A Decision-Making Approach Based on Time Series to Configure a Responsive and Sustainable Supply Chain Considering Different Transportation Modes under Uncertainty
الموضوعات :
Behzad Shahram
1
,
Ali Naderan
2
,
Hassan Javanshir
3
1 - Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran 14778-93855, Iran
2 - Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran 14778-93855, Iran
3 - Department of Industrial Engineering, Faculty of Industry,South Tehran Branch, Islamic Azad University, Tehran, Iran.
الکلمات المفتاحية: Sustainable supply chain, Data-driven decision-making, Transportation modes, Responsive supply chain, Medical equipment,
ملخص المقالة :
The importance of logistics activities in the nowadays modern and competitive marketplace has attracted the attention of researchers toward the supply chain management (SCM) problem. Also, by increasing environmental and social concerns, the concept of sustainability has been drastically highlighted in today’s logistics networks. Therefore, the current study focuses on designing a closed-loop supply chain based on the sustainability and responsiveness dimensions by considering different transportation modes using a data-driven model. In this research, at the outset, a mathematical model is proposed to optimize the sustainability dimensions by considering the responsiveness metric. Then, since the dynamic nature of the business environment leads to the high level of uncertainty, we combine the SARIMA (Seasonal Autoregressive Integrated Moving Average) and the Possibilistic Robust Stochastic (PRS) methods to develop a data-driven framework for tackling the mixed uncertainty. Afterward, by considering the medical equipment industry as a real-world application, a heuristic-based meta-goal programming (MGP) method is developed to solve the proposed model. The achieved results show the appropriate performance of the proposed data-driven model and it has estimated the parameters with over 90% accuracy across all scenarios. Afterward, a series of sensitivity analyses are performed to evaluate how key model parameters influence the research problem, leading to the generation of relevant managerial insights. For example, it has been observed that an increase in the level of services leads to a rise in greenhouse gas emissions. Additionally, social factors increase as the level of services rises.
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