Linear Programming–Based Extended COPRAS Technique for the Highway Project Planning Oriented MCGDM Problem in Cylindrical Neutrosophic Domain

Baisakhi Banik, Avishek Chakraborty () and Shariful Alam
Additional contact information
Baisakhi Banik: Indian Institute of Engineering Science and Technology
Avishek Chakraborty: Academy of Technology
Shariful Alam: Indian Institute of Engineering Science and Technology

SN Operations Research Forum, 2025, vol. 6, issue 2, 1-48

Abstract: Abstract The utility of road and traffic networks is a part and parcel of human life. The idea of highway project planning is the sole motivation of this article. Planning a highway for a specific topographic region needs various criteria to evaluate and prepare a suitable highway for public and vehicular transport. This research article deeply recommends a suitable highway pattern in the cylindrical neutrosophic number (CNN) environment. The cylindrical neutrosophic weighted arithmetic aggregation (CNWAA) operator is proposed to compose the information collected from the decision-makers who suggest their decision in CNN form. Among various highway design patterns, three have been proposed—gridiron, radial, and hexagonal—by different stakeholders associated with the road and transport departments. By assessing some beneficiary, non-beneficiary, and hesitant types of criteria such as quality control, safety norms, cost factor, environmental impact, and weather factor, the experts prescribe their decision with the extended complex proportional assessment (COPRAS) approach to solve this multi-criteria group decision-making (MCGDM) problem. In the meantime, the weights concerned with the decision experts $$\Delta =\{ \text{0.34,0.32,0.34} \}$$ Δ = { 0.34,0.32,0.34 } are collected through a survey report which was conducted by various departments of road and transportation. As per the CNN decisions by the decision-maker, the hexagonal pattern of highway is suggested as the most suitable one. By amalgamating the linear programming (LP) technique with the extended COPRAS methodology, the criteria weights are also determined. Analysis of the weight-bound fluctuations during sensitivity testing highlights the hexagonal highway pattern as the best fit for the specific conditions of this region. Even when the importance value of one sub-criterion is held constant while the importance values of the other sub-criteria are varied, the ranking of the alternatives remains stable. Also, evaluation of this MCGDM problem with other decision-making techniques demonstrates that the hexagonal pattern of the highway is the best suit. Further, this research study can be enhanced with other patterns of data, like fuzzy soft set, fuzzy rough set, q-rung ortho pair fuzzy set, bipolar fuzzy sets, and Pythagorean sets. Our proposed methodology can also be extended with other optimization techniques to diverse fields of decision-making problems.

Keywords: Extended COPRAS (complex proportional assessment); CNN (cylindrical neutrosophic number); MCGDM (multi-criteria group decision-making); Linear programming; Weighed arithmetic averaging operator (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s43069-025-00481-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:snopef:v:6:y:2025:i:2:d:10.1007_s43069-025-00481-6

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/43069

DOI: 10.1007/s43069-025-00481-6

Access Statistics for this article

SN Operations Research Forum is currently edited by Marco Lübbecke

More articles in SN Operations Research Forum from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().