INNDAGA: an environmental data acquisition innovation platform

INNDAGA is a multipurpose platform for conducting oceanographic surveys in coastal areas developed on an 8.5 m long inflatable boat. This concept allows the vessel to operate safely and with great manoeuvring flexibility in areas where larger research vessels cannot access (rocky areas, port ...) at low operational cost. Is fully integrated in an information management system to providing efficiency and effectiveness of strategic decision making.Peer Reviewe

But who learns what? On the risks of knowledge accumulation through networked learning in R&D

In big companies, managerial activities and organizational boundaries will over time hide most unevenly developed skill and knowledge distribution patterns; studying the organizations with the means of modern applied physics is thus quite difficult. People are forced to communicate along the organizational lines, and their personal preferences that could affect the communication networks are often dampened to nearly obsolete. In small companies, however, as well as other less structured non-business organizations, many network patterns exist, based on the preferred cooperation and communication behaviour of human beings, and are observable in various real-life situations. Given their free choice of either to solve the problem themselves or go to one of the colleagues to ask for help, and a preference based on the transactive memory of the organization (a word-of-mouth "reputation" information about who has the skill needed to solve the problem, or who solved the previous one with some similarity) will over time lead to most difficult problems always being solved by one or two key individuals. This paper tests this idea with an agent model to confirm the accumulation of critical knowledge to few individuals. Furthermore, the paper presents a network relation study in a 45-person software solution company. It seems the knowledge is on its way to become distributed according to power law – centralized more and more to a couple of individuals – also in the reality of this case company, even if there are not enough interactions in the five-year history of the company to prove this in a statistically significant way

A Multi-Criteria Reference Point Based Approach for Assessing Regional Innovation Performance in Spain

