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Peer-reviewedCC BY 4.0Mathematics

Sustainable Cold-Chain Vehicle Selection: A Novel AYBI-MUNRA MCDM Model

  • Alptekin Ulutaş1
  • Željko Stević2,3
  • Ayşe Topal4
4 affiliations
  1. 1Department of International Trade and Business, Faculty of Economics and Administrative Sciences, Inonu University, Malatya 44280, Türkiye
  2. 2Graduate School of Energy & Environment (KU-KIST GREEN SCHOOL), Korea University, Seoul 02841, Republic of Korea
  3. 3Department of Mobile Machinery and Railway Transport, Faculty of Transport Engineering, Vilnius Gediminas Technical University, 10223 Vilnius, Lithuania
  4. 4Department of Business, Faculty of Economics and Administrative Sciences, Nigde Omer Halisdemir University, Nigde 51240, Türkiye
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Abstract

The part of the food supply chain that uses the most resources for transportation is the cold supply chain. For this reason, vehicle selection in the cold supply chain is a crucial task. Because there are several alternative vehicles and different criteria, choosing a vehicle is a challenging issue for decision makers. Six distinct cold-chain vehicle options were assessed with twelve sustainability (economic, environmental, and technological) criteria using an integrated Multi-Criteria Decision Making (MCDM) model. The main contribution and novelty of this study include developing a new Accordance-Yielding-Based Integration (AYBI) method for determining criteria weights. For vehicle evaluation and selection, the Multiple Normalization Rating Analysis (MUNRA) technique is applied. According to the AYBI, it was determined that Purchase Price (PP) is the most crucial criterion, followed by Infrastructure and Service Accessibility (ISA) and Load Capacity (LC), and Noise Emissions (NE) is the least significant criterion. According to the MUNRA results, the Twin-Axle Refrigerated Trailer (TRT) performs the best, followed by the Frigorific Trailer (Standard) (FT) and Double-Axle Refrigerated Truck (DART). The results also show that the developed AYBI method provides some advantages in the procedure of determining criteria weights. A sensitivity study verified the AYBI-MUNRA approach’s stability under various weight situations, and comparative analysis demonstrated its resilience in comparison to other MCDM methodologies.

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Cite

BibTeXRIS
BibTeX
@article{ulutas2026sustainable,
  title = {{Sustainable Cold-Chain Vehicle Selection: A Novel AYBI-MUNRA MCDM Model}},
  author = {Alptekin Ulutaş and Željko Stević and Ayşe Topal},
  journal = {Mathematics},
  year = {2026},
  month = nov,
  doi = {10.3390/math14213994},
  url = {https://www.mdpi.com/2227-7390/14/21/3994},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Sustainable Cold-Chain Vehicle Selection: A Novel AYBI-MUNRA MCDM Model
AU  - Alptekin Ulutaş
AU  - Željko Stević
AU  - Ayşe Topal
PY  - 2026
DA  - 2026/11/02
JO  - Mathematics
DO  - 10.3390/math14213994
UR  - https://www.mdpi.com/2227-7390/14/21/3994
AB  - The part of the food supply chain that uses the most resources for transportation is the cold supply chain. For this reason, vehicle selection in the cold supply chain is a crucial task. Because there are several alternative vehicles and different criteria, choosing a vehicle is a challenging issue for decision makers. Six distinct cold-chain vehicle options were assessed with twelve sustainability (economic, environmental, and technological) criteria using an integrated Multi-Criteria Decision Making (MCDM) model. The main contribution and novelty of this study include developing a new Accordance-Yielding-Based Integration (AYBI) method for determining criteria weights. For vehicle evaluation and selection, the Multiple Normalization Rating Analysis (MUNRA) technique is applied. According to the AYBI, it was determined that Purchase Price (PP) is the most crucial criterion, followed by Infrastructure and Service Accessibility (ISA) and Load Capacity (LC), and Noise Emissions (NE) is the least significant criterion. According to the MUNRA results, the Twin-Axle Refrigerated Trailer (TRT) performs the best, followed by the Frigorific Trailer (Standard) (FT) and Double-Axle Refrigerated Truck (DART). The results also show that the developed AYBI method provides some advantages in the procedure of determining criteria weights. A sensitivity study verified the AYBI-MUNRA approach’s stability under various weight situations, and comparative analysis demonstrated its resilience in comparison to other MCDM methodologies.
SN  - 2227-7390
ER  -