At the Academy of Technology (AOT), research in optimization addresses complex, real-world problems across engineering, business, and societal domains. Faculty and students work on linear and nonlinear programming, integer and combinatorial optimization, multi-objective optimization, and metaheuristic approaches such as genetic algorithms, simulated annealing, and particle swarm optimization. The aim is to develop models and algorithms capable of handling large-scale, dynamic, and uncertain environments. Applications of optimization research at AOT are diverse. In manufacturing, optimization techniques are used for production scheduling, resource allocation, and process improvement. In transportation and logistics, route optimization reduces travel time and fuel consumption. In energy systems, optimization supports grid management, renewable energy integration, and load balancing. Healthcare applications include optimizing patient scheduling, treatment planning, and hospital resource allocation. AOT places special emphasis on decision-making under uncertainty, incorporating probabilistic models and robust optimization techniques to ensure reliable outcomes even in volatile conditions. The research also explores hybrid approaches that combine optimization with machine learning, enabling adaptive decision-making in complex systems like supply chains, autonomous vehicles, and smart grids.

Students and researchers at AOT benefit from advanced optimization software, high-performance computing facilities, and real-world case studies from industry collaborations. This hands-on experience ensures graduates are well-equipped to tackle decision-making challenges in various professional settings. Looking ahead, AOT aims to advance research in real-time optimization, AI-integrated decision systems, and sustainable optimization frameworks, contributing to smarter, faster, and more efficient solutions for industries and communities.