Analysis of Green Vehicle Routing Problems with Integrated Pythagorean Fuzzy AHP and EDAS Methods

Year 2024, Erken Görünüm, 1 - 1

Gözde Bakioğlu

https://doi.org/10.29109/gujsc.1480578

Abstract

Nowadays, environmental issues such as climate change, air pollution and depletion of natural resources are of increasing concern on a global scale and require urgent measures for environmental sustainability. The transportation industry is one of the sectors that contributes significantly to environmental impacts. Green vehicle routing problems facilitate the establishment of sustainable transportation systems by minimizing environmental impacts through the preference of low-emission and eco-friendly vehicles. In this study, green vehicle routing problems are considered as a multi-criteria decision-making problem, enhancing decision-making transparency by providing decision-makers with various scenarios and weighting options. In this context, an extended Analytic Hierarchy Process (AHP) and EDAS (Evaluation Based on Average Distance to Solution) integrated model in Pythagorean Fuzzy Sets environment, which is a relatively new approach, was carried out and 5 green vehicle routing problems were selected with 6 evaluation criteria. Furthermore, sensitivity analysis was conducted to assess how the decision-making problem is influenced by varying criteria weights, and the robustness of the proposed method was tested across 15 different scenarios. The results of this study provide researchers with the opportunity to evaluate and analyze various green vehicle routing scenarios, while providing industry professionals and logistics managers with environmentally friendly solutions that reduce costs and increase operational efficiency.

Keywords

Green Vehicle Routing Problem, Pythagorean fuzzy AHP, EDAS, Sustainability

Ethical Statement

The author declares that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Yeşil Araç Rotalama Problemlerinin Entegre Pisagor Bulanık AHP ve EDAS Yöntemleri ile Analizi

Abstract

Günümüzde, iklim değişikliği, hava kirliliği ve doğal kaynakların tükenmesi gibi çevresel sorunlar, küresel ölçekte artan bir endişe kaynağı olmuştur ve bu sorunlar çevresel sürdürülebilirlik için acil önlemlerin alınmasını gerektirmektedir. Ulaşım endüstrisi, çevresel etkilere önemli ölçüde katkı sağlayan sektörlerden biridir. Yeşil araç rotalama problemleri, düşük emisyonlu ve çevre dostu araçların tercih edilmesiyle çevresel etkileri minimize ederek sürdürülebilir ulaşım sistemlerinin oluşturulmasına katkı sağlamaktadır. Bu çalışmada, yeşil araç rotalama problemleri, çok kriterli karar verme problemi olarak ele alınmış, karar vericilere çeşitli senaryolar ve ağırlıklandırma seçenekleri sunularak karar sürecinin şeffaflığı artırılmıştır. Bu bağlamda, göreceli olarak yeni bir yaklaşım olan Pisagor Bulanık Kümeler ortamında genişletilmiş Analitik Hiyerarşi Prosesi (AHP) ve EDAS (Ortalama Çözüm Uzaklığına Dayalı Değerlendirme) entegre modeli üzerine çalışılmış ve 6 değerlendirme ölçütü ile 5 yeşil araç rotalama problemi arasında seçim yapılmıştır. Bununla birlikte, farklı ölçüt ağırlıkları altında karar verme probleminin nasıl etkilendiğini belirlemek amacıyla duyarlılık analizi gerçekleştirilmiş ve önerilen metodun duyarlılığı 15 farklı senaryo üzerinde test edilmiştir. Bu çalışmanın sonuçları, araştırmacılara çeşitli yeşil araç rotalama senaryolarını değerlendirme ve analiz etme imkânı sağlarken, endüstri profesyonellerine ve lojistik yöneticilerine maliyeti düşüren, operasyonel verimliliği arttıran, çevre dostu çözümleri sunmaktadır.

