Optimal Design of an Eco-Friendly Transportation Network under Uncertain Parameters

Ahmad Alshamrani, Dipanjana Sengupta, Amrit Das (), Uttam Kumar Bera, Ibrahim M. Hezam, Moddassir Khan Nayeem and Faisal Aqlan
Additional contact information
Ahmad Alshamrani: Department of Statistics and Operations Research, College of Sciences, King Saud University, Riyadh 11451, Saudi Arabia
Dipanjana Sengupta: School of Management, National Institute of Technology Agartala, Jirania 799046, India
Amrit Das: Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore 632014, India
Uttam Kumar Bera: Department of Mathematics, National Institute of Technology Agartala, Jirania 799046, India
Ibrahim M. Hezam: Department of Statistics and Operations Research, College of Sciences, King Saud University, Riyadh 11451, Saudi Arabia
Moddassir Khan Nayeem: Department of Industrial and Production Engineering, Military Institute of Science and Technology, Dhaka 1216, Bangladesh
Faisal Aqlan: Industrial Engineering Department, J.B. Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA

Sustainability, 2023, vol. 15, issue 6, 1-26

Abstract: The choice of attributes in the multi-attribute decision-making process becomes frequently uncertain because of the diverse degree of preference for alternatives. These are assessed utilizing human decisions and linguistic terms that can be utilized for a more adaptable and delicate assessment. The present article illustrates a multi-attribute decision-making (MADM) process, named the exponential technique for order of preference by similarity to an ideal solution (Exp-TOPSIS), considering the selection of attributes with existing uncertainty. Another three notable multi-attribute decision-making (MADM) processes, termed as multi-attribute utility theory (MAUT), elimination and choice expressing reality method (ELECTRE), and the technique for order of preference by similarity to an ideal solution (TOPSIS) are utilized to present a comparison with the proposed methodology by proposing a mathematical model for a solid transportation problem intending to minimize carbon emissions under an uncertain environment. The uncertainty theory, which depends on human conviction degree, is utilized to define the uncertain parameters of the model related to the problem. Applying the proposed one and the other three multi-attribute decision-making processes, the best emission factors are observed to mitigate the carbon emissions from the transport sectors. In this context, the proposed method has some advantages over the existing techniques in selecting the emission factors. All four MADM approaches with different weights have been tested to choose the best five attributes among nine options to be utilized in the mathematical model to minimize the total carbon emission ejection from transportation. In every case, the obtained result states that the proposed Exp-TOPSIS gives the minimum carbon emissions in a range of 2100–2500 units. LINGO 13.0 solver is used to address the deterministic solid transportation problem, and finally, this study presents some investigations on the selection of carbon emission factors and future utilization of the proposed multi-attribute decision-making process.

Keywords: multi-attribute decision-making; multi-attribute utility theory; ELECTRE; TOPSIS; Exp-TOPSIS; solid transportation problem; zigzag uncertain variable (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/6/5538/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/6/5538/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:6:p:5538-:d:1103481

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().