Mixed Integer Linear Programming
PulseAugur coverage of Mixed Integer Linear Programming — every cluster mentioning Mixed Integer Linear Programming across labs, papers, and developer communities, ranked by signal.
-
New theory links ML to Lagrangian Relaxation for MILP
Researchers have developed a theoretically grounded method for using machine learning to improve Lagrangian Relaxation (LR) for Mixed Integer Linear Programming (MILP). The new approach, framed as Data-driven Algorithm …
-
Hybrid CDCL and CP-SAT architecture accelerates facility layout optimization
Researchers have developed a hybrid architecture combining Conflict-Driven Clause Learning (CDCL) and CP-SAT solvers to accelerate discrete facility layout optimization. While CDCL excels at quickly finding feasible sol…
-
New CP method optimizes counterfactual explanations for tree ensembles
Researchers have developed a new constraint programming (CP) formulation called CPCF for computing optimal counterfactual explanations in tree ensembles. This method encodes numerical features as interval domains and di…
-
研究人员开发用于电动汽车叫车服务的半马尔可夫强化学习，提高利润并确保可行性。
研究人员开发了一种新颖的半马尔可夫强化学习方法，用于管理大规模电动汽车叫车车队。该方法确保调度、重新定位和充电决策严格遵守充电器和馈线限制等物理约束，即使在需求和出行时间不确定的情况下也是如此。该系统利用掩码执行器产生高级意图，然后通过混合整数线性规划进行投影以保证可行性。在纽约市出租车数据集模拟器上的实验表明，名为 PD--RSAC 的方法显著优于基线方法，净利润达到 122 万美元，同时避免了任何馈线限制违规。
-
Researchers develop new training methods for neural networks to improve MILP tractability
Researchers have developed new training regularizers for neural network surrogate models that directly improve their tractability within mixed-integer linear programs (MILPs). These regularizers penalize factors like bi…
-
Deep learning framework accelerates electricity grid unit commitment
Researchers have developed a new deep learning framework to address the complex Unit Commitment (UC) problem in electricity grids. This transformer-based approach predicts generator schedules over a 72-hour horizon, inc…
-
AI framework blends LSTM and MILP for improved supply chain forecasting and optimization
Researchers have developed a novel Hybrid AI Framework for Demand-Supply Forecasting and Optimization (HAF-DS) to improve supply chain efficiency in volatile industries. This framework integrates a Long Short-Term Memor…