# Intelligent Procurement Scheduling System for Items Involving Public Procurement
## 1 Introduction
Fuzzy logic employs linguistic variables like \"low\", \"medium\", \"high\", \"fast\", \"slow\", \"hot\", \"warm\", \"normal\" and \"cold\" to represent input data. The system comprises three core components: (i) a fuzzifier controller that converts crisp values into linguistic variables, (ii) a knowledge-based system [26; 27; 28], and (iii) a defuzzifier controller that transforms fuzzy values into crisp physical signals for controlling system operations. Fuzzy logic systems offer flexibility, allowing for rule modifications and the acceptance of imprecise, distorted or erroneous input information [29]. They find applications in various domains, including electrically driven wheeled mobile robots [30], load forecasting [31], control of domestic appliances, HVAC systems and supply chains [32]. This literature review reveals that while AI and fuzzy logic systems have been extensively studied and applied in various aspects of supply chain management and public procurement, specific application of these technologies to establish procurement norms based on empirical data remains underexplored. Existing studies focus on optimization techniques, decision support systems and cost minimization but seldom provide comprehensive methods for determining procurement norms that can adapt to the dynamic nature of public procurement. The primary research gap identified is the lack of a systematic approach to establish procurement norms using machine learning and fuzzy logic systems. This gap highlights the need for integrating empirical data with advanced AI techniques to create adaptable and efficient procurement policies. This paper proposes a novel approach that integrates machine learning and fuzzy logic systems to establish optimal procurement norms. By identifying and focusing on the most critical input variables, this study aims to provide a data-driven decision-making framework that can adapt to the complexities and uncertainties inherent in public procurement processes. The proposed system not only addresses the existing research gap but also offers practical solutions that can enhance transparency, efficiency and cost-effectiveness in public procurement. This work addresses a significant gap in the existing literature by providing a comprehensive framework for establishing procurement norms using AI techniques. The proposed approach has the potential to revolutionize public procurement processes, making them more efficient, transparent and responsive to changing market conditions. By offering practical insights and a robust methodology, this paper can serve as a valuable resource for researchers and practitioners in the field of supply chain management and public procurement. This paper proposes a machine learning and fuzzy-based decision-making system to provide optimal solutions for establishing procurement norms, ultimately optimizing resource utilization. The subsequent section elaborates on the procurement process, defines objectives and identifies problems. Section 3 details the development of the fuzzy-based system, while the results and discussion are presented in Section 4.