39 INASS Express, Vol. 1, Article No. 5, 2025 doi: 10.22266/inassexpress.2025.005 This article is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. License details: https://creativecommons.org/licenses/by-sa/4.0/ Application of the Builder Optimization Algorithm for Sustainable Lot Size Optimization in Supply Chain Management: A Comprehensive Analysis and Comparison with Metaheuristic Approaches Frank Werner 1 * Belal Batiha 2 Tareq Hamadneh 3 Gharib Mousa Gharib 4 Widi Aribowo 5 Mohammad Dehghani 6 1 Faculty of Mathematics, Otto-von-Guericke University, P.O. Box 4120, 39016 Magdeburg, Germany 2 Department of Mathematics. Faculty of Science and Information Technology, Jadara University, Irbid 21110, Jordan 3 Department of Mathematics, Al Zaytoonah University of Jordan, Amman 11733, Jordan 4 Department of Mathematics, Faculty of Science, Zarqa University, Zarqa 13110 Zarqa, Jordan 5 Department of Electrical Engineering, Faculty of Vocational Studies, Universitas Negeri Surabaya, Surabaya, East Java 60231, Indonesia 6 Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, 7155713876, Iran * Corresponding author’s Email: frank.werner@ovgu.de (Received: January 21, 2025. Accepted: March 30, 2025. Published: April 15, 2025.) Abstract Sustainable Lot Size Optimization (SLSO) is a crucial challenge in Supply Chain Management (SCM) that aims to strike a balance between minimizing costs and achieving environmental and social objectives. It focuses on ensuring efficient production and inventory management while reducing the environmental footprint and enhancing social responsibility. Metaheuristic algorithms have proven to be highly effective in solving SLSO problems, as they can explore complex solution spaces to find near-optimal solutions, outperforming traditional methods in terms of flexibility and scalability. In this paper, we investigate the application of the recently published Builder Optimization Algorithm (BOA) to address SLSO challenges. The BOA is evaluated across 10 different SLSO scenarios, and its performance is compared with that of twelve well-established metaheuristic algorithms. The results indicate that BOA performs exceptionally well, consistently providing high-quality solutions for SLSO problems. Moreover, simulation results demonstrate that BOA significantly outperforms its competitors, offering superior optimization results across all test cases. These findings highlight the potential of BOA as a robust and reliable optimization tool for tackling complex supply chain optimization problems, particularly those with sustainability objectives. Keywords: Supply chain management, Sustainable lot size optimization, Metaheuristic, Builder optimization algorithm, Performance comparison, Optimization. 1. Introduction Supply Chain Management (SCM) is an essential aspect of modern business operations, dealing with the efficient movement and storage of goods and services across various stages of production, from the initial raw materials to the final consumer. At its core, SCM seeks to enhance the flow of materials, information, and finances, with the aim of optimizing processes to meet customer demands, minimize costs, and improve overall service levels. Effective SCM incorporates a range of key activities, including procurement, production, inventory management, transportation, and distribution, all while maintaining a strategic focus on customer satisfaction. As globalization and technological advancements continue to shape the dynamics of modern business environments, SCM is becoming increasingly complex, requiring advanced solutions to handle challenges such as supply chain disruptions, demand fluctuations, and sustainability issues [1]. In recent years, there has been a growing emphasis on integrating sustainability into SCM practices. This has led to the emergence of various optimization techniques that not only focus on cost reduction and efficiency but also account for environmental and social factors. One of the most important problems in SCM is the optimization of production and inventory decisions, known as the Lot Size Optimization (LSO) problem. Traditionally, the goal of lot size optimization has been to