known for even the respective deterministic problems [Altinkemer, K., B. Gavish. 1987. Heuristics for unequal weight delivery problems with a fixed error ...
• The Knowledge Gradient Algorithm for a General Class of Online Learning Problems AI翻译
  作者：Ryzhov, Ilya O.; Powell, Warren B.; Frazier, Peter I.
  作者单位：University System of Maryland; University of Maryland College Park; Princeton University; Cornell University
  摘要：We derive a one-period look-ahead policy for finite- and infinite-horizon online optimal learning problems with Gaussian rewards. Our approach is able to handle the case where our prior beliefs about the rewards are correlated, which is not handled by traditional multiarmed bandit methods. Experiments show that our KG policy performs competitively against the best-known approximation to the optimal policy in the classic bandit problem, and it outperforms many learning policies in the correlate...
• Nurse Staffing in Medical Units: A Queueing Perspective AI翻译
  作者：de Vericourt, Francis; Jennings, Otis B.
  作者单位：INSEAD Business School; Duke University
  摘要：In this paper, we present a closed queueing model to determine efficient nurse staffing policies. We explicitly model the workload experienced by s nurses within a single medical unit with n homogeneous patients as a closed M / M / s / / n queueing system, where each patient alternates between requiring assistance and not. The performance of the medical unit is based on the probability of excessive delay, the relative frequency with which the delay between the onset of patient neediness and th...
• Generation Capacity Expansion in a Risky Environment: A Stochastic Equilibrium Analysis AI翻译
  作者：Ehrenmann, Andreas; Smeers, Yves
  作者单位：Engie; Universite Catholique Louvain; Universite Catholique Louvain
  摘要：We cast models of the generation capacity expansion type formally developed for the monopoly regime into equilibrium models better adapted for a competitive environment. We focus on some of the risks faced today by investors in generation capacity and thus pose the problem as a stochastic equilibrium model. We illustrate the approach on the problem of the incentive to invest. Agents can be risk neutral or risk averse. We model risk aversion through the CVaR of plants' profit. The CVaR induces ...
• Dynamic Pricing with Loss-Averse Consumers and Peak-End Anchoring AI翻译
  作者：Nasiry, Javad; Popescu, Ioana
  作者单位：Hong Kong University of Science & Technology; INSEAD Business School
  摘要：We study the dynamic pricing implications of a new, behaviorally motivated reference price mechanism based on the peak-end memory mode. This model suggests that consumers anchor on a reference price that is a weighted average of the lowest and most recent prices. Loss-averse consumers are more sensitive to perceived losses than gains relative to this reference price. We find that a range of constant pricing policies is optimal for the corresponding dynamic pricing problem. This range is wider ...
• Integrated Optimization of Procurement, Processing, and Trade of Commodities AI翻译
  作者：Devalkar, Sripad K.; Anupindi, Ravi; Sinha, Amitabh
  作者单位：Indian School of Business (ISB); University of Michigan System; University of Michigan
  摘要：We consider the integrated optimization problem of procurement, processing, and trade of commodities in a multiperiod setting. Motivated by the operations of a prominent commodity processing firm, we model a firm that procures an input commodity and has processing capacity to convert the input into a processed commodity. The processed commodity is sold using forward contracts, while the input itself can be traded at the end of the horizon. We solve this problem optimally and derive closed-form...
• We Will Be Right with You: Managing Customer Expectations with Vague Promises and Cheap Talk AI翻译
  作者：Allon, Gad; Bassamboo, Achal; Gurvich, Itai
  作者单位：Northwestern University
  摘要：Delay announcements informing customers about anticipated service delays are prevalent in service-oriented systems. How delay announcements can influence customers in service systems is a complex problem that depends on both the dynamics of the underlying queueing system and on the customers' strategic behavior. We examine this problem of information communication by considering a model in which both the firm and the customers act strategically: the firm in choosing its delay announcement whil...
• Efficient Simulation of Value at Risk with Heavy-Tailed Risk Factors AI翻译
  作者：Fuh, Cheng-Der; Hu, Inchi; Hsu, Ya-Hui; Wang, Ren-Her
  作者单位：National Central University; Hong Kong University of Science & Technology; Abbott Laboratories; Tamkang University
  摘要：Simulation of small probabilities has important applications in many disciplines. The probabilities considered in value-at-risk (VaR) are moderately small. However, the variance reduction techniques developed in the literature for VaR computation are based on large-deviations methods, which are good for very small probabilities. Modeling heavy-tailed risk factors using multivariate t distributions, we develop a new method for VaR computation. We show that the proposed method minimizes the vari...