[EN] The evaluation of regional innovation performance through composite innovation indices can serve as a valuable tool for policy-making. While discussion on the best methodology to construct composite innovation indices continues, we are interested in deepening the use of reference levels and the aggregation issue. So far, additive aggregation methods are, largely, the most widespread aggregation rule, thus allowing for full compensability among single indicators. In this paper, we present an integrated assessment methodology to evaluate regional innovation performance using the Multi-Reference Point based Weak and Strong Composite Indicator (MRP-WSCI) approach, which allows defining reference levels and different degrees of compensability. As an example of application to the Regional Innovation Scoreboard, the proposed technique is developed to measure the innovation performance of Spain¿s regions taking into account Spanish and European reference levels. The main features of the proposed approach are: (i) absolute or relative reference levels could be previously defined by the decision maker; (ii) by establishing the reference levels, the resulting composite innovation index is an easy-to-interpret measure; and (iii) the non-compensatory strong composite indicator provides an additional layer of information for policy-making (iv) a visualization tool called Light-Diagram is proposed to track the specific strengths and weaknesses of the regions' innovation performance.This research has been partially supported by the Spanish Ministry of Economy and Competitiveness (Project ECO2016-76567-C4-4-R), by the Regional Government of Andalucia (research group SEJ-417), and by the ERDF funds (Project UMA18-FEDERJA-065).Garcia-Bernabeu, A.; Cabello, JM.; Ruiz, F. (2020). A Multi-Criteria Reference Point Based Approach for Assessing Regional Innovation Performance in Spain. Mathematics. 8(5):1-21. https://doi.org/10.3390/math8050797S12185Hauser, C., Siller, M., Schatzer, T., Walde, J., & Tappeiner, G. (2018). Measuring regional innovation: A critical inspection of the ability of single indicators to shape technological change. Technological Forecasting and Social Change, 129, 43-55. doi:10.1016/j.techfore.2017.10.019Makkonen, T., & van der Have, R. P. (2012). Benchmarking regional innovative performance: composite measures and direct innovation counts. Scientometrics, 94(1), 247-262. doi:10.1007/s11192-012-0753-2Asheim, B. T., Smith, H. L., & Oughton, C. (2011). Regional Innovation Systems: Theory, Empirics and Policy. Regional Studies, 45(7), 875-891. doi:10.1080/00343404.2011.596701Buesa, M., Heijs, J., & Baumert, T. (2010). The determinants of regional innovation in Europe: A combined factorial and regression knowledge production function approach. Research Policy, 39(6), 722-735. doi:10.1016/j.respol.2010.02.016Di Cagno, D., Fabrizi, A., Meliciani, V., & Wanzenböck, I. (2016). The impact of relational spillovers from joint research projects on knowledge creation across European regions. Technological Forecasting and Social Change, 108, 83-94. doi:10.1016/j.techfore.2016.04.021Capello, R., & Lenzi, C. (2012). Territorial patterns of innovation: a taxonomy of innovative regions in Europe. The Annals of Regional Science, 51(1), 119-154. doi:10.1007/s00168-012-0539-8Navarro, M., Gibaja, J. J., Bilbao-Osorio, B., & Aguado, R. (2009). Patterns of Innovation in EU-25 Regions: A Typology and Policy Recommendations. Environment and Planning C: Government and Policy, 27(5), 815-840. doi:10.1068/c0884rPinto, H. (2009). The Diversity of Innovation in the European Union: Mapping Latent Dimensions and Regional Profiles. European Planning Studies, 17(2), 303-326. doi:10.1080/09654310802553571Ruiz, F., El