Developing mathematical models and intelligent sustainable supply chains by uncertain parameters and algorithms

Massoumeh Nazari; Mahmoud Dehghan Nayeri; Kiamars Fathi Hafshjani; Massoumeh Nazari; Mahmoud Dehghan Nayeri; Kiamars Fathi Hafshjani

doi:10.3934/math.2024252

AIMS Mathematics

2024, Volume 9, Issue 3: 5204-5233. doi: 10.3934/math.2024252

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Research article

Developing mathematical models and intelligent sustainable supply chains by uncertain parameters and algorithms

1.
Department of Industrial Management, Islamic Azad University, Tehran South Branch, Faculty of Management, Tehran, Iran
2.
Department of Industrial Management, Tarbiat Modares University, Faculty of Management and Economics, Tehran, Iran
3.
Department of Industrial Management, Islamic Azad University, Tehran South Branch, Faculty of Management, Tehran, Iran

Received: 16 November 2023 Revised: 13 January 2024 Accepted: 18 January 2024 Published: 26 January 2024
MSC : 47H40, 68T37, 70K75, 90

In the modern era, uncertainty is a common feature of modeling techniques for designing sustainable supply chains. The increasing severity of environmental issues necessitates the integration of sustainable production in supply chain management. The present study aims to develop mathematical models and intelligent sustainable supply chains with uncertain parameters and algorithms. The goal is to design a sustainable and eco-friendly model that minimizes environmental contaminants and system costs. This descriptive-analytical study employs a novel hybrid technique to manage the uncertainty associated with the model parameters, research problems, and problem complexity, and tackle large-scale problems. The automotive industry was selected to implement the mathematical model. These combined techniques consider the disruption-induced capacity reduction and the uncertainties surrounding shipping costs and demand. Results suggest that hybrid models and techniques are efficient in solving large-scale problems and delivering high-quality processing. Further, the findings show that heuristic solutions can significantly reduce computation time for larger problems.
- sustainable supply chain,
- Artificial Intelligence,
- robust,
- uncertain,
- meta-heuristic algorithms
Citation: Massoumeh Nazari, Mahmoud Dehghan Nayeri, Kiamars Fathi Hafshjani. Developing mathematical models and intelligent sustainable supply chains by uncertain parameters and algorithms[J]. AIMS Mathematics, 2024, 9(3): 5204-5233. doi: 10.3934/math.2024252

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Abstract

In the modern era, uncertainty is a common feature of modeling techniques for designing sustainable supply chains. The increasing severity of environmental issues necessitates the integration of sustainable production in supply chain management. The present study aims to develop mathematical models and intelligent sustainable supply chains with uncertain parameters and algorithms. The goal is to design a sustainable and eco-friendly model that minimizes environmental contaminants and system costs. This descriptive-analytical study employs a novel hybrid technique to manage the uncertainty associated with the model parameters, research problems, and problem complexity, and tackle large-scale problems. The automotive industry was selected to implement the mathematical model. These combined techniques consider the disruption-induced capacity reduction and the uncertainties surrounding shipping costs and demand. Results suggest that hybrid models and techniques are efficient in solving large-scale problems and delivering high-quality processing. Further, the findings show that heuristic solutions can significantly reduce computation time for larger problems.

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