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作者：Pesenti, Silvana M.; Jaimungal, Sebastian; Saporito, Yuri F.; Targino, Rodrigo S.
作者单位：University of Toronto; University of Oxford; Getulio Vargas Foundation
摘要：We define and develop an approach for risk budgeting allocation-a risk diversification portfolio strategy-where risk is measured using a dynamic time-consistent risk measure. For this, we introduce a notion of dynamic risk contributions that generalize the classical Euler contributions, which allows us to obtain dynamic risk contributions in a recursive manner. We prove that for the class of coherent dynamic distortion risk measures, the risk allocation problem may be recast as a sequence of s...
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作者：Aveklouris, Angelos; DeValve, Levi; Stock, Maximiliano; Ward, Amy
作者单位：University of Chicago
摘要：Service platforms must determine rules for matching heterogeneous demand (customers) and supply (workers) that arrive randomly over time and may be lost if forced to wait too long for a match. Our objective is to maximize the cumulative value of matches, minus costs incurred when demand and supply wait. We develop a fluid model, that approximates the evolution of the stochastic model and captures explicitly the nonlinear dependence between the amount of demand and supply waiting and the distri...
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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...
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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...
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作者：Li, Weiyuan; Rusmevichientong, Paat; Topaloglu, Huseyin
作者单位：University of Southern California
摘要：When modeling the demand in revenue management systems, a natural approach is to focus on a canonical interval of time, such as a week, so that we forecast the demand over each week in the selling horizon. Ideally, we would like to use random variables with general distributions to model the demand over each week. The current demand can give a signal for the future demand, so we also would like to capture the dependence between the demands over different weeks. Prevalent demand models in the l...
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作者：Besbes, Omar; Kanoria, Yash; Kumar, Akshit
作者单位：Columbia University
摘要：Dynamic resource allocation problems are ubiquitous, arising in inventory management, order fulfillment, online advertising, and other applications. We initially focus on one of the simplest models of online resource allocation: the multisecretary problem. In the multisecretary problem, a decision maker sequentially hires up to B out of T candidates, and candidate ability values are independently and identically distributed from a distribution F on [ 0, 1 ] . First, we investigate fundamental ...
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作者：den Hertog, Dick; Pauphilet, Jean; Pham, Yannick; Sainte-Rose, Bruno; Song, Baizhi
作者单位：University of Amsterdam; University of London; London Business School
摘要：Increasing ocean plastic pollution is irreversibly harming ecosystems and human economic activities. We partner with a nonprofit organization and use optimization to help clean up oceans from plastic faster. Specifically, we optimize the route of their plastic collection system in the ocean to maximize the quantity of plastic collected over time. We formulate the problem as a longest path problem in a well-structured graph. However, because collection directly impacts future plastic density, t...
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作者：Xie, Tong; Wang, Zizhuo
作者单位：University of Chicago; The Chinese University of Hong Kong, Shenzhen
摘要：In this paper, we introduce a consumer choice model where each consumer's utility is affected by their neighbors' purchase probabilities in a network. We first characterize the choice probabilities in this model and then consider the associated personalized assortment optimization problem. Although this problem is NP-hard, we show that for star networks, the optimal assortment to the central consumer and peripheral consumers cannot be strictly larger than that without network effects, and the ...
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作者：Zhang, Jingwei; Ma, Will; Topaloglu, Huseyin
作者单位：The Chinese University of Hong Kong, Shenzhen; Columbia University; Columbia University
摘要：We study the joint assortment and inventory planning problem with stockoutbased substitution. In this problem, we pick the number of units to stock for the products at the beginning of the selling horizon. Each arriving customer makes a choice among the set of products with remaining on-hand inventories. Our goal is to pick the stocking quantities to maximize the total expected revenue from the sales net of the stocking cost. We develop a rounding scheme that uses the solution to a fluid appro...
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作者：Hu, Jiaqiao; Song, Meichen; Fu, Michael C.
作者单位：State University of New York (SUNY) System; Stony Brook University; University System of Maryland; University of Maryland College Park; University System of Maryland; University of Maryland College Park
摘要：We consider quantile optimization of black-box functions that are estimated with noise. We propose two new iterative three-timescale local search algorithms. The first algorithm uses an appropriately modified finite-difference-based gradient estimator that requires 2d + 1 samples of the black-box function per iteration of the algorithm, where d is the number of decision variables (dimension of the input vector). For higher-dimensional problems, this algorithm may not be practical if the black-...
