Optimizing Uncapacitated Facility Locations Problem: A Hybrid Approach with Genetic Algorithms and Tabu Search
Yazarlar (3)
Beyza Akyıldız
Doç. Dr. Emrullah SONUÇ Karabük Üniversitesi, Türkiye
Doç. Dr. Caner ÖZCAN Karabük Üniversitesi, Türkiye
| Bildiri Türü | Tebliğ/Bildiri | Bildiri Dili | İngilizce |
| Bildiri Alt Türü | Tam Metin Olarak Yayınlanan Tebliğ (Uluslararası Kongre/Sempozyum) | ||
| Bildiri Niteliği | Alanında Hakemli Uluslararası Kongre/Sempozyum | ||
| Kongre Adı | 2nd International Conference on Trends in Advanced Research ICTAR | ||
| Kongre Tarihi | 22-11-2024 / 23-11-2024 | ||
| Basıldığı Ülke | Türkiye | Basıldığı Şehir | Konya |
| Bildiri Linki | https://drive.google.com/file/d/1X-kxcrCBsfd_M3lcclPHQDZm_NyufQgK/view?usp=drive_link | ||
| UAK Araştırma Alanları |
Yapay Zeka
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| Özet |
| In today’s dynamic and competitive business environment, optimizing uncapacitated facility location decisions is a critical factor in minimizing logistical costs, enhancing customer satisfaction, and maintaining a competitive edge. Traditional deterministic models, while foundational, often fail to adequately address the inherent complexities and uncertainties of modern supply chains, creating a need for more advanced and flexible optimization approaches. This study introduces a hybrid optimization method that leverages the complementary strengths of Genetic Algorithms (GA) and Tabu Search (TS). The proposed GA-TS method combines GA’s robust global exploration capabilities with TS’s intensive local search proficiency, enabling efficient navigation of vast solution spaces and precise refinement of solutions. Extensive experimental analyses validate the hybrid method’s effectiveness, consistently achieving optimal or near-optimal solutions with high computational efficiency and reliability. Compared to traditional optimization techniques, this approach demonstrates superior performance, particularly in addressing the challenges posed by complex and dynamic problem environments. Moreover, the versatility of the hybrid GA-TS framework suggests its applicability to a wide range of optimization problems beyond facility location decisions. By integrating this methodology with emerging technologies, such as machine learning, future research can further enhance its adaptability and scalability, solidifying its relevance in both theoretical and practical domains of operational research. |
| Anahtar Kelimeler |