Gibari, S., Cabello, J. M., & Gómez, T. (2020). MRP-WSCI: Multiple reference point based weak and strong composite indicators. Omega, 95, 102060. doi:10.1016/j.omega.2019.04.003Hollenstein, H. (1996). A composite indicator of a firm’s innovativeness. An empirical analysis based on survey data for Swiss manufacturing. Research Policy, 25(4), 633-645. doi:10.1016/0048-7333(95)00874-8Gu *, W., & Tang, J. (2004). Link between innovation and productivity in Canadian manufacturing industries. Economics of Innovation and New Technology, 13(7), 671-686. doi:10.1080/1043890410001686806Tang, J., & Le, C. D. (2007). Multidimensional Innovation and Productivity. Economics of Innovation and New Technology, 16(7), 501-516. doi:10.1080/10438590600914585Kumar, S., Haleem, A., & Sushil. (2019). Assessing innovativeness of manufacturing firms using an intuitionistic fuzzy based MCDM framework. Benchmarking: An International Journal, 26(6), 1823-1844. doi:10.1108/bij-12-2017-0343Grupp, H., & Mogee, M. E. (2004). Indicators for national science and technology policy: how robust are composite indicators? Research Policy, 33(9), 1373-1384. doi:10.1016/j.respol.2004.09.007Schibany, A., & Streicher, G. (2008). The European Innovation Scoreboard: drowning by numbers? Science and Public Policy, 35(10), 717-732. doi:10.3152/030234208x398512Kozłowski, J. (2015). Innovation indices: the need for positioning them where they properly belong. Scientometrics, 104(3), 609-628. doi:10.1007/s11192-015-1632-4Carayannis, E. G., Goletsis, Y., & Grigoroudis, E. (2018). Composite innovation metrics: MCDA and the Quadruple Innovation Helix framework. Technological Forecasting and Social Change, 131, 4-17. doi:10.1016/j.techfore.2017.03.008Greco, S., Ishizaka, A., Tasiou, M., & Torrisi, G. (2018). On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness. Social Indicators Research, 141(1), 61-94. doi:10.1007/s11205-017-1832-9El Gibari, S., Gómez, T., & Ruiz, F. (2018). Building composite indicators using multicriteria methods: a review. Journal of Business Economics, 89(1), 1-24. doi:10.1007/s11573-018-0902-zRuiz, F., Cabello, J. M., & Luque, M. (2011). An application of reference point techniques to the calculation of synthetic sustainability indicators. Journal of the Operational Research Society, 62(1), 189-197. doi:10.1057/jors.2009.187Cabello, J. M., Ruiz, F., Pérez-Gladish, B., & Méndez-Rodríguez, P. (2014). Synthetic indicators of mutual funds’ environmental responsibility: An application of the Reference Point Method. European Journal of Operational Research, 236(1), 313-325. doi:10.1016/j.ejor.2013.11.031Ruiz, F., Cabello, J. M., & Pérez-Gladish, B. (2018). Building Ease-of-Doing-Business synthetic indicators using a double reference point approach. Technological Forecasting and Social Change, 131, 130-140. doi:10.1016/j.techfore.2017.06.005El Gibari, S., Gómez, T., & Ruiz, F. (2018). Evaluating university performance using reference point based composite indicators. Journal of Informetrics, 12(4), 1235-1250. doi:10.1016/j.joi.2018.10.003Mazziotta, M., & Pareto, A. (2017). Measuring Well-Being Over Time: The Adjusted Mazziotta–Pareto Index Versus Other Non-compensatory Indices. Social Indicators Research, 136(3), 967-976. doi:10.1007/s11205-017-1577-5Munda, G., & Nardo, M. (2009). Noncompensatory/nonlinear composite indicators for ranking countries: a defensible setting. Applied Economics, 41(12), 1513-1523. doi:10.1080/00036840601019364Cabello, J. M., Navarro, E., Prieto, F., Rodríguez, B., & Ruiz, F. (2014). Multicriteria development of synthetic indicators of the environmental profile of the Spanish regions. Ecological Indicators, 39, 10-23. doi:10.1016/j.ecolind.2013.11.01

