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Journal of Industrial and Systems Engineering، جلد ۱۶، شماره ۲، صفحات ۲۶-۵۰
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عنوان انگلیسی Data-Driven Robust Optimization for Hub Location-Routing Problem under Uncertain Environment
چکیده انگلیسی مقاله This study addresses the Hub Location-Routing Problem (HLRP) in transportation networks, considering the inherent uncertainty in travel times between nodes. We employed a method centered on data-driven robust optimization, utilizing Support Vector Clustering (SVC) to form an uncertainty set grounded in empirical data. The proposed methodology is compared against traditional uncertainty sets, showcasing its superior performance in providing robust solutions. A comprehensive case study on a retail store's transportation network in Tehran is presented, demonstrating significant differences in hub locations, allocations, and vehicle routes between deterministic and robust models. The SVC-based model proves to be particularly effective, yielding substantially improved objective function values compared to polyhedral and box uncertainty sets. The study concludes by highlighting the practical significance of this research and suggesting future directions for advancing transportation network optimization under uncertainty.
کلیدواژه‌های انگلیسی مقاله robust optimization,Hub Location,Machine Learning,data-driven approach,support vector clustering
نویسندگان مقاله MirMohammad Musavi |
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

Ali Bozorgi-Amiri |
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

نشانی اینترنتی https://www.jise.ir/article_210377_19abb1c73431dd777e2b62d53795a9a7.pdf
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