5 papers
Empirical Evaluation of Policy-Based Reinforcement Learning for Dynamic Service Control in an M/M/1 Queue
Joseph Walton, Gabriel Nicolosi
While reinforcement learning has been increasingly applied to stochastic control, few studies have systematically examined policy-based methods in queuing environments modeled as a…
SynthCharge: An Electric Vehicle Routing Instance Generator with Feasibility Screening to Enable Learning-Based Optimization and Benchmarking
Mertcan Daysalilar, Fuat Uyguroglu, Gabriel Nicolosi +1
The electric vehicle routing problem with time windows (EVRPTW) extends the classical VRPTW by introducing battery capacity constraints and charging station decisions. Existing ben…
A Two-Stage Stochastic Optimization Model for the Equitable Deployment of Fixed and Mobile Electric Vehicle Charging Stations
Hamid Najafzad, Moddassir Khan Nayeem, Fuhad Ahmed Opu +2
A major barrier to wide adoption of Electric Vehicles (EVs) is the absence of reliable and equitable charging infrastructure. Poorly located charging stations create coverage gaps…
A Curriculum-Based Deep Reinforcement Learning Framework for the Electric Vehicle Routing Problem
Mertcan Daysalilar, Fuat Uyguroglu, Gabriel Nicolosi +1
The electric vehicle routing problem with time windows (EVRPTW) is a complex optimization problem in sustainable logistics, where routing decisions must minimize total travel dista…
Fourier Learning Machines: Nonharmonic Fourier-Based Neural Networks for Scientific Machine Learning
Mominul Rubel, Adam Meyers, Gabriel Nicolosi
We introduce the Fourier Learning Machine (FLM), a neural network (NN) architecture designed to represent a multidimensional nonharmonic Fourier series. The FLM uses a simple feedf…