Productivity, Digital Footprint and Sustainability in the Textile and Clothing Industry

[EN] In recent years, there has been a shift from the linear economic model on which the textile and clothing industry is based to a more sustainable model. However, to date, limited research on the relationship between sustainability commitment and firm productivity has focused on the textile and clothing industry. This study addresses this gap and aims to explore whether the digital footprint of small and medium-sized textile companies in terms of their sustainable performance is related to their productivity. To this end, the paper proposes an innovative model to monitor the companies’ commitment to sustainable issues by analyzing online data retrieved from their corporate websites. This information is merged with balance sheet data to examine the impact of sustainability practices, capital and human capital on productivity. The estimated firm’s total factor productivity is explained as a function of the sustainability digital footprint measures and additional control variables for a sample of 315 textile firms located in the region of Comunidad Valenciana, Spain.This work was partially funded by MCIN/AEI/10.13039/501100011033 under grant PID2019-107765RB-I00.Domenech, J.; Garcia-Bernabeu, A.; Diaz-Garcia, P. (2023). Productivity, Digital Footprint and Sustainability in the Textile and Clothing Industry. Editorial Universitat Politècnica de València. 319-326. https://doi.org/10.4995/CARMA2023.2023.1644631932

Patologías musculoesqueléticas y/o dolor en los músicos profesionales de orquesta: revisión bibliográfica

INTRODUCCIÓN: La interpretación musical requiere mucho tiempo de ensayo suponiendo movimientos repetitivos y esfuerzos significativos. Los músicos son susceptibles a padecer una gran variedad de patologías, sobre todo trastornos musculoesqueléticos. El término más utilizado para describir dicha patología es “playing-related musculoskeletal disorder” (PRMD). OBJETIVOS: Conocer las lesiones musculoesqueléticas y/o dolor en músicos profesionales de orquesta más prevalentes según la literatura científica. MÉTODOS: Revisión bibliográfica de artículos publicados entre el 1 de enero de 2011 y el 1 de abril del 2022 utilizando Pubmed y Scopus como base de datos. RESULTADOS: Un total de 20 artículos observacionales fueron seleccionados. El 65% confirma que la prevalencia de los PRMD está alrededor del 62,5% y el 95%. El 40% y el 30% indica que las mujeres y los músicos de cuerda son más susceptibles respectivamente. El 50% afirma que existe una relación entre el instrumento tocado y el lugar afectado y el 60% que los lugares comunes más afectados son la columna cervical y lumbar, y las extremidades superiores. Finalmente, dos de los artículos tratan la articulación temporomandibular concluyendo que es más frecuente en los músicos de viento. CONCLUSIONES: Existe una prevalencia entre el 62,5% y el 95% de PRMD en los músicos profesionales siendo más frecuente en las mujeres y los instrumentos de cuerda. La columna cervical y lumbar y las extremidades superiores son los lugares más afectados. Sería conveniente seguir investigando y concienciar a la población de los hábitos posturales para prevenir dichas lesiones.INTRODUCTION: Music performance requires a lot of rehearsal time involving repetitive movements and significant strain. Musicians are susceptible to a wide variety of pathologies, especially musculoskeletal disorders. The term most commonly used to describe such patology is “playing-related musculoskeletal disorder” (PRMD). OBJECTIVES: To know the most prevalent musculoskeletal injuries and/or pain in professional orchestra musicians according to scientific literature. METHODS: Bibliographic review of articles published between January 1st, 2011 and April 1st, 2022 using Pubmed and Scopus as database. RESULTS: A total of 20 observational articles were selected. 65% confirm that the prevalence of PRMD is around 62.5% and 95%. 40% and 30% indicate that women and stringed musician are more susceptible respectively. 50% state that there is a relationship between the instrument played and the affected site and 60% that the most common sites affected are the cervical and lumbar spine and the upper extremities. Finally, 2 of the articles deal with the temporomandibular joint, concluding that it is more frequent in wind players. CONCLUSIONS: There is a prevalence between 62,5% and 95% of PRMD in professional musicians, being more frequent in women and stringed instruments. The cervical and lumbar spine and upper extremities are the most affected sites. It would be convenient to continue researching and to make the population aware of postural habits in order to prevent these injuries

