Optimizing A Batik Manufacturing Supply Chain Under Carbon Tax Regulations Using Evolutionary Algorithms

Abstract

Optimizing supply chains to be both cost-efficient and environmentally responsible has become essential in today’s business landscape, especially with the introduction of carbon tax policies like Indonesia’s Law No. 7 of 2021. The main goal of this research is to develop and implement an integrated supply chain model that brings together production, inventory, transportation, and carbon emission costs into a single decision-making framework. By combining these elements, the model provides a data-backed approach for companies that want to balance their financial objectives with environmental responsibility. To solve this complex challenge, the research applies evolutionary algorithms, to identify the most efficient solutions for order quantities, production batch sizes, and delivery routes. The model was tested using real operational data from a MSME batik manufacturer, serving as a practical example of how such a system can be put into action. The results show that with coordinated planning across all parts of the supply chain, companies can achieve significant cost savings while also reducing their carbon footprint and ensuring compliance with carbon tax regulations. Beyond the specific case, this approach offers valuable guidance for other small and medium-sized manufacturers in Indonesia and similar markets. The model is designed to be flexible and adaptable, making it suitable for businesses with varying production systems, product types, or market demands. By using this method, companies can more confidently navigate regulatory challenges and contribute to greener, more sustainable industrial practices. Ultimately, the research demonstrates the practical benefits and wider relevance of integrating sustainability into supply chain optimization.