E-procurement optimization in supply chain: A dynamic approach using evolutionary algorithms

• Published in: Expert systems with applications 2024-12, Vol.255, p.124823, Article 124823
• Main Authors: Raghul, S., Jeyakumar, G., Anbuudayasankar, S.P., Lee, Tzong-Ru
• Format: Article
• Language: English
• Subjects: Differential evolution; Dynamic optimization; Genetic algorithm; Procurement; Supply chain dynamics
• ISSN: 0957-4174
• DOI: 10.1016/j.eswa.2024.124823

•Introducing SMDM for accurate dynamic landscape change detection.•Empirical comparison of SMDM with cutting-edge methods in change detection.•Incorporating SMDM into the HMRS framework to boost dynamic optimization.•Comparing proposed method to advanced methods in dynamic e-procurement. The increasing dynamism of global markets, coupled with the occurrence of unpredictable events, has introduced substantial challenges in formulating efficient supply chain strategies. The inherent dynamic nature of logistic networks necessitates a departure from traditional supply chain methodologies. This study proposes an advanced solution for dynamic e-procurement utilizing evolutionary algorithms (EAs). In conventional supply chains involving buyers and suppliers, a critical challenge is identifying cost-efficient suppliers capable of fulfilling consumer demands amidst fluctuating prices and quantities. Traditional optimization techniques often fail to perform effectively under these dynamic conditions. Moreover, detecting changes during the optimization process is an additional hurdle in dynamic optimization problems. Recent advancements have demonstrated the efficacy of EAs in solving a variety of real-world dynamic optimization issues. This research introduces a novel evolutionary algorithmic framework that integrates the Hybrid Multipopulational Reinitialization Strategy (HMRS), with a proposed hybrid change detection mechanism (named Smirnov-based Multi-sensor Detection Mechanism (SMDM)) to address the dynamic e-procurement problems. The proposed framework enhances the algorithm’s adaptability and responsiveness to real-time changes within the e-procurement environment. By effectively detecting and responding to these variations, the framework aims to optimize procurement processes, ensuring efficiency and robustness in managing fluctuating requirements and conditions inherent to dynamic e-procurement scenarios. The empirical analysis presented underscores the superiority of Differential Evolution (DE) variants over Genetic Algorithm (GA) variants within the procurement context. The detailed empirical study validates the effectiveness of the proposed dynamic approach in addressing the challenges associated with dynamic e-procurement. Considering real-world parameter fluctuations, the proposed approach demonstrates significant resilience, positioning it as a robust and efficient solution for optimizing the e-procurement process and adeptly managing the complexities