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作者：Chen, Du; Chua, Geoffrey A.
作者单位：Nanyang Technological University
摘要：Data are now widely considered a key firm asset for enabling better operational decisions. However, data-driven decisions can inadvertently expose private data, leaving firms vulnerable to unforeseen danger. How to manage data security risks by protecting data from being inferred from observable decisions thus becomes an important question. In this paper, we focus on data security in supply chains because of their data-intensive nature. Specifically, we examine a data-driven contextual newsven...
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作者：Xia, Jun; Xu, Zhou; Baldacci, Roberto
作者单位：Shanghai Jiao Tong University; Hong Kong Polytechnic University; Qatar Foundation (QF); Hamad Bin Khalifa University-Qatar
摘要：The liner shipping network design (LSND) problem involves creating regular ship rotations to transport containerized cargo between seaports. The objective is to maximize carrier profit by balancing revenue from satisfied demand against operating and transshipment costs. Finding an optimal solution is challenging because of complex rotation structures and joint decisions on fleet deployment, cargo routing, and rotation design. This work introduces a set partitioning-like formulation for LSND wi...
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作者：Jiang, Jiashuo; Ma, Will; Zhang, Jiawei
作者单位：Hong Kong University of Science & Technology; Columbia University; New York University
摘要：We study the classic network revenue management (NRM) problem with accept/ reject decisions and T independent and identically distributed arrivals. We consider a distributional form in which each arrival must fall under a finite number of possible categories, each with a deterministic resource consumption vector, but a random value distributed continuously over an interval. We develop an online algorithm that achieves O(log2 T) regret under this model with the only (necessary) assumption being...
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作者：Gorissen, Bram L.; den Hertog, Dick; Reusken, Meike
作者单位：Harvard University; Harvard University Medical Affiliates; Massachusetts General Hospital; Harvard University; Harvard Medical School; Harvard University; Massachusetts Institute of Technology (MIT); Broad Institute; University of Amsterdam; Tilburg University; Wageningen University & Research
摘要：In this paper we identify a new class of nonconvex optimization problems that can be equivalently reformulated to convex ones. These nonconvex problems can be characterized by convex functions with bilinear arguments. We describe several examples of important applications that have this structure. These include the problems with variable coefficients, the dual of robust nonlinear optimization problems that are convex in the optimization variables and concave in the uncertain parameters, and in...
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作者：Huang, Chenyu; Tang, Zhengyang; Hu, Shixi; Jiang, Ruoqing; Zheng, Xin; Ge, Dongdong; Wang, Benyou; Wang, Zizhuo
作者单位：Shanghai University of Finance & Economics; The Chinese University of Hong Kong, Shenzhen; The Chinese University of Hong Kong, Shenzhen; Shenzhen Research Institute of Big Data; Columbia University; Tsinghua University; Duke University; Shanghai Jiao Tong University; The Chinese University of Hong Kong, Shenzhen
摘要：Optimization modeling plays a critical role in the application of Operations Research (OR) tools to address real-world problems, yet they pose challenges and require extensive expertise from OR experts. With the advent of large language models (LLMs), new opportunities have emerged to streamline and automate such tasks. However, current research predominantly relies on closed-source LLMs, such as GPT-4, along with extensive prompt engineering techniques. This reliance stems from the scarcity o...
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作者：Guo, Xin; Wang, Binnan; Zhang, Ruixun; Zhao, Chaoyi
作者单位：University of California System; University of California Berkeley; Peking University; Peking University; Peking University; Peking University; Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT)
摘要：Signatures are iterated path integrals of continuous and discrete-time processes, and their universal nonlinearity linearizes the problem of feature selection in time series data analysis. This paper studies the consistency of signature using Lasso regression, both theoretically and numerically. We establish conditions under which the Lasso regression is consistent both asymptotically and in finite sample. Furthermore, we show that the Lasso regression is more consistent with the Ito signature...
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作者：Wang, Hanzhao; Talluri, Kalyan; Li, Xiaocheng
作者单位：Imperial College London
摘要：We consider dynamic pricing with covariates under a generalized linear demand model: A seller can dynamically adjust the price of a product over a horizon of T time periods, and at each time period t, the demand of the product is jointly determined by the price and an observable covariate vector xt is an element of Rd through a generalized linear model with unknown coefficients. Most of the existing literature assumes the covariate vectors xts are independently and identically distributed (i.i...
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作者：Li, Zihao; Wang, Hao; Yan, Zhenzhen
作者单位：National University of Singapore; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Nanyang Technological University; Nanyang Technological University
摘要：We study a fully online matching problem with general stochastic arrivals and departures. In this model, each online arrival follows a known identical and independent distribution over a fixed set of agent types. Its sojourn time is unknown in advance and follows type-specific distributions with known expectations. The goal is to maximize the weighted reward from successful matches. To solve this problem, we propose a linear program (LP)-based algorithm whose competitive ratio is lower bounded...
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作者：Simchi-Levi, David; Xu, Yunzong; Zhao, Jinglong
作者单位：Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT); University of Illinois System; University of Illinois Urbana-Champaign; Boston University
摘要：This paper studies the impact of limited switches on resource-constrained dynamic pricing with demand learning. We focus on the classical price-based blind network revenue management problem and extend our results to the bandits with knapsacks problem. In both settings, a decision maker faces stochastic and distributionally unknown demand, and must allocate finite initial inventory across multiple resources over time. In addition to standard resource constraints, we impose a switching constrai...
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作者：Ba, Wenjia; Lin, Tianyi; Zhang, Jiawei; Zhou, Zhengyuan
作者单位：University of British Columbia; Columbia University; New York University
摘要：We consider online no-regret learning in unknown games with bandit feedback, where each player can only observe its reward at each time-determined by all players' current joint action-rather than its gradient. We focus on the class of smooth and strongly monotone games and study optimal no-regret learning therein. Leveraging self-concordant barrier functions, we first construct a new bandit learning algorithm and show that it root ffiffiffi achieves the single-agent optimal regret of Theta ( n...
