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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...
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作者：Sun, Xu; Liu, Weiliang
作者单位：University of Miami; National University of Singapore
摘要：An on-demand workforce can greatly benefit a traditional call center by allowing it to adjust its service capacity on demand quickly. Despite its conceptual elegance, the operationalization of this process is challenging due to the various sources of randomness involved. The purpose of this paper is to help call centers enhance service levels while keeping operating expenses low by taking advantage of an on-call pool of temporary agents in day-to-day operations. For that purpose, we develop a ...
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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...
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作者：Simchi-Levi, David; Sun, Rui; Wang, Xinshang
作者单位：Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT); Shanghai Jiao Tong University
摘要：We study in this paper an online matching problem where a central platform needs to match a number of limited resources to different groups of users that arrive sequentially over time. The reward of each matching option depends on both the type of resource and the time period the user arrives. The matching rewards are assumed to be unknown but drawn from probability distributions that are known a priori. The platform then needs to learn the true rewards online based on real-time observations o...
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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...
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作者：Meng, Xiaochun; Taylor, James W.; Taieb, Souhaib Ben; Li, Siran
作者单位：University of Sussex; University of Oxford; University of Mons; Shanghai Jiao Tong University; Shanghai Jiao Tong University
摘要：Forecasts of multivariate probability distributions are required for a variety of applications. Scoring rules enable the evaluation of forecast accuracy and comparison between forecasting methods. We propose a theoretical framework for scoring rules for multivariate distributions that encompasses the existing quadratic score and multivariate continuous ranked probability score. We demonstrate how this framework can be used to generate new scoring rules. In some multivariate contexts, it is a f...
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作者：Deo, Anand; Murthy, Karthyek
作者单位：Indian Institute of Management (IIM System); Indian Institute of Management Bangalore; Singapore University of Technology & Design
摘要：This paper presents a novel importance sampling (IS) scheme for estimating distribution tails of performance measures modeled with a rich set of tools, such as linear programs, integer linear programs, piecewise linear/quadratic objectives, feature maps specified with deep neural networks, etc. The conventional approach of explicitly identifying efficient changes of measure suffers from feasibility and scalability concerns beyond highly stylized models because of their need to be tailored intr...
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作者：Kilinc-Karzan, Fatma; Kucukyavuz, Simge; Lee, Dabeen; Shafieezadeh-Abadeh, Soroosh
作者单位：Carnegie Mellon University; Northwestern University; Korea Advanced Institute of Science & Technology (KAIST)
摘要：We consider a general conic mixed-binary set where each homogeneous conic constraint j involves an affine function of independent continuous variables and an epigraph variable associated with a nonnegative function, fj, of common binary variables. Sets of this form naturally arise as substructures in a number of applications, including mean-risk optimization, chance-constrained problems, portfolio optimization, lot sizing and scheduling, fractional programming, variants of the best subset sele...
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作者：Eckstein, Jonathan; Watson, Jean-Paul; Woodruff, David L.
作者单位：Rutgers University System; Rutgers University New Brunswick; Rutgers University Newark; United States Department of Energy (DOE); Lawrence Livermore National Laboratory; University of California System; University of California Davis
摘要：We propose a decomposition algorithm for multistage stochastic programming that resembles the progressive hedging method of Rockafellar and Wets but is provably capable of several forms of asynchronous operation. We derive the method from a class of projective operator splitting methods fairly recently proposed by Combettes and Eckstein, significantly expanding the known applications of those methods. Our derivation assures convergence for convex problems whose feasible set is compact, subject...
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作者：Zhong, Yueyang; Gopalakrishnan, Ragavendran; Ward, Amy R.
作者单位：University of Chicago; Queens University - Canada
摘要：Service system design is often informed by queueing theory. Traditional queueing theory assumes that servers work at constant speeds. That is reasonable in computer science and manufacturing contexts. However, servers in service systems are people, and in contrast to machines, the incentives created by design decisions influence their work speeds. We study how server work speed is affected by managerial decisions concerning (i) how many servers to staff and how much to pay them and (ii) whethe...