Keywords

Yeşil araç rotalama problemi, Pisagor bulanık AHP, EDAS, Sürdürülebilirlik

Ethical Statement

Bu makalenin yazarları çalışmalarında kullandıkları materyal ve yöntemlerin etik kurul izni ve/veya yasal-özel bir izin gerektirmediğini beyan ederler.

References

• [1] World energy outlook, 2024; https://www.eiu.com/n/campaigns/energy-in-2024/#:~:text=Global%20energy%20consumption%20will%20grow,energy%20will%20rise%20by%2011%25
• [2] Höök, M, Li, J, Johansson, K, Snowden, S. Growth rates of global energy systems and future outlooks. Nat. Resour. Res. 2012; 21: 23–41.
• [3] European Commission. Report from the Commission to the European Parliament and the Council – Progress towards Achieving the Kyoto and EU2020 Objecitves. European Commission. 2014
• [4] Erdogan, S, Miller-Hooks, E. A green vehicle routing problem. Transp. Res. E Logist. Transp. Rev. 2012; 48 (1): 100–114.
• [5] Chen, J, Liao, W, Yu, C. Route optimization for cold chain logistics of front warehouses based on traffic congestion and carbon emission. Computers & Industrial Engineering. 2021; 161: 107663.
• [6] Palacio, J. D, Rivera, J. C. A multi-start evolutionary local search for the one-commodity pickup and delivery traveling salesman problem. Annals of Operations Research. 2022; 316(2): 979-1011.
• [7] Zhang, J, Van Woensel, T. Dynamic vehicle routing with random requests: A literature review. International Journal of Production Economics. 2023; 256: 108751.
• [8] Li, Y, Yang, J. The last-mile delivery vehicle routing problem with handling cost in the front warehouse mode. Computers & Industrial Engineering. 2024; 190: 110076.
• [9] Bektaş, T, Laporte, G. The pollution-routing problem. Transp. Res. Part B Methodol. 2011; 45 (8): 1232-1250.
• [10] Kopfer, H,W, Schönberger, J, Kopfer, H. Reducing greenhouse gas emissions of a heterogeneous vehicle fleet. Flex. Serv. Manuf. J. 2014; 26 (1): 221-248.
• [11] Adiba, E, E, Aahmed, E, A, Youssef, B. The green capacitated vehicle routing problem: optimizing of emissions of greenhouse gas. In: 2014 International Conference on Logistics Operations Management. 2014; 161-167.
• [12] Psychas, I.-D, Marinaki, M, Marinakis, Y. A Parallel Multi-Start NSGA II Algorithm for Multiobjective Energy Reduction Vehicle Routing Problem. Springer International Publishing, Cham. 2015; 336-350.
• [13] Desaulniers, G, Errico, F, Irnich, S, Schneider, M. Exact algorithms for electric vehicle-routing problems with time windows. Oper. Res. 2016; 64 (6): 1388–1405.
• [14] Ashtineh, H, Pishvaee, M,S. Alternative fuel vehicle-routing problem: a life cycle analysis of transportation fuels. J. Clean. Prod. 2019; 219: 166-182.
• [15] Amiri, A, Amin, S. H, Zolfagharinia, H. A bi-objective green vehicle routing problem with a mixed fleet of conventional and electric trucks: Considering charging power and density of stations. Expert Systems with Applications. 2023; 213: 119228.
• [16] Lou, P, Zhou, Z, Zeng, Y, Fan, C. Vehicle routing problem with time windows and carbon emissions: a case study in logistics distribution. Environmental Science and Pollution Research. 2024; 1-21.
• [17] Garside, A. K, Ahmad, R., Muhtazaruddin, M, N, B. A Recent Review of Solution Approaches for Green Vehicle Routing Problem and its variants. Operations Research Perspectives. 2024; 100303.
• [18] Asghari, M, Al-e, S, M, J, M. Green vehicle routing problem: A state-of-the-art review. International Journal of Production Economics. 2021; 231: 107899.
