A multi-valued neutrosophic ARAS model for economic resilience in uncertain olive farming systems

DOI: https://doi.org/10.3846/tede.2026.26279

Abstract

This study proposes the integration of Multi-Valued Neutrosophic Numbers (MVNNs) with the Additive Ratio Assessment (ARAS) method to conduct an economic evaluation of multi-criteria decision-making (MCDM) problems under high uncertainty. MVNNs enhance traditional neutrosophic approaches by allowing multiple degrees of truth, indeterminacy, and falsity, providing a richer representation of the ambiguous and often conflicting economic data prevalent in agricultural planning. The proposed MVN-ARAS framework is presented with formal definitions, aggregation operators, and a step-by-step decision-making procedure tailored for economic assessment. Its effectiveness is demonstrated through an illustrative investment case and a real-world application to prioritize and economically evaluate climate change adaptation strategies for smallholder olive growers in Chile’s Coquimbo Region. Results show that drought-resistant olive cultivars and water-saving irrigation technologies achieve the highest relative utility values, indicating their dominant role in improving economic resilience under prolonged water scarcity. In contrast, diversification-oriented measures – such as value-added olive products and rural ecotourism – rank lower but still contribute to long-term income stability. These findings highlight the ability of the MVN-ARAS framework to deliver a structured economic evaluation, a robust ranking of alternatives based on cost-benefit and risk criteria, and improved handling of conflicting expert opinions, offering decision-makers a transparent and reliable tool for strategic investment and resource allocation to enhance economic resilience.

Keywords:

multi-valued neutrosophic numbers, additive ratio assessment, neutrosophic decision-making, multi-criteria decision making, climate change adaptation, economic resilience

How to Cite

Montenegro-Dos Santos, F., & Hashemkhani Zolfani, S. (2026). A multi-valued neutrosophic ARAS model for economic resilience in uncertain olive farming systems. Technological and Economic Development of Economy, 32(2), 727–764. https://doi.org/10.3846/tede.2026.26279

Share

Published in Issue
May 12, 2026
Abstract Views
0

References

Abdalla, M. A. (2025). GIS-based identification of optimal rainwater harvesting sites to support irrigation in Egypt’s northwestern coastal region. Sustainable Geosciences: People, Planet and Prosperity, 1, Article 100004. https://doi.org/10.1016/j.susgeo.2025.100004

Abro, Z., Kassie, M., Tiku, H. A., Taye, B., Ayele, Z. A., & Ayalew, W. (2022). The impact of beekeeping on household income: evidence from north-western Ethiopia. Heliyon, 8(5), Article e09492. https://doi.org/10.1016/j.heliyon.2022.e09492

Adalı, E. A., & Tuş, A. (2023). ARAS method based on Z-numbers in FMEA. Quality and Reliability Engineering International, 39(7), 3059–3081. https://doi.org/10.1002/qre.3416

Adi, B., Dag, A., Ben-Dor, E., Gabay, G., & Barazani, O. (2025). Exploring drought tolerance in wild and traditional olive varieties from the Southern Levant. Frontiers in Plant Science, 16, Article 1547174. https://doi.org/10.3389/fpls.2025.1547174

Ahmed, F., & Kilic, K. (2019). Fuzzy analytic hierarchy process: A performance analysis of various algorithms. Fuzzy Sets and Systems, 362, 110–128. https://doi.org/10.1016/j.fss.2018.08.009

Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87–96. https://doi.org/10.1016/S0165-0114(86)80034-3

Bahrami, Y., Hassani, H., & Maghsoudi, A. (2019). BWM-ARAS: A new hybrid MCDM method for CU prospectivity mapping in the Abhar area, NW Iran. Spatial Statistics, 33, Article 100382. https://doi.org/10.1016/j.spasta.2019.100382

Bhavsar, D., Limbasia, B., Mori, Y., Imtiyazali Aglodiya, M., & Shah, M. (2023). A comprehensive and systematic study in smart drip and sprinkler irrigation systems. Smart Agricultural Technology, 5, Article 100303. https://doi.org/10.1016/j.atech.2023.100303

