The Role of Artificial Intelligence in Optimizing Resource Allocation in Engineering Projects in Libya

Abstract:

Resource allocation is the bedrock of a successful engineering project management, with respect to effective usage of resources like labor, materials, and equipment. The traditional methods that depend on static optimization and heuristic techniques are restricted in their adaptability to real-time changes and result in inefficiencies, cost overruns, and missed deadlines. These factors are increasingly necessary as modern engineering projects become complex and large in scale. This research goes beyond the issues outlined above, instead proposing a fully Al-driven framework to better allocate resources to bring about vastly improved project outcomes. While traditional techniques will rely solely on predictive analytics and advanced algorithms using machine learning techniques to evaluate immense amounts of both historical and real-time data in order to create accurate demand for resources as well as to do dynamic re-allocations. The proposed framework as illustrated in the case study performed in Libya, can minimize resource wastage, enhance productivity, and respond to unforeseen disruptions such as supply chain interruptions or labor shortages by integrating these capabilities with optimization algorithms. The novelty in this approach is the integration of predictive analytics with real-time decision-making within the constraint framework of meeting budgetary and timeline limits without compromise to efficiency or quality. The study aims to design and validate a robust resource allocation model for the purpose of forecasting an accuracy of 99.3% and optimizing resource utilization. This study follows a hybrid Al system, where predictive analytics are generating the forecast regarding demands on resources and optimization algorithms dynamically allocating them. Simulations and case studies demonstrate that the proposed framework does reduce idle time, minimize costs, and ensure timely project completion. The results obtained show tremendous potential for AI-driven systems in shifting the paradigm of engineering resource management.

Keywords: Artificial Intelligence, Efficiency, Optimization, Predictive Analytics, Resource Allocation.