Neutrosophic fuzzy decision-making framework for site selection

These days, the area of interest based on canteen location selection in the university campus is a prominent topic. This research work directly affects students' convenience, accessibility and the total campus experience. An ideal canteen site not only provides good food to the students and staff but also takes care of their health by providing a healthy environment. In this paper, we choose various criteria and sub-criteria to calculate the best site for a canteen in the university campus. Location, space and layout, infrastructure and Utilities, safety and compliance and environmental factors as criteria with their corresponding sub-criteria are chosen for this work. Here, the data sets used are provided by decision makers in linguistic words and then converted into crisp numbers. The criteria and sub-criteria weights are considered by a popular multi criteria decision-making (MCDM) method named Criteria Importance Through Intercriteria Correlation, i.e., CRITIC method. Therefore, the ideal location for the canteen in the university campus was determined by applying the complex proportional assessment method (COPRAS), which is basically a ranking method of MCDM. And finally, sensitivity analysis was conducted to make sure the outcomes were flexible and stable.

Authors

Institutions

Publication Details

Journal
Journal of Decision Analytics and Intelligent Computing
Published
2024-12-04
DOI
https://doi.org/10.31181/jdaic10004122024b
Citations
21
Primary Topic
Multi-Criteria Decision Making
Type
article
Field-Weighted Citation Impact
10.30
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Neutrosophic fuzzy decision-making framework for site selection

Kamal Hossain Gazi, Aditi Biswas, Sankar Prasad Mondal, P. N. Bhaduri
21 citations
Journal of Decision Analytics and Intelligent Computing
Multi-Criteria Decision Making
10.30
article

Neutrosophic fuzzy decision-making framework for site selection

Kamal Hossain Gazi, Aditi Biswas, Sankar Prasad Mondal, P. N. Bhaduri
article en
21 citations

Abstract

These days, the area of interest based on canteen location selection in the university campus is a prominent topic. This research work directly affects students' convenience, accessibility and the total campus experience. An ideal canteen site not only provides good food to the students and staff but also takes care of their health by providing a healthy environment. In this paper, we choose various criteria and sub-criteria to calculate the best site for a canteen in the university campus. Location, space and layout, infrastructure and Utilities, safety and compliance and environmental factors as criteria with their corresponding sub-criteria are chosen for this work. Here, the data sets used are provided by decision makers in linguistic words and then converted into crisp numbers. The criteria and sub-criteria weights are considered by a popular multi criteria decision-making (MCDM) method named Criteria Importance Through Intercriteria Correlation, i.e., CRITIC method. Therefore, the ideal location for the canteen in the university campus was determined by applying the complex proportional assessment method (COPRAS), which is basically a ranking method of MCDM. And finally, sensitivity analysis was conducted to make sure the outcomes were flexible and stable.

Journal of Decision Analytics and Intelligent ComputingVol. 4(1)
Maulana Abul Kalam Azad University of Technology, West Bengal (IN)
Industry, innovation and infrastructure
Openalex Percentile: Top 2%
Multi-Criteria Decision Making
10.30
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.