A Reference Point-Based Proposal to Build Regional Quality of Life Composite Indicators

[EN] There is a growing interest in research on the role that space plays in defining and measuring well-being or quality of life. In this paper, we propose to evaluate the regional quality of life using the Multi-Reference Point based Weak Strong Composite Indicator approach, to further enhance the quality of the sub-national analysis. The major motivation is to facilitate assessing the regional quality of life performance at different geographical scales and compensability levels. As an example of application, we compute the composite indicators for 19 Spanish regions to paint a comprehensive picture of the regional quality of life using two different geographical scales: the Spanish and the European ones. Moreover, we provide warning signals to regional, national and European policy-makers on the quality of life dimensions in which each region needs further improvements.This research was partially funded by the Spanish Ministry of Economy and Competitiveness (Project PID2019-104263RB-C42), from the Regional Government of Andalucía (Project P18-RT-1566), and by the EU ERDF operative program (Project UMA18-FEDERJA-065)Garcia-Bernabeu, A.; Cabello, JM.; Ruiz, F. (2021). A Reference Point-Based Proposal to Build Regional Quality of Life Composite Indicators. Social Indicators Research (Online). 1-20. https://doi.org/10.1007/s11205-021-02818-0S120Blancas, F., Caballero, R., González, M., Lozano-Oyola, M., & Pérez, F. (2010). Goal programming synthetic indicators: An application for sustainable tourism in andalusian coastal counties. Ecological Economics, 69(11), 2158–2172.Boggia, A., Massei, G., Pace, E., Rocchi, L., Paolotti, L., & Attard, M. (2018). Spatial multicriteria analysis for sustainability assessment: A new model for decision making. Land Use Policy, 71, 281–292.Booysen, F. (2002). An overview and evaluation of composite indices of development. Social Indicators Research, 59(2), 115–151.Cabello, J. M., Ruiz, F., Pérez-Gladish, B., & Méndez-Rodríguez, P. (2014). Synthetic indicators of mutual fund’s environmental responsibility: An application of the Reference Point Method. European Journal of Operational Research, 236(1), 313–325.Costa, D. S. (2015). Reflective, causal, and composite indicators of quality of life: A conceptual or an empirical distinction? Quality of Life Research, 24(9), 2057–2065.Durand, M. (2015). The OCDE better life initiative: How’s life? and the measurement of well-being. Review of Income and Wealth, 61(1), 4–17.El Gibari, S., Cabello, J. M., Gómez, T., & Ruiz, F. (2021). Composite indicators as decision making tools: The joint use of compensatory and non-compensatory schemes. International Journal of Information Technology and Decision Making, 20(3), 847–879.El Gibari, S., Gómez, T., & Ruiz, F. (2018). Evaluating university performance using reference point based composite indicators. Journal of Informetrics, 12(4), 1235–1250.El Gibari, S., Gómez, T., & Ruiz, F. (2019). Building composite indicators using multicriteria methods: A review. Journal of Business Economics, 89(1), 1–24.European Commission: Eurostat quality of life database. (2020). url http://ec.europa.eu/eurostat/data/database.Freudenberg, M. (2003). Composite indicators of country performance.Garcia-Bernabeu, A., Cabello, J. M., & Ruiz, F. (2020). A multi-criteria reference point based approach for assessing regional innovation performance in Spain. Mathematics, 8(5), 797.Goerlich, F. J., & Reig, E. (2021). Quality of life ranking of spanish cities: A non-compensatory approach. Cities, 109, 102979.Greco, S., Ishizaka, A., Tasiou, M., & Torrisi, G. (2018). On the methodological framework of composite indices: A review of the issues of weighting, aggregation, and robustness. Social Indicators Research, 141, 61–94.Greyling, T., & Tregenna, F. (2017). Construction and analysis of a composite quality of life index for a region of South Africa. Social Indicators Research, 131(3), 887–930.Hagerty, M. R., Cummins, R., Ferriss, A. L., Land, K., Michalos, A. C., Peterson, M., et al. (2001). Quality of life indexes for national policy: Review and agenda for research. Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique, 71(1), 58–78.INE: Indicadores de calidad de vida. (2020). url https://cutt.ly/Zj0L0qX.Ivaldi, E., Bonatti, G., Soliani, R., et al. (2014). Composite index for quality of life in italian cities: An application to urbes indicators. Review of Economics and Finance, 4(4)Karagiannis, R., & Karagiannis, G. (2020). Constructing composite indicators with shannon entropy: The case of human development index. Socio-Economic Planning Sciences, 70, 100701.Lagas, P., van Dongen, F., van Rijn, F., & Visser, H. (2015). Regional quality of living in Europe. Region, 2(2), 1–26.Malkina-Pykh, I. G., & Pykh, Y. A. (2008). Quality-of-life indicators at different scales: Theoretical background. Ecological Indicators, 8(6), 854–862.Marchante, A. J., & Ortega, B. (2006). Quality of life and economic convergence across Spanish regions, 1980–2001. Regional Studies, 40(5), 471–483.Mazziotta, M., & Pareto, A. (2016). On a generalized non-compensatory composite index for measuring socio-economic phenomena. Social Indicators Research, 127(3), 983–1003.Mazziotta, M., & Pareto, A. (2020). Composite indices construction: The performance interval approach. Social Indicators Research pp. 1–11.Nardo, M., Saisana, M., Saltelli, A., Tarantola, S., Hoffman, A., & Giovannini, E. (2008). Handbook on constructing composite indicators.OECD: Handbook on constructing composite indicators: methodology and user guide. (2008). Paris: OECD publishing.Patil, G.R., & Sharma, G. (2020). Urban quality of life: An assessment and ranking for indian cities. Transport Policy.Royuela, V., Suriñach, J., & Reyes, M. (2003). Measuring quality of life in small areas over different periods of time. Social Indicators Research, 64(1), 51–74.Ruiz, F., Cabello, J. M., & Luque, M. (2011). An application of reference point techniques to the calculation of synthetic sustainability indicators. Journal of the Operational Research Society, 62(1), 189–197.Ruiz, F., Cabello, J. M., & Pérez-Gladish, B. (2018). Building ease-of-doing-business synthetic indicators using a double reference point approach. Technological Forecasting and Social Change, 131, 130–140.Ruiz, F., El Gibari, S., Cabello, J.M., & Gómez, T. (2019). MRP-WSCI: Multiple reference point based weak and strong composite indicators. Omega.Saisana, M., & Tarantola, S. (2002). State-of-the-art report on current methodologies and practices for composite indicator development. Ispra: Joint Research Centre.Stiglitz, J.E., Sen, A., Fitoussi, J.P., et al. (2009). Report by the commission on the measurement of economic performance and social progress

