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作者：He, Taotao; Zhang, Yating; Zheng, Huan
作者单位：Shanghai Jiao Tong University
摘要：This paper examines how to plan multiperiod assortments when customer utility depends on historical assortments. We formulate this problem as a nonlinear integer programming model and show it is NP-hard in the presence of a negative historydependent effect (such as a satiation effect). We build solution methodologies for obtaining global optimal solutions under a general setting where the history-dependent effects could be a mixture of positive and negative. We propose using a lifting-based fr...
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作者：Xu, Yihua; Kerimov, Seleyman; Perez-Salazar, Sebastian
作者单位：Rice University; Rice University; Rice University
摘要：In numerous online selection problems, decision makers (DMs) must allocate on the fly limited resources to customers with uncertain values. The DM faces the tension between allocating resources to currently observed values and saving them for potentially better, unobserved values in the future. Addressing this tension becomes more demanding if an uncertain disruption occurs while serving customers. Without any disruption, the DM gets access to the capacity information to serve customers throug...
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作者：Goyal, Vineet; Iyengar, Garud; Udwani, Rajan
作者单位：Columbia University; University of California System; University of California Berkeley
摘要：We consider the problem of pricing a limited inventory of substitutable products over a finite planning horizon. In our model, customers with the same choice model arrive sequentially, observe current prices, and choose at most one available product. Our goal is to find a revenue maximizing (dynamic) pricing policy subject to a crucial show-all constraint that requires that every available product must always be displayed at a finite, feasible price. Although a relaxation of our setting where ...
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作者：Liang, Yong; Mao, Xiaojie; Wang, Shiyuan
作者单位：Tsinghua University; Tsinghua University; Shanghai University of Finance & Economics
摘要：We study an online joint assortment-inventory optimization problem, in which we assume that the choice behavior of each customer follows the multinomial logit (MNL) choice model, and the attraction parameters are unknown a priori. The retailer makes periodic assortment and inventory decisions to dynamically learn from the customer choice observations about the attraction parameters while maximizing the expected total profit over time. In this paper, we propose a novel algorithm that can effect...
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作者：Sekar, Shreyas; Siddiq, Auyon
作者单位：University of Toronto; University Toronto Scarborough; University of Toronto; University of California System; University of California Los Angeles
摘要：Two-sided platforms, such as labor marketplaces for hiring freelancers, typically generate revenue by matching prospective buyers and sellers and extracting commissions from completed transactions. Disintermediation, where sellers transact off-platform with buyers to bypass commission fees, can undermine the viability of these marketplaces. Although circumventing the platform allows sellers to avoid commission fees, it also leaves them fully exposed to risky buyers (given the absence of the pl...
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作者：Luo, Yiyun; Sun, Will Wei; Liu, Yufeng
作者单位：Shanghai University of Finance & Economics; Purdue University System; Purdue University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
摘要：In online retailing, the seller aims to offer assortment of items with maximized revenue. We introduce a new online learning problem called dynamic assortment selection with positioning (DAP) that additionally learns the optimal positioning within the assortment. Specifically, the customers make purchases based on the item attractiveness as the product of the position effect and unknown preference parameter through a multinomial logit choice model. We first demonstrate that any assortment-only...
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作者：Sahoo, Roshni; Wager, Stefan
作者单位：Stanford University; Stanford University
摘要：Decision makers often aim to learn a treatment assignment policy under a capacity constraint on the number of agents that they can treat. When agents can respond strategically to such policies, competition arises, complicating estimation of the optimal policy. In this paper, we study capacity-constrained treatment assignments in the presence of such interference. We consider a dynamic model in which the decision maker allocates treatments at each time step and heterogeneous agents myopically b...
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作者：Namkoong, Hongseok; Ma, Yuanzhe; Glynn, Peter W.
作者单位：Columbia University; Columbia University; Stanford University
摘要：The performance of decision policies and prediction models often deteriorates when applied to environments different from the ones seen during training. To ensure reliable operation, we analyze the stability of a system under distribution shift, which is defined as the smallest change in the underlying environment that causes the system's performance to deteriorate beyond a permissible threshold. In contrast to standard tail risk measures and distributionally robust losses that require the spe...
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作者：Li, Zhouzi; Gurushankar, Keerthana; Harchol-Balter, Mor; Scheller-Wolf, Alan
作者单位：Carnegie Mellon University; Carnegie Mellon University
摘要：Scheduling a stream of jobs whose holding cost changes over time is a classic and practical problem. Specifically, each job is associated with a holding cost (penalty), and a job's instantaneous holding cost is some nondecreasing function of its class and current age (the time it has spent in the system since its arrival). The goal is to schedule the jobs to minimize the time-average total holding cost across all jobs. The seminal paper on this problem, by Van Mieghem in 1995, introduced the g...
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作者：Cheung, Wang Chi; Lyu, Guodong
作者单位：National University of Singapore; Hong Kong University of Science & Technology
摘要：A central issue in (finite horizon) online planning problems is to synthesize the impact of real-time decisions on the subsequent states of the system and the performance in the remaining time horizon (cost-to-go function). A complete resolution often leads to intractable dynamic programming problems. We propose a computationally efficient approach to this problem that attains near-optimal performance in nonstationary environments. More specifically, we study a general class of online planning...
