31 citations · 96 across the 18 of their papers we have counts for
18 papers
OptiRepair: Closed-Loop Diagnosis and Repair of Supply Chain Optimization Models with LLM Agents
Ruicheng Ao, David Simchi-Levi, Xinshang Wang
Supply chain optimization models frequently become infeasible because of modeling errors. Diagnosis and repair require scarce OR expertise: analysts must interpret solver diagnosti…
ORLoopBench: Solver-in-the-Loop Benchmarks for Self-Correction and Behavioral Rationality in Operations Research
Ruicheng Ao, David Simchi-Levi, Xinshang Wang
Operations Research practitioners debug infeasible models through an iterative process: inspecting Irreducible Infeasible Subsystems ( IIS), identifying constraint conflicts, and r…
Optimizing LLM Inference: Fluid-Guided Online Scheduling with Memory Constraints
Ruicheng Ao, Gan Luo, David Simchi-Levi +1
Large language models now serve millions of users daily, with providers incurring costs exceeding $700,000 per day. Each request requires token-by-token inference, making GPU sched…
DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models
Zeping Min, Xinshang Wang
We introduce a novel index, the Distribution of Cosine Similarity (DOCS), for quantitatively assessing the similarity between weight matrices in Large Language Models (LLMs), aimin…
Expressive Power of Graph Neural Networks for (Mixed-Integer) Quadratic Programs
Ziang Chen, Xiaohan Chen, Jialin Liu +2
Quadratic programming (QP) is the most widely applied category of problems in nonlinear programming. Many applications require real-time/fast solutions, though not necessarily with…
Rethinking the Capacity of Graph Neural Networks for Branching Strategy
Ziang Chen, Jialin Liu, Xiaohan Chen +2
Graph neural networks (GNNs) have been widely used to predict properties and heuristics of mixed-integer linear programs (MILPs) and hence accelerate MILP solvers. This paper inves…
Efficient Algorithms for Sum-of-Minimum Optimization
Lisang Ding, Ziang Chen, Xinshang Wang +1
In this work, we propose a novel optimization model termed "sum-of-minimum" optimization. This model seeks to minimize the sum or average of objective functions over parame…
DIG-MILP: a Deep Instance Generator for Mixed-Integer Linear Programming with Feasibility Guarantee
Haoyu Wang, Jialin Liu, Xiaohan Chen +3
Mixed-integer linear programming (MILP) stands as a notable NP-hard problem pivotal to numerous crucial industrial applications. The development of effective algorithms, the tuning…
On Representing Mixed-Integer Linear Programs by Graph Neural Networks
Ziang Chen, Jialin Liu, Xinshang Wang +2
While Mixed-integer linear programming (MILP) is NP-hard in general, practical MILP has received roughly 100--fold speedup in the past twenty years. Still, many classes of MILPs qu…
On Representing Linear Programs by Graph Neural Networks
Ziang Chen, Jialin Liu, Xinshang Wang +2
Learning to optimize is a rapidly growing area that aims to solve optimization problems or improve existing optimization algorithms using machine learning (ML). In particular, the…
Near-Optimal Primal-Dual Algorithms for Quantity-Based Network Revenue Management
Rui Sun, Xinshang Wang, Zijie Zhou
We study the canonical quantity-based network revenue management (NRM) problem where the decision-maker must irrevocably accept or reject each arriving customer request with the go…
Shrinking the Upper Confidence Bound: A Dynamic Product Selection Problem for Urban Warehouses
Rong Jin, David Simchi-Levi, Li Wang +2
The recent rising popularity of ultra-fast delivery services on retail platforms fuels the increasing use of urban warehouses, whose proximity to customers makes fast deliveries vi…
Prophet Inequality with Correlated Arrival Probabilities, with Application to Two Sided Matchings
Van-Anh Truong, Xinshang Wang
The classical Prophet Inequality arises from a fundamental problem in optimal-stopping theory. In this problem, a gambler sees a finite sequence of independent, non-negative random…
The Lingering of Gradients: Theory and Applications
Zeyuan Allen-Zhu, David Simchi-Levi, Xinshang Wang
Classically, the time complexity of a first-order method is estimated by its number of gradient computations. In this paper, we study a more refined complexity by taking into accou…
Inventory Balancing with Online Learning
Wang Chi Cheung, Will Ma, David Simchi-Levi +1
We study a general problem of allocating limited resources to heterogeneous customers over time under model uncertainty. Each type of customer can be serviced using different actio…
Online Advance Admission Scheduling for Services with Customer Preferences
Xinshang Wang, Van-Anh Truong, David Bank
We study web and mobile applications that are used to schedule advance service, from medical appointments to restaurant reservations. We model them as online weighted bipartite mat…
Advance Service Reservations with Heterogeneous Customers
Clifford Stein, Van-Anh Truong, Xinshang Wang
We study a fundamental model of resource allocation in which a finite number of resources must be assigned in an online manner to a heterogeneous stream of customers. The customers…
Online Resource Allocation with Customer Choice
Guillermo Gallego, Anran Li, Van-Anh Truong +1
We introduce a general model of resource allocation with customer choice. In this model, there are multiple resources that are available over a finite horizon. The resources are no…