Biswas, P., Pramanik, S., & Giri, B. C. (2015). TOPSIS method for multi-attribute group decision-making under single-valued neutrosophic environment. Neural Computing and Applications, 27, 727–737. https://doi.org/10.1007/s00521-015-1891-2

Borah, G., & Dutta, P. (2024, December). Fuzzy risk analysis in crop selection using information measures on quadripartitioned single-valued neutrosophic sets. Expert Systems with Applications, 255, Article 124750. https://doi.org/10.1016/j.eswa.2024.124750

Brauers, W. K. M., & Zavadskas, E. K. (2010). Project management by MULTIMOORA as an instrument for transition economies. Technological and Economic Development of Economy, 16(1), 5–24. https://doi.org/10.3846/tede.2010.01

Büyüközkan, G., & Göçer, F. (2018). An extension of ARAS methodology under interval valued intuitionistic fuzzy environment for digital supply chain. Applied Soft Computing, 69, 634–654. https://doi.org/10.1016/j.asoc.2018.04.040

Büyüközkan, G., & Güler, M. (2020). Smart watch evaluation with integrated hesitant fuzzy linguistic SAW-ARAS technique. Measurement, 153, Article 107353. https://doi.org/10.1016/j.measurement.2019.107353

Campana, P. E., Papic, I., Jakobsson, S., & Yan, J. (2022). Photovoltaic water pumping systems for irrigation: Principles and advances. In S. Gorjian & P. E. Campana (Eds.), Solar energy advancements in agriculture and food production systems (pp. 113–157). Academic Press. https://doi.org/10.1016/B978-0-323-89866-9.00007-9

Chatterjee, R., Majumdar, P., & Samanta, S. (2016). On some similarity measures and entropy on quadripartitioned single valued neutrosophic sets. Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology , 30 (4), 2475–2485. https://doi.org/10.3233/IFS-152017

Chehab, H., Tekaya, M., Hajlaoui, H., Abdelhamid, S., Gouiaa, M., Sfina, H., Chihaoui, B., Boujnah, D., & Mechri, B. (2020). Complementary irrigation with saline water and soil organic amendments modified soil salinity, leaf Na+, productivity and oil phenols of olive trees (cv. Chemlali) grown under semiarid conditions. Agricultural Water Management, 237, Article 106183. https://doi.org/10.1016/j.agwat.2020.106183

Congress of Chile. (1981, August 13). Código de aguas. [The Water Code of Chile, DLF 1122) https://www.bcn.cl/leychile/navegar?idNorma=5605

Congress of Chile. (1985, October 22). Ley de fomento a la inversión privada en obras de riego y drenaje [Approves rules for the promotion of private investment in irrigation and drainage works (Law No. 18450)]. https://www.bcn.cl/leychile/navegar?idNorma=29855

Congress of Chile. (2010, January 12). Crea el ministerio, el servicio de evaluación ambiental y la superintendencia del medio ambiente [Creates the Ministry, the environmental assessment service and the superintendence of the environment (Law No. 20417)] https://www.bcn.cl/leychile/navegar?idNorma=1010459

Dahooie, J. H., Zavadskas, E., Abolhasani, M., Vanaki, A., & Turskis, Z. (2018). A novel approach for evaluation of projects using an interval-valued fuzzy additive ratio assessment (ARAS) method: A case study of oil and gas well drilling projects. Symmetry, 10(2), Article 45. https://doi.org/10.3390/sym10020045

Dehkordi, M. F., Hatefi, S. M., & Tamošaitienė, J. (2025). An integrated Fuzzy Shannon entropy and Fuzzy ARAS model using risk indicators for water resources management under uncertainty. Sustainability, 17(11), Article 5108. https://doi.org/10.3390/su17115108

Demirel, T., Demirel, N. Ç., & Kahraman, C. (2008). Fuzzy analytic hierarchy process and its application. In C. Kahraman (Ed.), Fuzzy multi-criteria decision making: Theory and applications with recent developments (pp. 53–83). Springer. https://doi.org/10.1007/978-0-387-76813-7_3

Ecer, F. (2018). An integrated Fuzzy AHP and ARAS model to evaluate mobile banking services. Technological and Economic Development of Economy, 24(2), 670–695. https://doi.org/10.3846/20294913.2016.1255275

