Comparing the Use of Ant Colony Optimization and Genetic Algorithms to Organize Kitting Systems Within Green Supply Chain Management Practices

• Published in: Sustainability Vol. 17; no. 5; p. 2001
• Main Authors: Senaras, Onur Mesut, Inanc, Sahin, Eren Senaras, Arzu, Ongen Bilir, Burcu
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2025
• Subjects: Algorithms; Comparative analysis; Decision making; Emerging markets; Energy consumption; Energy efficiency; Environmental aspects; Environmental impact; Genetic algorithms; Integer programming; Inventory; Logistics; Management; Methods; Objectives; Route optimization; Social responsibility; Supply chain management; Supply chains; Sustainable development; Technology application; Turkey
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su17052001

As product diversity continues to expand in today’s market, there is an increasing demand from customers for unique and varied items. Meeting these demands necessitates the transfer of different sub-product components to the production line, even within the same manufacturing process. Lean manufacturing has addressed these challenges through the development of kitting systems that streamline the handling of diverse components. However, to ensure that these systems contribute to sustainable practices, it is crucial to design and implement them with environmental considerations in mind. The optimization of warehouse layouts and kitting preparation areas is essential for achieving sustainable and efficient logistics. To this end, we propose a comprehensive study aimed at developing the optimal layout, that is, creating warehouse layouts and kitting preparation zones that minimize waste, reduce energy consumption, and improve the flow of materials. The problem of warehouse location assignment is classified as NP-hard, and the complexity increases significantly when both storage and kitting layouts are considered simultaneously. This study aims to address this challenge by employing the genetic algorithm (GA) and Ant Colony Optimization (ACO) methods to design a system that minimizes energy consumption. Through the implementation of genetic algorithms (GAs), a 24% improvement was observed. This enhancement was achieved by simultaneously optimizing both the warehouse layout and the kitting area, demonstrating the effectiveness of integrated operational strategies. This substantial reduction not only contributes to lower operational costs but also aligns with sustainability goals, highlighting the importance of efficient material handling practices in modern logistics operations. This article provides a significant contribution to the field of sustainable logistics by addressing the vital role of kitting systems within green supply chain management practices. By aligning logistics operations with sustainability goals, this study not only offers practical insights but also advances the broader conversation around environmentally conscious supply chain practices.