Corporate Desktop Computer Procurement under Uncertain Criteria Weights: An SMAA-2 Application
DOI:
https://doi.org/10.63556/tisej.2026.1923Keywords:
Multi-Criteria Decision Making, SMAA, SMAA-2, Corporate Computer Procurement, Monte-Carlo SimulationAbstract
The renewal of an organization's IT infrastructure requires the simultaneous consideration of multiple, and sometimes conflicting, factors, including cost, performance, technical suitability, and service availability. In such decisions, ambiguity and disagreement regarding the priorities of the various stakeholder groups often restrict the use of conventional multi-criteria decision making (MCDM) methods that rely on deterministic weights.
In this paper, the Stochastic Multi-Criteria Acceptability Analysis (SMAA-2) method is applied to a corporate desktop computer procurement problem in which the criteria weights are uncertain. The criterion values of the alternatives were assumed to be deterministic, whereas preference uncertainty was modeled by sampling criterion weights from a uniform distribution over the weight simplex. Eighteen desktop computer models available in Turkish market were evaluated with respect to the main decision dimensions of processor performance, memory and storage characteristics, service accessibility and price, together with their related technical indicators. A total of 10,000 Monte Carlo iterations were performed in the JSMAA software to obtain the Rank Acceptability Index (RAI), the Central Weight Vectors (CWV), and the Confidence Factor (CF) for each alternative.
The results indicate that the Pro Tower 290 G9 model has the highest probability of being ranked first (55.5%) and the highest confidence factor (0.71) under the current criterion structure. The CWV results reveal the preference profiles under which each alternative is favorable, while the CF analysis indicates the relative stability of these preferences across varying weight structures. Overall, the study provides a probabilistic evaluation framework that explicitly accounts for preference uncertainty and offers a data-driven, flexible and interpretable decision support approach for corporate hardware procurement.
References
Angilella, S., Catalfo, P., Corrente, S., Giarlotta, A., Greco, S., & Rizzo, M. (2018). Robust sustainable development assessment with composite indices aggregating interacting dimensions: The hierarchical-SMAA-Choquet integral approach. Knowledge-Based Systems, 158, 136-153. https://doi.org/10.1016/j.knosys.2018.05.041
Arı, E. S., ve Gencer, C. (2019). The use and comparison of a deterministic, a stochastic, and a hybrid multiple-criteria decision-making method for site selection of wind power plants: An application in Turkey. Wind Engineering, 44(1), 60–74. https://doi.org/10.1177/0309524X19849831
Aspen, D. M., ve Sparrevik, M. (2020). Evaluating alternative energy carriers in ferry transportation using a stochastic multi-criteria decision analysis approach. Transportation Research Part D: Transport and Environment, 86, 102383. https://doi.org/10.1016/j.trd.2020.102383
Belton, V., ve Stewart, T. J. (2002). Multiple criteria decision analysis: An integrated approach (1st ed.). Springer. https://doi.org/10.1007/978-1-4615-1495-4
Boyacı, A. Ç. (2021). Stokastik çok kriterli kabul edilebilirlik analizi ile bitkisel atık yağ toplama kutuları için yer seçimi. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 10(1), 125-131. https://doi.org/10.28948/ngumuh.811240
Brynjolfsson, E., ve McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company.
Chai, J., Liu, J. N., & Ngai, E. W. (2013). Application of decision-making techniques in supplier selection: A systematic review of literature. Expert systems with applications, 40(10), 3872-3885. https://doi.org/10.1016/j.eswa.2012.12.040
Chen, Y., Ding, S., Zheng, H., Zhang, Y., & Yang, S. (2020). Decision support for personalized hospital choice using the DEX hierarchical model with SMAA. Knowledge and Information Systems, 62(8), 3059-3082. https://doi.org/10.1007/s10115-020-01448-1
Coco, G., Lagravinese, R., & Resce, G. (2020). Beyond the weights: a multicriteria approach to evaluate inequality in education. The Journal of Economic Inequality, 18(4), 469-489. https://doi.org/10.1007/s10888-020-09449-4
Corrente, S., Greco, S., ve Słowiński, R. (2012). Multiple criteria hierarchy process in robust ordinal regression. Decision Support Systems, 53(3), 660–674. https://doi.org/10.1016/j.dss.2012.05.022
Delice, E. K., & Can, G. F. (2018). An Integrated mental workload assessment approach based on Nasa-TLX and SMAA-2: A case study. Eskişehir osmangazi üniversitesi mühendislik ve mimarlık fakültesi dergisi, 26(2), 88-99. https://doi.org/10.31796/ogummf.384328
Demirdöğen, O., Erdal, D. Ö. H., & Kul, A. G. S. (2017). Dağıtım Merkezi Yer Seçimi Problemine Stokastik Bir Model Önerisi: TRA Bölgesinde Bir Uygulama. Ataturk University Journal of Economics & Administrative Sciences, 31(3).