Fernández, F. J., Vásquez-Lavín, F., Ponce, R. D., Garreaud, R., Hernández, F., Link, O., Zambrano, F., & Hanemann, M. (2023). The economics impacts of long- run droughts: Challenges, gaps, and way forward. Journal of Environmental Management, 344, Article 118726. https://doi.org/10.1016/j.jenvman.2023.118726

Fuentes, I., Fuster, R., Avilés, D., & Vervoort, W. (2021). Water scarcity in central Chile: The effect of climate and land cover changes on hydrologic resources. Hydrological Sciences Journal, 66(6), 1028–1044. https://doi.org/10.1080/02626667.2021.1903475

Ghorbani, M., Eskandari-Damaneh, H., Cotton, M., Ghoochani, O. M., & Borji, M. (2021). Harnessing indigenous knowledge for climate change-resilient water management – lessons from an ethnographic case study in Iran. Climate and Development, 13(9), 766–779. https://doi.org/10.1080/17565529.2020.1841601

Ghram, M., & Frikha, H. M. (2022). Multiple hierarchically structured criteria in ARAS method under fuzzy environment. International Journal of Fuzzy System Applications, 11(1), 1–19. https://doi.org/10.4018/IJFSA.315013

Gorzałczany, M. B. (1987). A method of inference in approximate reasoning based on interval-valued fuzzy sets. Fuzzy Sets and Systems, 2 (1), 1–17. https://doi.org/10.1016/0165-0114(87)90148-5

Gullón, P., Gullón, B., Astray, G., Carpena, M., Fraga-Corral, M., Prieto, M. A., & Simal-Gandara, J. (2020). Valorization of by-products from olive oil industry and added-value applications for innovative functional foods. Food Research International, 137, Article 109683. https://doi.org/10.1016/j.foodres.2020.109683

Herrera-Cáceres, C., Pérez-Galarce, F., Álvarez Miranda, E., & Candia-Véjar, A. (2017). Optimization of the harvest planning in the olive oil production: A case study in Chile. Computers and Electronics in Agriculture, 141, 147–159. https://doi.org/10.1016/j.compag.2017.07.017

Hu, Y., Al-Barakati, A., & Rani, P. (2022). Investigating the internet-of- things (IoT) risks for supply chain management using q-rung orthopair fuzzy-SWARA-ARAS framework. Technological and Economic Development of Economy, 30(2), 376–401. https://doi.org/10.3846/tede.2022.16583

Hwang, C.-L., & Yoon, K. (1981). Multiple attribute decision making: Methods and applications a state-of-the-art survey. Springer. https://doi.org/10.1007/978-3-642-48318-9

Kamal, N. L. A. M., Abdullah, L., Abdullah, I., & Saqlain, M. (2020). Multi-valued interval neutrosophic linguistic soft set theory and its application in knowledge management. CAAI Transactions Intelligence Technology, 5(3), 200–208. https://doi.org/10.1049/trit.2020.0036

Kamruzzaman, M. (2022). Impact of social media on geopolitics and economic growth: Mitigating the risks by developing artificial intelligence and cognitive computing tools. Computational Intelligence and Neuroscience. https://doi.org/10.1155/2022/7988894

Karimi, H., & Nikkhah-Farkhani, Z. (2022). Performance appraisal of knowledge workers using augmented additive ratio assessment (A-ARAS) method: A case study. IEEE Transactions on Engineering Management, 69, 2285–2295. https://doi.org/10.1109/TEM.2020.3009134

Liao, H., Wen, Z., & Liu, L. (2019). Integrating BWM and ARAS under hesitant linguistic environment for digital supply chain finance supplier section. Technological and Economic Development of Economy, 25(6), 1188–1212. https://doi.org/10.3846/tede.2019.10716

Lin, M.-P., Lin, C.-H., Llonch-Molina, N., & Marine-Roig, E. (2025). The impact of olive oil tourism on multisensory experiences and tourist loyalty. International Journal of Gastronomy and Food Science, 40, Article 101195. https://doi.org/10.1016/j.ijgfs.2025.101195