• [19] Moghdani, R, Salimifard, K, Demir, E, Benyettou, A. The green vehicle routing problem: A systematic literature review. Journal of Cleaner Production. 2021; 279: 123691.
• [20] Stević, Ž, Tanackov, I, Vasiljević, M, Vesković, S. Evaluation in logistics using combined AHP and EDAS method. In Proceedings of the XLIII international symposium on operational research. Belgrade, Serbia. 2016; 20-23.
• [21] Karatop, B, Taşkan, B, Adar, E, Kubat, C. Decision analysis related to the renewable energy investments in Turkey based on a Fuzzy AHP-EDAS-Fuzzy FMEA approach. Computers & Industrial Engineering. 2021; 151: 106958.
• [22] Narad, A, Josh, M. Selection of Optimum Fuel Blend Using AHP and EDAS Analysis. Available at SSRN 3712853. 2020.
• [23] Zadeh, L.A. Fuzzy sets, Inf. Control. 1965; 8 (3): 338–353.
• [24] Zhang, X. Multicriteria pythagorean fuzzy decision analysis: a hierarchical qualiflex approach with the closeness index-based ranking methods, Inform. Sci. 2016; 330: 104–124.
• [25] Yager, R, R. Pythagorean fuzzy subsets, in: IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), IEEE. 2013; 57–61.
• [26] Zeng S, Chen, J, Li, X. A. hybrid method for Pythagorean fuzzy multiplecriteria decision making, Int. J. Inf. Technol. Decis. Mak. 2016; 15 (02): 403–422.
• [27] Saaty, T, L, Bennett, J, P. A theory of analytical hierarchies applied to political candidacy. Behavioral Science. 1977; 22: 237-245.
• [28] Sarkar, B, Biswas, A. Pythagorean fuzzy AHP-TOPSIS integrated approach for transportation management through a new distance measure. Soft Computing. 2021; 25(5): 4073-4089.
• [29] Zhou, F, Chen, T, Y. An integrated multicriteria group decision-making approach for green supplier selection under Pythagorean fuzzy scenarios. IEEE Access. 2020; 8: 165216-165231.
• [30] Demir, E, Ak, M, F, Sarı, K. Pythagorean fuzzy based AHP-VIKOR integration to assess rail transportation systems in Turkey. International Journal of Fuzzy Systems. 2023; 25(2): 620-632.
• [31] Farooq, D. Application of pythagorean fuzzy analytic hierarchy process for assessing driver behavior criteria associated to road safety. Journal of Soft Computing and Decision Analytics. 2024; 2(1): 144-158.
• [32] Ghorabaee, M, K, Zavadskas, E K, Olfat, L., Turskis, Z. Multi-criteria inventory classification using a new method of Evaluation Based on Distance from Average Solution (EDAS). Informatica. 2015; 26(3): 435–451.
• [33] Simić, V, Milovanović, B, Pantelić, S, Pamučar, D, Tirkolaee, E, B. Sustainable route selection of petroleum transportation using a type-2 neutrosophic number based ITARA-EDAS model. Information Sciences. 2023; 622: 732-754.
• [34] Bakioglu, G. Selection of sustainable transportation strategies for campuses using hybrid decision-making approach under picture fuzzy sets. Technological Forecasting and Social Change. 2024; 206: 123567.
• [35] Krishankumar, R, Pamucar, D, Deveci, M, Ravichandran, K. S. Prioritization of zero-carbon measures for sustainable urban mobility using integrated double hierarchy decision framework and EDAS approach. Science of The Total Environment. 2021; 797: 149068.
• [36] Moghdani, R, Salimifard, K, Demir, E, Benyettou, A. The green vehicle routing problem: A systematic literature review. Journal of Cleaner Production. 2021; 279: 123691.
• [37] Polimeni, A, Vitetta, A. Network design and vehicle routing problems in road transport systems: Integrating models and algorithms. Transportation Engineering. 2024; 16: 100247.
• [38] Schmidt, M, E. Integrating routing decisions in public transportation problems. New York: Springer. 2014.