A Hybrid Machine Learning-Based Model for Evaluating the Performance of Agile-Sustainable Supply Chains in the Context of Industry 4.0: A Case Study DOI Creative Commons

Aboozar Ghorbani,

Mehdi Fadaei, Mansour Soufi

et al.

RAIRO - Operations Research, Journal Year: 2024, Volume and Issue: 58(5), P. 4681 - 4700

Published: Aug. 16, 2024

In today’s world, businesses and, in general, supply chains have undergone extensive transformations, and relying solely on traditional metrics such as cost quality cannot provide a comprehensive complete evaluation of companies active various sections chains. One the main concerns chain managers is to create an integrated structure for evaluating performance branches. this context, study presents that, by simultaneously considering agility sustainability within context industry 4.0, which has brought about fundamental changes environment recent years, aims evaluate branches dairy product chain. On other hand, increase volume data produced development applications machine learning algorithms fields, offer better compared intuitive approaches, led use hybrid data-driven are combination expert-based methods documented organizational data, Therefore, innovative terms approach developed. first step, appropriate dimensions agility, sustainability, Industry general were identified, then fuzzy best-worth method (FBWM) was used weight metrics. According findings, data-driven, marketing, overhead costs, delivery timeframe, selected most important Subsequently, using developed artificial neural network algorithm, calculates input weights FBWM method, model presented, findings show that performs than problem with more 92% accuracy.

Language: Английский

A hybrid machine learning-based decision-making model for viable supplier selection problem considering circular economy dimensions DOI
AmirReza Tajally,

Mahla Zhian Vamarzani,

Mohssen Ghanavati-Nejad

et al.

Environment Development and Sustainability, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 25, 2025

Language: Английский

Citations

1

Developing Agility, Resilience, and Circular Economy Decision-Making Model Based on Data Envelopment Analysis for Evaluating Medical Equipment Suppliers DOI

M. Mirzayi

Process Integration and Optimization for Sustainability, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 18, 2025

Language: Английский

Citations

0

Sustainable supplier selection and order allocation problem considering the agility and resilience dimensions: a novel multi-stage data-driven decision-making approach DOI
AmirReza Tajally,

Benyamin Babakhani,

Emaad Jeyzanibrahimzade

et al.

International Journal of Systems Science Operations & Logistics, Journal Year: 2025, Volume and Issue: 12(1)

Published: Feb. 5, 2025

Language: Английский

Citations

0

A Multi-stage Machine Learning Model to Design a Sustainable-Resilient-Digitalized Pharmaceutical Supply Chain DOI
Mostafa Jafarian,

Iraj Mahdavi,

Ali Tajdin

et al.

Socio-Economic Planning Sciences, Journal Year: 2025, Volume and Issue: unknown, P. 102165 - 102165

Published: Feb. 1, 2025

Language: Английский

Citations

0

A Heuristic-based Multi-Stage Machine Learning-based Model to Design a Sustainable, Resilient, and Agile Reverse Corn Supply Chain by considering Third-party Recycling DOI

Fardin Rezaei Zeynali,

Mohammad Parvin, Ali Akbar ForouzeshNejad

et al.

Applied Soft Computing, Journal Year: 2025, Volume and Issue: unknown, P. 113042 - 113042

Published: March 1, 2025

Language: Английский

Citations

0

The customer-based supplier selection and order allocation problem based on the waste management and resilience dimensions: A data-driven approach DOI

Borna Rezaie,

Nikbakhsh Javadian, Mohammad Kazemi

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 153, P. 110692 - 110692

Published: April 19, 2025

Language: Английский

Citations

0

A novel stochastic machine learning approach for resilient-leagile supplier selection: a circular supply chain in the era of industry 4.0 DOI

Bahar Javan Molaei,

Mohssen Ghanavati-Nejad,

AmirReza Tajally

et al.

Soft Computing, Journal Year: 2025, Volume and Issue: unknown

Published: April 28, 2025

Language: Английский

Citations

0

Creating reliable and resilient logistic systems—A new conceptual approach DOI
Agnieszka Tubis, Sylwia Werbińska-Wojciechowska

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 125 - 249

Published: Jan. 1, 2025

Language: Английский

Citations

0

A Hybrid Machine Learning Approach to Evaluate and Select Agile-Resilient-Sustainable Suppliers Considering Supply Chain 4.0: A Real Case Study DOI

Mahyar Abbasian,

Amin Jamili

Process Integration and Optimization for Sustainability, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 14, 2025

Language: Английский

Citations

0

A Hybrid Machine Learning-Based Model for Evaluating the Performance of Agile-Sustainable Supply Chains in the Context of Industry 4.0: A Case Study DOI Creative Commons

Aboozar Ghorbani,

Mehdi Fadaei, Mansour Soufi

et al.

RAIRO - Operations Research, Journal Year: 2024, Volume and Issue: 58(5), P. 4681 - 4700

Published: Aug. 16, 2024

In today’s world, businesses and, in general, supply chains have undergone extensive transformations, and relying solely on traditional metrics such as cost quality cannot provide a comprehensive complete evaluation of companies active various sections chains. One the main concerns chain managers is to create an integrated structure for evaluating performance branches. this context, study presents that, by simultaneously considering agility sustainability within context industry 4.0, which has brought about fundamental changes environment recent years, aims evaluate branches dairy product chain. On other hand, increase volume data produced development applications machine learning algorithms fields, offer better compared intuitive approaches, led use hybrid data-driven are combination expert-based methods documented organizational data, Therefore, innovative terms approach developed. first step, appropriate dimensions agility, sustainability, Industry general were identified, then fuzzy best-worth method (FBWM) was used weight metrics. According findings, data-driven, marketing, overhead costs, delivery timeframe, selected most important Subsequently, using developed artificial neural network algorithm, calculates input weights FBWM method, model presented, findings show that performs than problem with more 92% accuracy.

Language: Английский

Citations

0