Optimization Techniques for Resource Allocation in Large-Scale Engineering Projects: A Review of Current Approaches

Abstract

Resource allocation in large-scale engineering projects is a complex and critical process that significantly impacts project success. This article provides a comprehensive narrative review of current optimization techniques employed in resource allocation, focusing on their application in large-scale engineering projects. The review begins with an overview of resource allocation challenges and the importance of optimization in addressing these challenges. The study then delves into various optimization techniques, including Linear Programming (LP), metaheuristic approaches such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), and advanced AI-based techniques like Neural Networks and Reinforcement Learning (RL). The integration of these techniques into hybrid models is also explored, highlighting their ability to combine the strengths of different methods. Case studies from real-world engineering projects are presented to illustrate the practical application and effectiveness of these techniques. The article concludes with a discussion of best practices, lessons learned, and future research directions, emphasizing the need for continued development and integration of advanced optimization methods to enhance resource allocation in increasingly complex engineering environments.