An efficient claim management assurance system using EPC contract based on improved monarch butterfly optimization models - K. Mukilan (22224793) (2024)
The Engineering Procurement Construction (EPC) contract systems are widely employed in the construction industry. Among the prevalent issues in this sector, cash flow problems frequently lead to decreased productivity and efficiency. To address these challenges, a claim management system is developed based on the Improved Monarch Butterfly Optimization Algorithm (IMBOA) and the principles of EPC. Conventional construction models typically optimize only a single objective, such as time, cost, or delay, which may not effectively enhance overall performance. This study aims to develop a claim management system based on IMBOA and EPC principles to optimize multiple objectives, focusing on minimizing project costs and time delays while ensuring high-quality results. The basic methodology of this research involves integrating EPC and claim management principles with the IMBOA algorithm to create an efficient, high-quality system. This process starts with a comprehensive literature review on EPC, claims, MBOA, and related algorithms. Common disputes and claims in the construction industry are examined, and critical factors influencing these claims are identified. The Monarch Butterfly Optimization Algorithm (MBOA) and its improved version (IMBOA) are explored for their application in optimizing project performance. A case study in China's coal mining industry evaluates the effectiveness of the EPC approach, demonstrating that it minimizes time delays and costs. The IMBOA approach proposed in this study has the potential to mitigate 23% of risks and avoid 32% of risks associated with the action plan of China's coal mining industry. Furthermore, comparative analysis with other optimization models indicates that the developed IMBOA model yields superior results, reducing overall project time by 15% and cost by 18%. Other Information Published in: Neural Computing and Applications License: https://creativecommons.org/licenses/by/4.0 See article on publisher's website: https://dx.doi.org/10.1007/s00521-024-10414-9