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Integrating minimum cost flow and capacitated location in global supply chains under uncertainty

Zhang, Yi 1; Nath, Hari Nandan; Nickel, Stefan 1
1 Institut für Operations Research (IOR), Karlsruher Institut für Technologie (KIT)

Abstract:

Modern supply chain networks face significant challenges in balancing operational efficiency and resilience under diverse real-world uncertainties such as demand fluctuations and disruptions. To address these complexities, this study proposes an Integrated Capacitated Facility Location and Minimum Cost Flow Problem under Multi-Uncertainty (ICLMF-MU). The integrated multi-objective optimization model simultaneously optimizes total cost, service utility, and total risk while accounting for unmet demands and disruptions in supply chain network design. To deal with the NP-hardness of the problem, we propose an enhanced hybrid non-dominated sorting genetic algorithm II (NSGA-II). A novel bi-level evaluation strategy couples an exact linear programming (LP) solver to maintain flow balance with a greedy heuristic as a fallback mechanism. Through a three-stage benchmarking process, the algorithm demonstrates strong convergence and consistent performance. A large-scale numerical study (100 nodes) evaluates performance under diverse global trade environments, contrasting cost-sensitive goods against strategic resources prone to export controls. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000196743
Veröffentlicht am 02.09.2026
Originalveröffentlichung
DOI: 10.1016/j.tre.2026.105180
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Operations Research (IOR)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 08.2026
Sprache Englisch
Identifikator ISSN: 1366-5545, 1878-5794
KITopen-ID: 1000196743
Erschienen in Transportation Research Part E: Logistics and Transportation Review
Verlag Elsevier
Seiten Art.Nr: 105180
Externe Relationen Siehe auch
Schlagwörter Capacitated facility location problem (CFLP); Minimum cost flow problem (MCFP); Supply chain network design; Multi-objective optimization; Global supply chain resilience; Multi-uncertainty; Non-dominated sorting genetic algorithm II (NSGA-II)
Nachgewiesen in Scopus
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