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

Onur Mesut Şenaras (), Şahin İnanç, Arzu Eren Şenaras and Burcu Öngen Bilir
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Onur Mesut Şenaras: Oyak Renault Automobile Factory, 16140 Bursa, Türkiye
Şahin İnanç: Department of Computer Technologies, Vocational School of Keles, Bursa Uludağ University, 16059 Bursa, Türkiye
Arzu Eren Şenaras: Department of Econometrics, Faculty of Economics and Administrative Sciences, Bursa Uludağ University, 16059 Bursa, Türkiye
Burcu Öngen Bilir: Department of Business Administration, Faculty of Humanities and Social Sciences, Bursa Technical University, 16310 Bursa, Türkiye

Sustainability, 2025, vol. 17, issue 5, 1-17

Abstract: 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.

Keywords: green storage management; genetic algorithm; ant colony optimization; green supply chain management; Python (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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