Location Selection for a Lumber Drying Facility via a Hybrid Pythagorean Fuzzy Decision-making Approach

被引:1
作者
Singer, Hilal [1 ]
机构
[1] Bolu Abant Izzet Baysal Univ, Dept Ind Engn, TR-14030 Bolu, Turkiye
来源
BIORESOURCES | 2024年 / 19卷 / 03期
关键词
Lumber drying facility; Location analysis; AHP; WASPAS; Pythagorean fuzzy set; AHP;
D O I
10.15376/biores.19.3.4120-4134
中图分类号
TB3 [工程材料学]; TS [轻工业、手工业、生活服务业];
学科分类号
0805 ; 080502 ; 0822 ;
摘要
The strategic selection of facility locations plays a critical role in optimizing operational efficiency, reducing costs, and enhancing customer satisfaction, thereby contributing significantly to the success and competitiveness of businesses. In this study, an interval-valued Pythagorean fuzzy decision-making framework is proposed to select the best location for the lumber drying industry. A four-level hierarchical model is devised with four main criteria, 16 subcriteria, and five alternatives. The opinions of different experts are gathered to obtain input data. The weights of the criteria are calculated using the interval-valued Pythagorean fuzzy analytic hierarchy process (AHP) method. The interval-valued Pythagorean fuzzy weighted aggregated sum product assessment (WASPAS) method is employed to evaluate the alternative locations. A sensitivity analysis is conducted to support the validity of the model results. The study concludes by revealing the optimal location for the lumber drying industry in Turkey. This study presents its novelty by formulating the lumber drying facility location selection problem as a complex fuzzy multicriteria decision-making problem and integrating the Pythagorean fuzzy AHP and WASPAS methods to solve the problem.
引用
收藏
页码:4120 / 4134
页数:15
相关论文
共 34 条
[1]  
Aktas A., 2022, MULTIPLE CRITERIA DE, P179, DOI [10.1007/978-3-030-98872-212, DOI 10.1007/978-3-030-98872-212]
[2]   An extended interval-valued Pythagorean fuzzy WASPAS method based on new similarity measures to evaluate the renewable energy sources [J].
Al-Barakati, Abdullah ;
Mishra, Arunodaya Raj ;
Mardani, Abbas ;
Rani, Pratibha .
APPLIED SOFT COMPUTING, 2022, 120
[3]   WASPAS-based decision making methodology with unknown weight information under uncertain evaluations [J].
Ali, Jawad ;
Bashir, Zia ;
Rashid, Tabasam .
EXPERT SYSTEMS WITH APPLICATIONS, 2021, 168
[4]   Membership Score of an Interval-Valued Pythagorean Fuzzy Numbers and Its Applications [J].
Alrasheedi, Melfi A. ;
Jeevaraj, S. .
IEEE ACCESS, 2023, 11 :37832-37839
[5]  
Athawale Vijay Manikrao, 2012, International Journal of Industrial and Systems Engineering, V11, P16, DOI 10.1504/IJISE.2012.046652
[6]  
Azizi M., 2008, Journal of the Institute of Wood Science, V18, P52
[7]  
Azizi M., 2015, Int. J. Multicriteria Decis. Mak., V5, DOI [10.1504/IJMCDM.2015.06793, DOI 10.1504/IJMCDM.2015.06793]
[8]  
Azizi M., 2004, OR Insight, V17, P22, DOI [10.1057/ori.2004.17, DOI 10.1057/ORI.2004.17]
[9]   Revisiting ranking accuracy within WASPAS method [J].
Baykasoglu, Adil ;
Golcuk, Ilker .
KYBERNETES, 2020, 49 (03) :885-895
[10]  
Bolturk Eda, 2020, Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making. Proceedings of the INFUS 2019 Conference. Advances in Intelligent Systems and Computing (AISC 1029), P867, DOI 10.1007/978-3-030-23756-1_104