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This advanced and complex project implements an AI-powered optimization system for 5G Open RAN networks. Using machine learning and deep learning, the system optimizes network performance by detecting anomalies, predicting network traffic, and dynamically allocating resources.
In this project, I built several optimization models to determine production level, manage shipment, and maximize thesis points. I also built queueing models to analyze queueing systems in a hospital and made recommendations to meet the criteria, and simulation models to analyze system performance under uncertainty.
This repository contains solution to the "DDAP/DAP solved with evolutionary algorithm" project of the course "OAST - Optimization and Analysis of ICT networks" realized during the winter semester of 2022 on the Warsaw University of Technology.
Solving linear programming using python optimizer interface. This repo allows you to add multiple decision vars and constraints etc. in a very easy way.
GPU implementation of Floyd-Warshall and R-Kleene algorithms to solve the All-Pairs-Shortest-Paths(APSP) problem on Graphs. Code includes random graph generators and benchmarking/plotting scripts.
Maximize the flow of data through a network with this efficient Maximum Flow Algorithm implemented in Kotlin. Use it to optimize data transmission and improve network performance.
Constrained optimization problems, including linear,network, dynamic,integer, and nonlinear programming, decision trees, queueing theory and Markov decision processes.
Formulate the network optimization problem as a discrete model, identifying mathematically the variables and constraints associated with the network. Formulate (mathematically) and solve a non-linear optimization problem based on real (or realistic) world data.