Optimizing Project Resource Allocation through a Caching-Enhanced Cloud AI Decision Support System
DOI:
https://doi.org/10.15680/IJCTECE.2022.0502003Keywords:
artificial intelligence, cloud computing, decision support system, resource allocation, project management, optimization, predictive analytics, multi-project portfolioAbstract
Efficient resource allocation is a perennial challenge in project management, particularly when multiple projects compete for limited human, financial, and technical resources. Traditional methods—spreadsheets, manual planning, and rule-based heuristics—are often rigid, slow, and suboptimal in rapidly changing environments. Cloud‑based artificial intelligence (AI) decision support systems (DSS) present a transformative solution: leveraging scalable computation, predictive analytics, and optimization to dynamically allocate resources across projects. This paper proposes a conceptual framework for a cloud‑hosted AI DSS that continuously ingests project data (e.g., historical performance, resource consumption, risk metrics), forecasts resource needs, detects bottlenecks, recommends reallocation, and supports “what-if” scenario analysis. We conduct a comprehensive literature review of AI in project management, resource optimization techniques, and cloud computing, identifying key models (machine learning, reinforcement learning, multi-criteria decision analysis) and their application. Our methodology uses a design‑science approach, constructing a prototype via simulation of a multi‑project portfolio in a cloud environment. We validate the system by comparing its performance against a baseline manual allocation method, measuring metrics such as resource utilization, project throughput, risk exposure, and execution efficiency. The results from simulation experiments indicate that the AI DSS improves resource utilization by up to 20–30%, reduces idle resource time, and mitigates resource-related risk in changing project conditions. We discuss the practical advantages (scalability, real-time agility, data-driven decisions), limitations (data quality, interpretability, integration with legacy tools), and governance issues (ethical constraints, human trust, override mechanisms). Finally, we outline future work, including piloting the system in real organizations, enhancing interpretability via explainable AI techniques, and extending the approach to hybrid cloud-edge resource settings. Our findings suggest that cloud-based AI decision support can significantly enhance project resource allocation, enabling smarter, more adaptive, and more strategic decisions.
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