Menu evaluation based on rough MAIRCA and BW methods

  • Slaviša N. Arsić University of Defence in Belgrade
  • Dragan Pamučar University of Defence in Belgrade
  • Milija Suknovic Faculty of Organisational Sciences in Belgrade
  • Miljojko Janošević Military Medical Academy in Belgrade


The evaluation of dishes represents basic activity in the structuring of menu, which allows the optimal use of resources in order to fully satisfy the expectations of users and restaurant management. This study presents a new approach in menu evaluation using the MultiAttributive Ideal-Real Comparative Analysis (MAIRCA) method, modified by application of rough numbers in dealing with imprecisions in group decision making and the Best-Worst Method (BWM) for determining the interval values of weight coefficients which objectivize the inconsistencies in expert judgment. The model was successfully tested on the menu of a collective nutrition restaurant, where the evaluation of six existing breakfast dishes and a new dish that should replace the worst ranking dish was carried out. Validation of the model was done by comparing the results obtained by application of MABAC and VIKOR methods, which confirmed high reliability. The validated values were discussed in sensitivity analysis, through 27 scenarios with the change in value of weight coefficients, which showed a large correlation of the obtained ranks and confirmed credibility of the obtained values.

Author Biographies

Slaviša N. Arsić, University of Defence in Belgrade
Department of Logistics, Assistant
Dragan Pamučar, University of Defence in Belgrade
Department of Logistics, Assistant Professor
Milija Suknovic, Faculty of Organisational Sciences in Belgrade
Dean at Faculty of Organisational Sciences in Belgrade, Full Professor
Miljojko Janošević, Military Medical Academy in Belgrade
Head of Logistics, Associate Professor


Arsic, S. (2014). Possibilities for improving the food system at the Military Academy - the economic aspect. Military Technical Courier, 4, 168-186.

Arsic, S., Pamucar, D., & Suknovic, M. (2018). Determining the weights of criteria in menu evaluation using Best-Worst method. In Proceedings of XVI International Symposium Doing Business in the Digital Age: Challenges, Approaches And Solutions, SymOrg 2018. University of Belgrade Faculty of Organizational Sciences. Zlatibor. 292-297.

Badi, I., & Ballem, M. (2018). Supplier Selection using rough BWM-MAIRCA model: A case study in pharmaceutical supplying in Libya. Decision Making: Applications in Management and Engineering, 1 (2), 15-32.

Bayou, M.E., & Bennett, L.B. (1992). Profitability analysis for table-service restaurants. Cornell Hotel and Restaurant Administration Quarterly, 33(2), 49-45.

Chatterjee, K., Pamučar, D., & Zavadskas, E.K. (2018). Evaluating the performance of suppliers based on using the R’AMATEL-MAIRCA method for green supply chain implementation in electronics industry. Journal of Cleaner Production, 184, 101-129.

Cohen, E., Mesika, R., & Schwartz, Z. (1998). A multidimensional approach to menu sales mix analysis. Praxis, 2(1), 130-144.

Gigović, Lj., Pamučar, D., Bajić, Z., & Milićević, M. (2016). The combination of expert judgment and GIS-MAIRCA analysis for the selection of sites for ammunition depot. Sustainability, 8(4), 1-30.

Hayes, D.K., & Huffman, L. (1985). Menu analysis: a better way. Cornell Hospitality Quarterly, 25(4), 64-70.

Horton, B.W. (2001). The effect of labor and menu category on menu classifications. Hospitality Review, 19(2), 35-46.

Kasawana, M.L., & Smith, D.J. (1982). Menu engineering. Hospitality Publications Inc. Lansing, MI

LeBruto, S., Ashley, R., & Quain, W. (1995). Menu Engineering: a model including labor. Hospitality Review, 13(1), 41-50.

Liu, Y., & Lv, Y. (2014). A Multiple Attribute Decision Making Method With Interval Rough Numbers Based on the Possibility. In Proceedings of the 10th International Conference on Natural Computation (ICNC '14). IEEE. Xiamen. China, 407–411.

McCall, M., & Lyn, A. (2008). The effects of restaurant menu item descriptions on perceptions of quality, price, and purchase intention. Journal of food service Business Research, 11(4), 439-445.

Miller, J.E. (1980). Menu Pricing and Strategy. CBI Publishing. Boston.

Miller, J.E. (1987). Menu Pricing and Strategy. Reinhold Van Nostrand. New York.

Morrison, P. (1996). Menu engineering in upscale restaurants. International Journal of Contemporery Hospitality Management, 8(4), 17-24.

Nunić, Z. (2018). Evaluation and selection of the PVC carpentry Manufacturer using the FUCOM-MABAC model, Operational Research in Engineering Sciences: Theory and Applications, 1(1), 13-28.

Pamučar, D., Petrović I., & Ćirović, G. (2018). Modification of the Best-Worst and MABAC methods: A novel approach based on interval-valued fuzzy-rough numbers. Expert systems with applications, 91, 89-106.

Pamučar, D., Mihajlović, M., Obradović, R., & Atanasković, P. (2017). Novel approach to group multi-criteria decision making based on interval rough numbers: Hybrid DEMATEL-ANP-MAIRCA model. Expert Systems with Applications, 88, 58-80.

Pamučar, D., Vasin, LJ., & Lukovac, V. (2014). Selection of railway level crossing for investing in security equipment using hybrid DEMATEL-MAIRCA. In: Proceedings of XVI International Scientific-expert Conference on Railway, Railcon. University of Niš. Niš. 89-92.

Pavesic, D. (1983). Cost-margin analysis: a third approach to menu pricing and design. International Journal of Hospitality Management, 2(3), 127-134.

Pawlak, Z. (1982). Rough sets. International Journal of Computer & Information Sciences, 11(5), 341–356.

Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega, 64, 126-130.

Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57.

Stanujkić, D., & Karabašević, D. (2018). An extension of the WASPAS method for decision-making problems with intuitionistic fuzzy numbers: a case of website evaluation. Operational Research in Engineering Sciences: Theory and Applications, 1(1), 29-39.

Taylor, J., & Brown, D. (2007). Menu analysis: a review of techniques and approaches. Hospitality Review, 25(2), 74-82.

Taylor, J., Reynolds, D., & Brown,D. (2009). Multi-factor menu analysis using data envelopment analysis. International Journal of Contemporary Hospitality Management, 21(2),213-225.

Tom, M., & Annaraud, K. (2017). A fuzzy multi-criteria decision making model for menu engineering. 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1-6.

Vasiljevic, M., Fazlollahtabar, H., Stevic, Z., Veskovic, S. (2018). A rough multicriteria approach for evaluation of the supplier criteria in automotive industry. Decision Making: Applications in Management and Engineering, 1(1), 82-96.

Zhai, L.Y., Khoo, L.P., & Zhong, Z.W. (2008). A rough set enhanced fuzzy approach to quality function deployment. International Journal of Advanced Manufacturing Technology, 37(5-6), 613-624.

Zhu, B., Xu, Z.S., & Xia, M.M. (2012). Hesitant fuzzy geometric Bonferroni means. Information Sciences, 205, 72–85.

Zhu, G.N., Hu, J., Qi, J., Gu, C.C., Peng, J.H. (2015). An integrated AHP and VIKOR for design concept evaluation based on rough number. Advanced Engineering Informatics, 29, 408–418.

Original Scientific Paper