In the ever-evolving world of supply chain management, a groundbreaking study led by Seyyed Jalaladdin Hosseini Dehshiri from the Department of Industrial Management at Allameh Tabataba’i University in Tehran, Iran, is set to revolutionize how we approach sustainability and cost-efficiency. The research, published in the journal ‘Transport’, introduces a novel method using Pentagonal Fuzzy Numbers (PFNs) to design sustainable closed-loop supply chains, marking a significant leap in the field.
Traditional supply chain models often grapple with uncertainties and incomplete information, leading to suboptimal decisions. Hosseini Dehshiri’s research addresses these challenges head-on by integrating random and cognitive uncertainties into a robust stochastic-possibilistic programming framework. “The use of PFNs allows us to capture a higher degree of uncertainty and subjectivity, providing a more comprehensive and accurate model,” Hosseini Dehshiri explains. This approach not only enhances decision-making but also paves the way for more sustainable and cost-effective supply chain operations.
The innovative model, known as Robust Stochastic-Possibilistic (RSP) programming, leverages PFNs to handle fuzzy scenarios more effectively than traditional triangular or trapezoidal fuzzy numbers. This method introduces measures of necessity, possibility, and credibility, enabling decision-makers to tailor their strategies based on different risk levels. “By incorporating these measures, we can create trade-offs between the mean of objective functions and risk, leading to more robust and reliable supply chain designs,” Hosseini Dehshiri elaborates.
The practical implications of this research are vast, particularly for the energy sector. As industries strive to reduce their carbon footprint and operational costs, the ability to model and optimize supply chains with greater precision becomes invaluable. The case study presented in the research, focusing on the stone paper supply chain, demonstrates the model’s efficacy in reducing costs and carbon pollution. The findings suggest that by adjusting robustness coefficients, optimal confidence levels can be specified, resulting in more accurate and reliable outcomes.
This breakthrough in supply chain management is poised to shape future developments in the field. As industries increasingly prioritize sustainability and efficiency, the adoption of robust stochastic-possibilistic programming could become a game-changer. By providing a more nuanced understanding of uncertainties and subjectivities, this approach enables better-informed decisions that drive both economic and environmental sustainability. The research, published in ‘Transport’, or ‘Transportation’ in English, offers a compelling blueprint for the future of supply chain design, promising a more resilient and eco-friendly industrial landscape.