Figueira, J. R., Greco, S., ve Ehrgott, M. (2005). Multiple criteria decision analysis: State of the art surveys (Vol. 78). Springer. https://doi.org/10.1007/b100605
Fu, Y., Lai, K. K., & Yu, L. (2021). Multi-nation comparisons of energy architecture performance: A group decision-making method with preference structure and acceptability analysis. Energy Economics, 96, 105139. https://doi.org/10.1016/j.eneco.2021.105139.
Giunipero, L. C. (1984). Purchasing’s role in computer buying: A comparative study. Industrial Marketing Management, 13(4), 241–248. https://doi.org/10.1016/0019-8501(84)90019-1
Govindan, K., & Jepsen, M. B. (2016). ELECTRE: A comprehensive literature review on methodologies and applications. European Journal of Operational Research, 250(1), 1-29. https://doi.org/10.1016/j.ejor.2015.07.019
Govindan, K., Rajendran, S., Sarkis, J., & Murugesan, P. (2015). Multi criteria decision making approaches for green supplier evaluation and selection: A literature review. Journal of Cleaner Production, 98, 66–83. https://doi.org/10.1016/j.jclepro.2013.06.046
Greco, S., Ishizaka, A., Matarazzo, B., & Torrisi, G. (2018). Stochastic multi-attribute acceptability analysis (SMAA): an application to the ranking of Italian regions. Regional studies, 52(4), 585-600. https://doi.org/10.1080/00343404.2017.1347612
Gupta, V., & Jain, N. (2017). Harnessing information and communication technologies for effective knowledge creation: Shaping the future of education. Journal of enterprise information management, 30(5), 831-855. https://doi.org/10.1108/JEIM-10-2016-0173
Hwang, C. L., ve Yoon, K. (1981). Multiple attribute decision making: Methods and applications. Springer-Verlag. https://doi.org/10.1007/978-3-642-48318-9
İç, Y. T. (2012). An experimental design approach using TOPSIS method for the selection of computer-integrated manufacturing technologies. Robotics and Computer-Integrated Manufacturing, 28(2), 245–256. https://doi.org/10.1016/j.rcim.2011.09.005
Karabay, S., Köse, E., ve Kabak, M. (2014). Facility Location Selection for a Public Organization by Stochastic Multi-criteria Acceptability Analysis. Ege Academic Review, 14(3), 361-369. https://izlik.org/JA48ZP98GH
Keeney, R. L. (1992). Value-focused thinking: A path to creative decisionmaking. Harvard University Press.
Keeney, R. L., ve Raiffa, H. (1993). Decisions with multiple objectives: Preferences and value trade-offs. Cambridge University Press. https://doi.org/10.1017/CBO9781139174084
Lahdelma R. and Salminen P. (2001). SMAA-2: Stochastic Multicriteria Acceptability Analysis for Group Decision Making. Operations Research, 49(3), 444-454. https://doi.org/10.1287/opre.49.3.444.11220
Lahdelma, R., & Salminen, P. (2002). Pseudo-criteria versus linear utility function in stochastic multi-criteria acceptability analysis. European Journal of Operational Research, 141(2), 454-469. https://doi.org/10.1016/S0377-2217(01)00276-4
Lahdelma, R., Hokkanen, J., & Salminen, P. (1998). SMAA-stochastic multiobjective acceptability analysis. European journal of operational research, 106(1), 137-143. https://doi.org/10.1016/S0377-2217(97)00163-X
Lahdelma, R., Makkonen, S., & Salminen, P. (2006). Multivariate Gaussian criteria in SMAA. European Journal of Operational Research, 170(3), 957-970. https://doi.org/10.1016/j.ejor.2004.08.022
Lahdelma, R., Miettinen, K., & Salminen, P. (2003). Ordinal criteria in stochastic multicriteria acceptability analysis (SMAA). European Journal of Operational Research, 147(1), 117-127. https://doi.org/10.1016/S0377-2217(02)00267-9
Laudon, K. C., & Laudon, J. P. (2004). Management information systems: Managing the digital firm. Pearson Educación.
Li, Z., Wu, X., Liu, F., Fu, Y., & Chen, K. (2019). Multicriteria ABC inventory classification using acceptability analysis. International Transactions in Operational Research, 26(6), 2494-2507. https://doi.org/10.1111/itor.12412
Mardani, A., Jusoh, A., & Zavadskas, E. K. (2015). Fuzzy multiple criteria decision-making techniques and applications – Two decades review from 1994 to 2014. Expert Systems with Applications, 42(8), 4126–4148. https://doi.org/10.1016/j.eswa.2015.01.003
Monczka, R. M., Handfield, R. B., Giunipero, L. C., ve Patterson, J. L. (2020). Purchasing and supply chain management (7th ed.). Cengage Learning.
