Operations Research

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• A Doubly Stochastic Simulator with Applications in Arrivals Modeling and Simulation AI翻译
  作者：Zheng, Yufeng; Zheng, Zeyu; Zhu, Tingyu
  作者单位：University of Toronto; University of California System; University of California Berkeley
  摘要：We propose a framework that integrates classic Monte Carlo simulators and Wasserstein generative adversarial networks to model, estimate, and simulate a broad class of arrival processes with general nonstationary and multidimensional random arrival rates. Classic Monte Carlo simulators have advantages in capturing the interpretable physics of a stochastic object, whereas neural network-based simulators have advantages in capturing less interpretable complicated dependence within a high-dimensi...
• A Stationary Infinite-Horizon Supply Contract Under Asymmetric Inventory Information AI翻译
  作者：Bensoussan, Alain; Sethi, Suresh; Wang, Shouqiang
  作者单位：University of Texas System; University of Texas Dallas; City University of Hong Kong
  摘要：We consider a decentralized supply chain in which a supplier sells goods to a retailer facing general random demand over an infinite horizon. The retailer satisfies the demand to the extent of the inventory on hand. The retailer has private information about the retailer's stock in each period, and the supplier offers the retailer a supply contract menu to account for the information asymmetry. We obtain a necessary condition for optimizing a long-term stationary truth-telling contract under g...
• Exponential Concentration in Stochastic Approximation AI翻译
  作者：Law, Kody . T. H.; Walton, Neil; Yang, Shangda
  作者单位：University of Manchester; Durham University
  摘要：We analyze the behavior of stochastic approximation algorithms where iterates, in expectation, progress toward an objective at each step. When progress is proportional to the step size of the algorithm, we prove exponential concentration bounds. These tailbounds contrast asymptotic normality results, which are more frequently associated with stochastic approximation. The methods that we develop rely on a proof of geometric ergodicity. The extends results on the exponential ergodicity of Markov...
• Crosscutting Bayesian Mechanism Design for Blockchain Transaction Fee Allocation AI翻译
  作者：Chen, Xi; Simchi-Levi, David; Zhao, Zishuo; Zhou, Yuan
  作者单位：New York University; Massachusetts Institute of Technology (MIT); University of Illinois System; University of Illinois Urbana-Champaign; Tsinghua University; Tsinghua University
  摘要：In blockchain systems, the design of transaction fee mechanisms (TFMs) is essential for stability and satisfaction for both miners and users. A recent work has proven the impossibility of collusion-proof mechanisms that achieve both nonzero miner revenue and Dominant Strategy Incentive Compatibility (DSIC) for users. However, a positive miner revenue is important in practice to motivate miners. To address this challenge, we consider a Bayesian game setting and relax the DSIC requirement for us...
• Technical Note: Multistage Robust Mixed-Integer Programming AI翻译
  作者：Postek, Krzysztof; Romeijnders, Ward; Wiesemann, Wolfram
  作者单位：Delft University of Technology; University of Groningen; Imperial College London
  摘要：Multistage robust optimization, in which decisions are taken sequentially as new information becomes available about uncertain problem parameters, is a very versatile yet computationally challenging paradigm for decision making under uncertainty. In this technical note, we propose a new model and solution approach for multistage robust mixed-integer programs, which may contain both continuous and discrete decisions at any time stage. Our model builds upon the finite adaptability scheme develop...
• Joint Bottom-up Method for Probabilistic Forecasting of Hierarchical Time Series AI翻译
  作者：Bertani, Nicollo; Jensen, Shane T.; Satopaa, Ville A.
  作者单位：Universidade Catolica Portuguesa; University of Pennsylvania; INSEAD Business School
  摘要：This article may be used only for the purposes of research, teaching, and/or private study. Commercial use or systematic downloading (by robots or other automatic processes) is prohibited without explicit Publisher approval, unless otherwise noted. For more information, contact permissions@informs.org. The Publisher does not warrant or guarantee the article's accuracy, completeness, merchantability, fitness inclusion of an advertisement in this article, neither constitutes nor implies a guaran...
• Dynamic Relocations in Car-Sharing Networks AI翻译
  作者：Hosseini, Mahsa; Milner, Joseph; Romero, Gonzalo
  作者单位：University of Windsor; University of Toronto
  摘要：We propose a novel dynamic car relocation policy for a car -sharing network with centralized control and uncertain, unbalanced demand. The policy is derived from a reformulation of the linear programming fluid model approximation of the dynamic problem. We project the full -dimensional fluid approximation onto the lowerdimensional space of relocation decisions only. This projection results in a characterization of the problem as n + 1 linear programs, where n is the number of nodes in the netw...
• Gaussian Process-Based Random Search for Continuous Optimization via Simulation AI翻译
  作者：Wang, Xiuxian; Hong, L. Jeff; Jiang, Zhibin; Shen, Haihui
  作者单位：Shanghai Jiao Tong University; Fudan University; Fudan University; Shanghai Jiao Tong University
  摘要：Random search is an important category of algorithms to solve continuous optimization via simulation problems. To design an efficient random search algorithm, the handling of the triple E (i.e., exploration, exploitation and estimation) is critical. The first two E's refer to the design of sampling distribution to balance explorative and exploitative searches, whereas the third E refers to the estimation of objective function values based on noisy simulation observations. In this paper, we pro...
• Technical Note-On Adaptivity in Nonstationary Stochastic Optimization with Bandit Feedback AI翻译
  作者：Wang, Yining
  作者单位：University of Texas System; University of Texas Dallas
  摘要：In this paper, we study the nonstationary stochastic optimization problem with bandit feedback and dynamic regret measures. The seminal work of Besbes et al. (2015) shows that, when aggregated function changes are known a priori, a simple restarting algorithm attains the optimal dynamic regret. In this work, we design a stochastic optimi-zation algorithm with fixed step sizes, which, combined with the multiscale sampling framework in existing research, achieves the optimal dynamic regret in no...
• Split Liver Transplantation: An Analytical Decision Support Model AI翻译
  作者：Tang, Yanhan (Savannah); Scheller-Wolf, Alan; Tayur, Sridhar; Perito, Emily R.; Roberts, John P.
  作者单位：Southern Methodist University; Carnegie Mellon University; University of California System; University of California San Francisco
  摘要：Split liver transplantation (SLT) is a procedure that potentially saves two lives using one liver, increasing the total benefit derived from the limited number of donated livers available. SLT may also improve equity by giving transplant candidates who are physically smaller (including children) increased access to liver transplants. However, SLT is rarely used in the United States. To help quantify the benefits of increased SLT utilization and provide decision support tools, we introduce a de...