A hybrid approach for low-carbon transportation system analysis: integrating CRITIC-DEMATEL and deep learning features

Abstract

As supply chains, logistics, and transportation activities continue to play a significant role in China's economic and social developments, concerns around energy consumption and carbon emissions are becoming increasingly prevalent. In light of sustainable development goals and the trend toward sustainable or green transportation, there is a need to minimize the environmental impact of these activities. To address this need, the government of China has made efforts to promote low-carbon transportation systems. This study aims to assess the development of low-carbon transportation systems in a case study in China using a hybrid approach based on the Criteria Importance Through Intercriteria Correlation (CRITIC), Decision-Making Trial and Evaluation Laboratory (DEMATEL) and deep learning features. The proposed method provides an accurate quantitative assessment of low-carbon transportation development levels, identifies the key influencing factors, and sorts out the inner connection among the factors. The CRITIC weight matrix is used to obtain the weight ratio, reducing the subjective color of the DEMATEL method. The weighting results are then corrected using an artificial neural network to make the weighting more accurate and objective. To validate our hybrid method, a numerical example in China is applied, and sensitivity analysis is conducted to show the impact of our main parameters and analyze the efficiency of our hybrid method. Overall, the proposed approach offers a novel method for assessing low-carbon transportation development and identifying key factors in China. The results of this study can be used to inform policy and decision-making to promote sustainable transportation systems in China and beyond.

Integration of machine learning in the supply chain for decision making: A systematic literature review

“…This includes route management, load optimization, shipment tracking, and warehouse and distribution center management. In this regard, Zhan, Zhang, Yuan, Chen, Zhang, Fathollahi-Fard et al (2023) and Oguntola, Ülkü, Saif and Engau (2023) highlight the importance of informed and quantitative decision-making. Both studies address sustainability in logistics and transportation, but from different angles.…”

“…Both studies address sustainability in logistics and transportation, but from different angles. While Zhan et al (2023) focus on the evaluation of low-carbon transport system development, Oguntola et al (2023) focus on the optimal design of a multimodal logistics network, considering factors such as shipment consolidation. In both cases, machine learning models play a significant role in generating accurate and objective predictions to guide decision-making.…”

“…In both cases, machine learning models play a significant role in generating accurate and objective predictions to guide decision-making. However, they differ in approach, Zhan et al (2023) use a combination of CRITIC and DEMATEL, where they use the CRITIC weight matrix to obtain the weight ratio, reducing the subjectivity of the DEMATEL method. The weighting results are then corrected using an artificial neural network.…”

“…• CRITIC and DEMATEL obtain the weight ratio of the variables using the Backpropagation Neural Network (BP) (Zhan et al, 2023) • Genetic algorithms feed the K-Means model (Aloui et al, 2021) Production Planning…”

Hybrid optimization algorithm for enhanced performance and security of counter-flow shell and tube heat exchangers

“…Zhan et al [ 31 ] present a hybrid approach for assessing China’s low-carbon transportation infrastructure. Deep learning features are integrated with the CRITIC and DEMATEL methodologies to reduce environmental impact.…”