Okul, D., Gencer, C., ve Kızılkaya Aydoğan, E. (2014). A method based on SMAA-TOPSIS for stochastic multi-criteria decision making and a real-world application. International Journal of Information Technology & Decision Making, 13(5), 957–978. https://doi.org/10.1142/S0219622014500175
Özden, Ü. H., & Erişlik, K. (2022). Stokastik Çok Kriterli Kabul Edilebilirlik Analizi (SMAA-2) ile Yaşanabilir En İyi Ülkelerin Sıralanması. Sosyal Bilimler Araştırma Dergisi, 11(1), 31-43.
Pelissari, R., Oliveira, M. C., Amor, S. B., Kandakoglu, A., & Helleno, A. L. (2020). SMAA methods and their applications: a literature review and future research directions. Annals of Operations Research, 293(2), 433-493. https://doi.org/10.1007/s10479-019-03151-z
Rana, P., & Vauhkonen, J. (2023). Stochastic multicriteria acceptability analysis as a forest management priority mapping approach based on airborne laser scanning and field inventory data. Landscape and Urban Planning, 230, 104637. https://doi.org/10.1016/j.landurbplan.2022.104637
Saaty, T. L. (1980). The analytic hierarchy process: Planning, priority setting, resources allocation. McGraw-Hill.
Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83–98. https://doi.org/10.1504/IJSSCI.2008.017590
Snijders, C. C. P., Tazelaar, F., ve Batenburg, R. S. (2003). Electronic decision support for procurement management: evidence on whether computers can make better procurement decisions. Journal of Purchasing and Supply Management, 9(5–6), 191–198. https://doi.org/10.1016/j.pursup.2003.09.001
Song, S., Wei, T., Yang, F., & Xia, Q. (2021). Stochastic multi-attribute acceptability analysis-based heuristic algorithms for multi-attribute project portfolio selection and scheduling problem. Journal of the Operational Research Society, 72(6), 1373-1389. https://doi.org/10.1080/01605682.2020.1718018
Song, S., & Zhang, Y. (2025). An SMAA-based approach for multi-attribute fixed resource allocation problem with random attribute values and uncertain weights. Journal of Modelling in Management, 20(4), 1246-1264. https://doi.org/10.1108/JM2-04-2024-0112
Tervonen, T. (2012). JSMAA: open source software for SMAA computations. International Journal of Systems Science, 45(1), 69–81. https://doi.org/10.1080/00207721.2012.659706
Tervonen, T., ve Figueira, J. R. (2008). A survey on stochastic multicriteria acceptability analysis methods. Journal of Multi‑Criteria Decision Analysis, 15(1‑2), 1–14. https://doi.org/10.1002/mcda.407
Tervonen, T., ve Lahdelma, R. (2007). Implementing stochastic multicriteria acceptability analysis. European Journal of Operational Research, 178(2), 500–513. https://doi.org/10.1016/j.ejor.2005.12.037
Thokala, P., & Duenas, A. (2012). Multiple criteria decision analysis for health technology assessment. Value in health, 15(8), 1172-1181. https://doi.org/10.1016/j.jval.2012.06.015
Tosun, Ö. (2017). Tedarikçi Değerlendirmede Stokastik Bir Karar Verme Yaklaşımı: Stokastik Çok Kriterli Kabul Edilebilirlik Analizi. Verimlilik Dergisi, (4), 111-121. https://izlik.org/JA58GT97SP
Tzeng, G. H., & Huang, J. J. (2011). Multiple attribute decision making: methods and applications. CRC press.
Xu, Y., Fu, Y., & Lai, K. K. (2022). A composite indicator methodology to rank national Olympic committees. Asia-Pacific Journal of Operational Research, 39(06), 2250035. https://doi.org/10.1142/S021759592250035X
Zavadskas, E. K., Turskis, Z., & Kildienė, S. (2014). State of art surveys of overviews on MCDM/MADM methods. Technological and economic development of economy, 20(1), 165-179. https://doi.org/10.3846/20294913.2014.892037
Zhu, F., Zhong, P. A., Xu, B., Chen, J., Sun, Y., Liu, W., & Li, T. (2020). Stochastic multi-criteria decision making based on stepwise weight information for real-time reservoir operation. Journal of Cleaner Production, 257, 120554. https://doi.org/10.1016/j.jclepro.2020.120554
Ziemba, P. (2020). Multi-criteria stochastic selection of electric vehicles for the sustainable development of local government and state administration units in Poland. Energies, 13(23), 6299. https://doi.org/10.3390/en13236299
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