Resilient supplier selection and order allocation under operational and disruption risks

By S. A. Torabi, Milad Baghersad, S. Afshin Mansouri. 2015

427 citations recorded by the source corpus.

Subfields: Management Information Systems, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/0a488f76e49853256f507c76ca8e9a116df71233

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In Management Information Systems

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Citations in the corpus

Ripple effect in the supply chain: an analysis and recent literature (2018, neutral): “For addressing supplier selection and order allocation given operational and disruption risks, Torabi et al. (2015) created a mixed two-stage stochastic programming model with a bi-objective.”
Supply chain resilience for single and multiple sourcing in the presence of disruption risks (2018, methodology): “Increasing the number of disruption scenarios leads to a significant increase in computational time, leading several comparable studies from the area of network design to limit experiments to a smaller subset of possible scenarios (Hatefi et al. 2015; Torabi et al. 2015).”
A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing (2019, methodology): “Recent research hypothesized that some of that success is attributable to the resilient supplier selection and development [1, 2, 3, 4, 5, 6, 7].”
Does the ripple effect influence the bullwhip effect? An integrated analysis of structural and operational dynamics in the supply chain† (2019, background): “…increasing number of studies have been reported in literature that consider, e.g. both demand and structural disruption risks (Kouvelis and Li 2012; Torabi, Baghersad, and Mansouri 2015; Yildiz et al. 2016; Fattahi, Govindan, and Keyvanshokooh 2017; Rezapour, Farahani, and Pourakbar 2017; He et…”
A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0 (2020, neutral): “…either by deactivating some structural elements, or by changing some operational parameters (e.g. capacity) and observing the resulting changes on costs or sales (Torabi, Baghersad, and Mansouri 2015; Yildiz et al. 2016; Ivanov et al. 2016; Rezapour, Farahani, and Pourakbar 2017; Sawik 2019).”
The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics (2018, background): “For addressing supplier selection and order allocation given operational and disruption risks, Torabi, Baghersad, and Mansouri (2015) created a mixed, two-stage stochastic programming model with a bi-objective.”
Low-Certainty-Need (LCN) supply chains: a new perspective in managing disruption risks and resilience (2018, background): “…impact investigation with disruption probability estimations has attracted considerable research attention (Chen et al. 2011, Zobel and Khansa 2014, Torabi et al. 2015, Sawik 2017, Pavlov et al. 2018), some fundamental issues in this research stream need to be pointed out, such as fair probability…”
Supply Chain Viability and the COVID-19 pandemic: a conceptual and formal generalisation of four major adaptation strategies (2021, methodology): “The generalised model is developed on the basis of the frameworks offered in Torabi, Baghersad, and Mansouri (2015) and Zobel et al. (2021) to measure SC recovery.”
Literature review on disruption recovery in the supply chain* (2017, background): “Torabi et al. (2015) develop a bi-objective mixed two-stage stochastic programming model for supplier selection and order allocation problem under operational and disruption risks.”
Resilient and sustainable supply chain design: sustainability analysis under disruption risks (2018, methodology): “For the purpose of analysis, we here assume that backup suppliers are not impacted by disruptions and are able to serve factories under all scenarios (Torabi, Baghersad, and Mansouri 2015).”