Liu, N., & Xu, Z. (2021). An overview of ARAS method: Theory development, application extension, and future challenge. International Journal of Intelligent Systems, 36, 3524–3565. https://doi.org/10.1002/int.22425

Liu, P., Zhang, L., Liu, X., & Wang, P. (2016). Multi-valued neutrosophic number Bonferroni mean operators with their applications in multiple attribute group decision making. International Journal of Information Technology Decision Making, 15(5), 1181–1210. https://doi.org/10.1142/S0219622016500346

Liu, Y., Eckert, C. M., & Earl, C. (2020). A review of fuzzy AHP methods for decision- making with subjective judgements. Expert Systems with Applications, 161, Article 113738. https://doi.org/10.1016/j.eswa.2020.113738

Mardani, A., Jusoh, A., Zavadskas, E. K., Khalifah, Z., & Nor, K. M. (2020). Fuzzy MCDM for agricultural sustainability: A review. Journal of Cleaner Production, 276, Article 124187.

Martina, D. J. S., & Deepa, G. (2023, 09). Application of multi-valued rough neutrosophic set and matrix in multi-criteria decision-making: Multi-valued neutrosophic rough set and matrix. Mathematics in Applied Sciences and Engineering, 4(3), 227–248. https://doi.org/10.5206/mase/16636

Mirzabaev, A., Bezner Kerr, R., Hasegawa, T., Pradhan, P., Wreford, A., Cristina Tirado von der Pahlen, M., & Gurney-Smith, H. (2023). Severe climate change risks to food security and nutrition. Climate Risk Management, 39, Article 100473. https://doi.org/10.1016/j.crm.2022.100473

Mishra, A. R., & Rani, P. (2021). A q-rung orthopair fuzzy ARAS method based on entropy and discrimination measures: An application of sustainable recycling partner selection. Journal of Ambient Intelligence and Humanized Computing, 14, 6897–6918. https://doi.org/10.1007/s12652-021-03549-3

Mishra, A. R., Rani, P., Saha, A., & Hezam, I. M. (2022). Interval-valued fuzzy ARAS for climate-smart agriculture. Environmental Science and Pollution Research, 29(15), 21234–21251.

Mishra, V., Seyedzenouzi, G., Almohtadi, A., Chowdhury, T., Khashkhusha, A., Axiaq, A., Wong, W. Y. E., & Harky, A. (2021). Health inequalities during COVID-19 and their effects on morbidity and mortality. Journal of Healthcare Leadership, 13, 19–26. https://doi.org/10.2147/JHL.S270175

Mora, F., Tapia, F., Scapim, C. A., & Martins, E. N. (2007). Vegetative growth and early production of six olive cultivars in southern Atacama Desert, Chile. Journal of Central European Agriculture, 8 (3), 269–276.

Morehead, M. S., & Scarbrough, C. (2018). Emergence of global antibiotic resistance. Primary Care: Clinics in Office Practice, 45(3), 467–484. https://doi.org/10.1016/j.pop.2018.05.006

Nădăban, S., Dzitac, S., & Dzitac, I. (2016). Fuzzy TOPSIS: A general view. Procedia Computer Science, 91, 823–831. https://doi.org/10.1016/j.procs.2016.07.088

Opricovic, S., & Tzeng, G.-H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445–455. https://doi.org/10.1016/S0377-2217(03)00020-1

Paelinck, J. (1978). Qualiflex: A flexible multiple-criteria method. Economics Letters, 1(3), 193–197. https://doi.org/10.1016/0165-1765(78)90023-X

Palczewski, K., & Sałabun, W. (2019). The fuzzy topsis applications in the last decade. Procedia Computer Science, 159, 2294–2303. https://doi.org/10.1016/j.procs.2019.09.404

Peng, J.-j., Wang, J.-q., Wu, X.-h., Wang, J., & Chen, X.-h. (2014). Multi-valued neutrosophic sets and power aggregation operators with their applications in multi-criteria group decision-making problems. International Journal of Computational Intelligence Systems, 8, 345–363. https://doi.org/10.1080/18756891.2015.1001957

