Supply Chain Resilience in California: Targeted-Efficient Spillover Methodology
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| Udgivet i: | International Journal of Combinatorial Optimization Problems and Informatics vol. 16, no. 3 (2025), p. 92-106 |
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International Journal of Combinatorial Optimization Problems & Informatics
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| Online adgang: | Citation/Abstract Full Text - PDF |
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| 022 | |a 2007-1558 | ||
| 024 | 7 | |a 10.61467/2007.1558.2025.v16i2.327 |2 doi | |
| 035 | |a 3233470578 | ||
| 045 | 2 | |b d20250701 |b d20250930 | |
| 084 | |a 155128 |2 nlm | ||
| 100 | 1 | |a Moreno-Baca, Fabricio | |
| 245 | 1 | |a Supply Chain Resilience in California: Targeted-Efficient Spillover Methodology | |
| 260 | |b International Journal of Combinatorial Optimization Problems & Informatics |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The world has changed drastically in logistical and economic spheres as a result of the COVID-19 pandemic. This pandemic has caused a global crisis in supply-chain structures, creating regional, national and international impacts of unprecedented magnitude. Accordingly, this research develops a methodology to favour the logistics-resilience framework based on regional externalities and technical-efficiency analysis of the 51 US states, applying a Spatial Data Panel model and a Stochastic Frontier model in conjunction with graph theory (Ford–Fulkerson algorithm). The findings indicate that New York, West Virginia and North Dakota are vital external regions to support California’s logistics resilience. We demonstrate that a region with high technical efficiency does not necessarily constitute a key logistics spillover for a target region. This study represents one of the first attempts to optimise and redirect externalities from one region to another using spatial and logistical mechanisms. | |
| 653 | |a Regional development | ||
| 653 | |a Stochastic models | ||
| 653 | |a Spatial data | ||
| 653 | |a Logistics | ||
| 653 | |a Resilience | ||
| 653 | |a Graph theory | ||
| 653 | |a Supply chains | ||
| 653 | |a Externality | ||
| 653 | |a Infrastructure | ||
| 653 | |a Cooperation | ||
| 653 | |a Communication | ||
| 653 | |a Optimization | ||
| 653 | |a Pandemics | ||
| 653 | |a Regions | ||
| 653 | |a Taxonomy | ||
| 653 | |a Algorithms | ||
| 653 | |a Medical supplies | ||
| 653 | |a Informatics | ||
| 653 | |a Suppliers | ||
| 653 | |a Efficiency | ||
| 653 | |a Public policy | ||
| 653 | |a COVID-19 | ||
| 700 | 1 | |a Cano-Olivos, Patricia | |
| 700 | 1 | |a Martínez-Flores, José Luis | |
| 700 | 1 | |a Sánchez-Partida, Diana | |
| 773 | 0 | |t International Journal of Combinatorial Optimization Problems and Informatics |g vol. 16, no. 3 (2025), p. 92-106 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3233470578/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3233470578/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |