volume 31 issue 9 pages 5045-5068

New multi-criteria LNN WASPAS model for evaluating the work of advisors in the transport of hazardous goods

Dragan Pamucar 1
Siniša Sremac 2
Željko Stević 3
Goran Ćirović 2
Dejan Tomić 4
1
 
Department of Logistics, Military Academy, University of Defence in Belgrade, Belgrade, Serbia
4
 
Provincial Secretariat for Energy, Construction and Transport, Autonomous Province of Vojvodina, Novi Sad, Serbia
Publication typeJournal Article
Publication date2019-01-24
scimago Q1
SJR1.102
CiteScore11.7
Impact factor
ISSN09410643, 14333058
Artificial Intelligence
Software
Abstract
Successfully organizing the transport of hazardous materials and handling them correctly is a very important logistical task that affects both the overall flow of transport and the environment. Safety advisors for the transport of hazardous materials have a very important role to play in the proper and safe development of the transport flow for these materials; their task is primarily to use their knowledge and effort to prevent potential accidents from happening. In this research, a total of 21 safety advisors for the transport of hazardous materials in Serbia are assessed using a new model that integrates Linguistic Neutrosophic Numbers (LNN) and the WASPAS (Weighted Aggregated Sum Product Assessment) method. In this way, two important contributions are made, namely a completely new methodology for assessing the work of advisors and the new LNN WASPAS model, which enriches the field of multi-criteria decision making. The advisors are assessed by seven experts on the basis of nine criteria. After performing a sensitivity analysis on the results, validation of the model is carried out. The results obtained by the LNN WASPAS model are validated by comparing them with the results obtained by LNN extensions of the TOPSIS (Technique for Order Performance by Similarity to Ideal Solution), LNN CODAS (COmbinative Distance-based ASsessment), LNN VIKOR (Multi-criteria Optimization and Compromise Solution) and LNN MABAC (Multi-Attributive Border Approximation area Comparison) models. The LNN CODAS, LNN VIKOR and LNN MABAC are also further developed in this study, which is an additional contribution made by the paper. After the sensitivity analysis, the SCC (Spearman Correlation Coefficient) is calculated which confirms the stability of the previously obtained results.
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GOST |
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GOST Copy
Pamucar D. et al. New multi-criteria LNN WASPAS model for evaluating the work of advisors in the transport of hazardous goods // Neural Computing and Applications. 2019. Vol. 31. No. 9. pp. 5045-5068.
GOST all authors (up to 50) Copy
Pamucar D., Sremac S., Stević Ž., Ćirović G., Tomić D. New multi-criteria LNN WASPAS model for evaluating the work of advisors in the transport of hazardous goods // Neural Computing and Applications. 2019. Vol. 31. No. 9. pp. 5045-5068.
RIS |
Cite this
RIS Copy
TY - JOUR
DO - 10.1007/s00521-018-03997-7
UR - https://doi.org/10.1007/s00521-018-03997-7
TI - New multi-criteria LNN WASPAS model for evaluating the work of advisors in the transport of hazardous goods
T2 - Neural Computing and Applications
AU - Pamucar, Dragan
AU - Sremac, Siniša
AU - Stević, Željko
AU - Ćirović, Goran
AU - Tomić, Dejan
PY - 2019
DA - 2019/01/24
PB - Springer Nature
SP - 5045-5068
IS - 9
VL - 31
SN - 0941-0643
SN - 1433-3058
ER -
BibTex |
Cite this
BibTex (up to 50 authors) Copy
@article{2019_Pamucar,
author = {Dragan Pamucar and Siniša Sremac and Željko Stević and Goran Ćirović and Dejan Tomić},
title = {New multi-criteria LNN WASPAS model for evaluating the work of advisors in the transport of hazardous goods},
journal = {Neural Computing and Applications},
year = {2019},
volume = {31},
publisher = {Springer Nature},
month = {jan},
url = {https://doi.org/10.1007/s00521-018-03997-7},
number = {9},
pages = {5045--5068},
doi = {10.1007/s00521-018-03997-7}
}
MLA
Cite this
MLA Copy
Pamucar, Dragan, et al. “New multi-criteria LNN WASPAS model for evaluating the work of advisors in the transport of hazardous goods.” Neural Computing and Applications, vol. 31, no. 9, Jan. 2019, pp. 5045-5068. https://doi.org/10.1007/s00521-018-03997-7.