Peng, J., Wang, J.-q., Hu, J., & Tian, C. (2018). Multi-criteria decision-making approach based on multi-valued neutrosophic geometric weighted choquet integral heronian mean operator. Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, 35(3), 3661–3674. https://doi.org/10.3233/JIFS-18249

Peng, J., Wang J.-q., & Wu, X.-h. (2017a). An extension of the electre approach with multi-valued neutrosophic information. Neural Computing and Applications, 28, 1011–1022. https://doi.org/10.1007/s00521-016-2411-8

Peng, J., Wang, J.-q., & Yang, W.-E. (2017b). A multi-valued neutrosophic qualitative flexible approach based on likelihood for multi-criteria decision-making problems. International Journal of Systems Science, 48(2), 425–435. https://doi.org/10.1080/00207721.2016.1218975

Peng, J., & Tian, C. (2018). Multi-valued neutrosophic distance-based QUALIFLEX method for treatment selection. Information, 9(12), Article 327. https://doi.org/10.3390/info9120327

Peng, J., & Wang, J.-Q. (2015). Multi-valued neutrosophic sets and its application in multi-criteria decision-making problems. Neutrosophic Sets and Systems, 10, 3–17.

Raina, R. S., & Longino, H. (2025). Community-led institutional innovation: Groundwater sharing, values and relationships in India’s rainfed farming systems. Studies in History and Philosophy of Science, 112, 102–111. https://doi.org/10.1016/j.shpsa.2025.06.011

Rani, P., Mishra, A., Saha, A., Hezam, I. M., & Pamučar, D. (2021). Fermatean fuzzy Heronian mean operators and MEREC-based additive ratio assessment method: An application to food waste treatment technology selection. International Journal of Intelligent Systems, 37, 2612–2647. https://doi.org/10.1002/int.22787

Roy, B. (1968). Classement et choix en présence de points de vue multiples (la méthode electre) [Classification and selection in the presence of multiple viewpoints (the ELECTRE method)]. Revue Française d’Informatique et de Recherche Opérationnelle, 2(8), 57–75. https://doi.org/10.1051/ro/196802V100571

Saaty, R. W. (1987). The analytic hierarchy process – what it is and how it is used. Mathematical Modelling, 9(3–5), 161–176. https://doi.org/10.1016/0270-0255(87)90473-8

Salam, M. A., Al-Amin, M. Y., Salam, M. T., Pawar, J. S., Akhter, N., Rabaan, A. A., & Alqumber, M. A. A. (2023). Antimicrobial resistance: A growing serious threat for global public health. Healthcare, 11(13), Article 1946. https://doi.org/10.3390/healthcare11131946

Shahmohammad, F. N., Pourrahimian, Y., & Akbari-Gharalari, N. (2024). Synthesizing complexity: Trends, challenges, and future directions in fuzzy- based multicriteria decision-making (FMCDM) methods. Applied Soft Computing, 167, Article 112362. https://doi.org/10.1016/j.asoc.2024.112362

Smarandache, F. (2004). Neutrosophic set – a generalization of the intuitionistic fuzzy set. International Journal of Pure and Applied Mathematics, 24.

Smarandache, F. (2006, May 10–12). Neutrosophic set – a generalization of the intuitionistic fuzzy set. In Proceedings of the 2006 IEEE International Conference on Granular Computing (pp. 38–42). Atlanta, GA, USA. IEEE. https://doi.org/10.1109/GRC.2006.1635754

Stević, Ž., Ersoy, N., Başar, E. E., & Baydaş, M. (2024, November). Addressing the global logistics performance index rankings with methodological insights and an innovative decision support framework. Applied Sciences, 14(22), Article 10334. https://doi.org/10.3390/app142210334

Thakkar, J. J. (2021). Additive ratio assessment method (ARM/ARAS). In Multi- criteria decision making (Vol. 336, pp. 239–252). Springer. https://doi.org/10.1007/978-981-33-4745-8_14

Tugendhaft, Y., Eppel, A., Kerem, Z., Barazani, O., Ben-Gal, A., Kadereit, J. W., & Dag, A. (2016). Drought tolerance of three olive cultivars alternatively selected for rain fed or intensive cultivation. Scientia Horticulturae, 199, 158–162. https://doi.org/10.1016/j.scienta.2015.12.043

