A hybrid Z-number data envelopment analysis and neural network for assessment of supply chain resilience: a case study
From MaRDI portal
Publication:2045628
DOI10.1007/s10100-018-0596-xOpenAlexW2902174163WikidataQ128825648 ScholiaQ128825648MaRDI QIDQ2045628
Leyla Aliabadi, Reza Tavakkoli-Moghaddam, R. Heidari, Reza Yazdanparast
Publication date: 13 August 2021
Published in: CEJOR. Central European Journal of Operations Research (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10100-018-0596-x
neural networkvulnerabilitysupply chain resilienceautomotive supply chainZ-numbers data envelopment analysis
Related Items (2)
An improved model in fusing multi-source information based on \(Z\)-numbers and POWA operator ⋮ Examining the balance between efficiency and resilience in closed-loop supply chains
Cites Work
- Measuring the efficiency of decision making units
- Coordinating a three-stage supply chain with competing manufacturers
- Towards a multi-objective performance assessment and optimization model of a two-echelon supply chain using SCOR metrics
- A note on \(Z\)-numbers
- Supply chain contracts for capacity decisions under symmetric and asymmetric information
- Collaboration and sharing mechanisms in improving corporate social responsibility
This page was built for publication: A hybrid Z-number data envelopment analysis and neural network for assessment of supply chain resilience: a case study
