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This paper introduces a learning-based method to accelerate Mixed-Integer Linear Programming solvers by predicting early-to-final solution consistency, achieving substantial performance gains with Gurobi and zero-shot transfer to SCIP.
This paper introduces the Travelling Thief Problem with Drone (TTP-D), which jointly optimizes ground routing, drone synchronization, and item selection using mixed-integer programming, metaheuristics, and attention-based deep reinforcement learning.
This paper presents two sound encodings (MIP and SMT) for multi-agent path planning with spatio-temporal and topological constraints expressed in STL-GO, and evaluates them on a multi-UAV search-and-rescue benchmark.
This paper formulates the Foundation Model Deployment Portfolio problem to minimize total cost of ownership for deploying LLMs and VLMs across transportation management functions, considering quality, latency, and safety constraints. A case study shows cost savings of 97% compared to all-closed-API baselines.
Proposes and compares two mathematical formulations for robust microgrid sizing and power scheduling under uncertainties, using a local reduction algorithm that achieves high feasibility rates in Monte Carlo simulations.
This paper proposes Mixed Integer Goal Programming (MIGP) for personalized meal optimization, addressing the limitations of fractional servings and infeasibility from hard constraints. It introduces integer variables for practical serving counts and goal programming deviations with inverse-target normalization, achieving 100% feasibility and better solutions than rounding-based approaches, with an open-source Python implementation.