Turskis, Z., Lazauskas, M., & Zavadskas, E. K. (2012). Fuzzy multiple criteria assessment of construction site alternatives for non-hazardous waste incineration plant in Vilnius city, applying ARAS-F and AHP methods. Journal of Environmental Engineering and Landscape Management, 20(2), 110–120. https://doi.org/10.3846/16486897.2011.645827

van de Loo, M., Camacho Poyato, E., van Halsema, G., & Rodríguez Díaz, J. A. (2024). Defining the optimization strategy for solar energy use in large water distribution networks: A case study from the Valle Inferior irrigation system, Spain. Renewable Energy, 228, Article 120610. https://doi.org/10.1016/j.renene.2024.120610

Wang, H., Smarandache, F., & Sunderraman, R. (2010). Single valued neutrosophic sets. In F. Smarandache (Ed.), Multispace & multistructure. Neutrosophic transdisciplinary (100 Collected Papers of Sciences) (Vol. IV, pp. 410–413). North-European Scientific Publishers.

Wang, J., & Li, X. (2015). TODIM method with multi-valued neutrosophic sets. Control and Decision, 30(6), 1139–1145.

Wang, J.-W., Ma, L.-y., Gómez del Campo, M., Zhang, D.-s., Deng, Y., & Jia, Z.-k. (2018). Youth tree behavior of olive (Olea europaea l.) cultivars in wudu, China: Cold and drought resistance, growth, fruit production, and oil quality. Scientia Horticulturae, 236, 106–122. https://doi.org/10.1016/j.scienta.2018.03.033

Xiao, F., Wang, J., & Wang, J.-Q. (2021). An improved MULTIMOORA method for multi-valued neutrosophic multi-criteria group decision-making based on prospect theory. Scientia Iranica.

Yager, R. R. (2014). Pythagorean membership grades in multicriteria decision making. IEEE Transactions on Fuzzy Systems, 22 (4), 958–965. https://doi.org/10.1109/TFUZZ.2013.2278989

Yager, R. R. (2016). Uncertainty modeling using fuzzy measures. Knowledge-Based Systems, 92, 1–8. https://doi.org/10.1016/j.knosys.2015.10.001

Yang, L., Li B., & Xu, H. (2018). Multi-valued neutrosophic linguistic power operators and their applications. Engineering Letters, 26(4), 518–525.

Ye, J. (2014). A multicriteria decision-making method using aggregation operators for simplified neutrosophic sets. Journal of Intelligent and Fuzzy Systems, 26(5), 2459–2466. https://doi.org/10.3233/IFS-130916

Ye, J., Song, J., & Du, S. (2020). Correlation coefficients of consistency neutrosophic sets regarding neutrosophic multi-valued sets and their multi-attribute decision-making method. International Journal of Fuzzy Systems, 24, 925–932. https://doi.org/10.1007/s40815-020-00983-x

Zadeh, L. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X

Zavadskas, E. K., & Turskis, Z. (2010). A new additive ratio assessment (ARAS) method in multicriteria decision-making. Technological and Economic Development of Economy, 16(2), 159–172. https://doi.org/10.3846/tede.2010.10

Zavadskas, E. K., Turskis, Z., Vilutienė, T., & Lepkova, N. (2017). Integrated group fuzzy multi-criteria model: Case of facilities management strategy selection. Expert Systems with Applications, 82, 317–331. https://doi.org/10.1016/j.eswa.2017.03.072

Zheng, Y., & Gong, B. (2024). Nexus between natural resources and digital economy: The role of geopolitical risk. Resources Policy, 89, Article 104600. https://doi.org/10.1016/j.resourpol.2023.104600

View article in other formats

CrossMark check

CrossMark logo

Published

2026-05-12

Issue

Section

Articles

How to Cite

Montenegro-Dos Santos, F., & Hashemkhani Zolfani, S. (2026). A multi-valued neutrosophic ARAS model for economic resilience in uncertain olive farming systems. Technological and Economic Development of Economy, 32(2), 727–764. https://doi.org/10.3846/tede.2026.26279

Share