ECONOMIC MODELING AND SIMULATION IN THE INDUSTRIAL SUPPLY CHAIN MODEL USING PARTICLE SWARM OPTIMIZATION ALGORITHM

. The economic developments revolve around the distributed and connected supply chains across different industries forming a circular model. The circular model depends on productivity, distribution, and revenue for better standardization. Still, optimizing the industrial economy depending on supply chains is required to reduce overhead costs. Thus, the main contribution of this work is to suppress the cost overheads in unplanned supply chain distributions. In addition, revenue and industrial productivity are enhanced with the help of the particle swarm optimization (PSO)-induced connecting model (CM). The proposed connecting model identifies the least possible circular chain design for product/productivity distribution. The design is based on PSO optimization, wherein the least cost-effective connecting position is identified for each distribution. The minimum cost-effective or less overhead-based supply chain design has been opted for because the distribution is iterative. The best-fit solution is identified using high-precision supply chain connectivity and low-cost overheads. This best-fit solution is iterated through new distribution designs with local and global positions. Therefore, standardization is instigated with the identified global solutions for supply chain economy management.