Agent-Based Simulation Modeling for Sustainable Chemical Production and Resource Management

This study investigates the optimization of resource allocation and energy efficiency within a sustainable chemical production network using three distinct methods: Resource Availability-Based Selection, Pareto-based Selection, and Pareto Optimization. Each method was analyzed based on its ability to manage energy consumption, production efficiency, and resource utilization across multiple iterations. The Resource Availability-Based Selection method prioritized available resources in storage, while the Pareto-based Selection introduced input price considerations. Pareto Optimization, the most advanced approach, balanced production efficiency and cost-effectiveness, resulting in the highest overall performance. Findings demonstrate that multi-objective optimization, particularly Pareto Optimization, enhances operational efficiency and sustainability. The study’s implications suggest adopting advanced optimization strategies to achieve energy efficiency and sustainability goals in the chemical industry. Additionally, recommendations for future research include incorporating real-time market dynamics, logistical factors, and renewable energy sources into the model to further enhance decision-making.