Water supplies build the cities: the Canal de Isabel II as origin of the Metropolis of Madrid

Water supply systems in big cities are fundamental parts of their metabolism. They respond to a scenario of increasing demands, and they actually act as a catalyst for their growth. This last reason is essential to explain the development of some big cities. A model example of this is the city of Madrid and its water supply, built up and managed by the public enterprise Canal de Isabel II. Since its origins in the 19th century, the Canal laid down the foundations that allowed Madrid to develop and shape itself as a metropolis. Public works, dams, channels and reservoirs, constitute the technical solution to water supply as well as they extend the influence of the urban area to wider territory, connecting urban and rural. The paper studies the origins of the Canal and its principal works which enabled the metropolitan growth of Madrid until today, and analyzes the influence of this works in the development of the city, in the processes of exchange and water management. Public works are not just useful infrastructures in contemporary polis, they have strong influence in social cohesion and urban processes

Extended Fuzzy Analytic Hierarchy Process (E-FAHP): A General Approach

[EN] Fuzzy analytic hierarchy process (FAHP) methodologies have witnessed a growing development from the late 1980s until now, and countless FAHP based applications have been published in many fields including economics, finance, environment or engineering. In this context, the FAHP methodologies have been generally restricted to fuzzy numbers with linear type of membership functions (triangular numbers-TN-and trapezoidal numbers-TrN). This paper proposes an extended FAHP model (E-FAHP) where pairwise fuzzy comparison matrices are represented by a special type of fuzzy numbers referred to as (m,n)-trapezoidal numbers (TrN (m,n)) with nonlinear membership functions. It is then demonstrated that there are a significant number of FAHP approaches that can be reduced to the proposed E-FAHP structure. A comparative analysis of E-FAHP and Mikhailov's model is illustrated with a case study showing that E-FAHP includes linear and nonlinear fuzzy numbers.Reig-Mullor, J.; Pla Santamaría, D.; Garcia-Bernabeu, A. (2020). Extended Fuzzy Analytic Hierarchy Process (E-FAHP): A General Approach. Mathematics. 8(11):1-14. https://doi.org/10.3390/math8112014S114811Chai, J., Liu, J. N. K., & Ngai, E. W. T. (2013). Application of decision-making techniques in supplier selection: A systematic review of literature. Expert Systems with Applications, 40(10), 3872-3885. doi:10.1016/j.eswa.2012.12.040Tavana, M., Zareinejad, M., Di Caprio, D., & Kaviani, M. A. (2016). An integrated intuitionistic fuzzy AHP and SWOT method for outsourcing reverse logistics. Applied Soft Computing, 40, 544-557. doi:10.1016/j.asoc.2015.12.005Medasani, S., Kim, J., & Krishnapuram, R. (1998). An overview of membership function generation techniques for pattern recognition. International Journal of Approximate Reasoning, 19(3-4), 391-417. doi:10.1016/s0888-613x(98)10017-8Medaglia, A. L., Fang, S.-C., Nuttle, H. L. W., & Wilson, J. R. (2002). An efficient and flexible mechanism for constructing membership functions. European Journal of Operational Research, 139(1), 84-95. doi:10.1016/s0377-2217(01)00157-6Mikhailov, L. (2003). Deriving priorities from fuzzy pairwise comparison judgements. Fuzzy Sets and Systems, 134(3), 365-385. doi:10.1016/s0165-0114(02)00383-4Appadoo, S. S. (2014). Possibilistic Fuzzy Net Present Value Model and Application. Mathematical Problems in Engineering, 2014, 1-11. doi:10.1155/2014/865968Mikhailov, L., & Tsvetinov, P. (2004). Evaluation of services using a fuzzy analytic hierarchy process. Applied Soft Computing, 5(1), 23-33. doi:10.1016/j.asoc.2004.04.001Hepu Deng. (1999). Multicriteria analysis with fuzzy pairwise comparison. FUZZ-IEEE’99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315). doi:10.1109/fuzzy.1999.793038Van Laarhoven, P. J. M., & Pedrycz, W. (1983). A fuzzy extension of Saaty’s priority theory. Fuzzy Sets and Systems, 11(1-3), 229-241. doi:10.1016/s0165-0114(83)80082-7Buckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy Sets and Systems, 17(3), 233-247. doi:10.1016/0165-0114(85)90090-9Chang, D.-Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3), 649-655. doi:10.1016/0377-2217(95)00300-2Enea, M., & Piazza, T. (2004). Project Selection by Constrained Fuzzy AHP. Fuzzy Optimization and Decision Making, 3(1), 39-62. doi:10.1023/b:fodm.0000013071.63614.3dKrejčí, J., Pavlačka, O., & Talašová, J. (2016). A fuzzy extension of Analytic Hierarchy Process based on the constrained fuzzy arithmetic. Fuzzy Optimization and Decision Making, 16(1), 89-110. doi:10.1007/s10700-016-9241-0Cakir, O., & Canbolat, M. S. (2008). A web-based decision support system for multi-criteria inventory classification using fuzzy AHP methodology. Expert Systems with Applications, 35(3), 1367-1378. doi:10.1016/j.eswa.2007.08.041Isaai, M. T., Kanani, A., Tootoonchi, M., & Afzali, H. R. (2011). Intelligent timetable evaluation using fuzzy AHP. Expert Systems with Applications, 38(4), 3718-3723. doi:10.1016/j.eswa.2010.09.030Büyüközkan, G., & Güleryüz, S. (2016). A new integrated intuitionistic fuzzy group decision making approach for product development partner selection. Computers & Industrial Engineering, 102, 383-395. doi:10.1016/j.cie.2016.05.038Zheng, G., Zhu, N., Tian, Z., Chen, Y., & Sun, B. (2012). Application of a trapezoidal fuzzy AHP method for work safety evaluation and early warning rating of hot and humid environments. Safety Science, 50(2), 228-239. doi:10.1016/j.ssci.2011.08.042Calabrese, A., Costa, R., & Menichini, T. (2013). Using Fuzzy AHP to manage Intellectual Capital assets: An application to the ICT service industry. Expert Systems with Applications, 40(9), 3747-3755. doi:10.1016/j.eswa.2012.12.081Ishizaka, A., & Nguyen, N. H. (2013). Calibrated fuzzy AHP for current bank account selection. Expert Systems with Applications, 40(9), 3775-3783. doi:10.1016/j.eswa.2012.12.089Somsuk, N., & Laosirihongthong, T. (2014). A fuzzy AHP to prioritize enabling factors for strategic management of university business incubators: Resource-based view. Technological Forecasting and Social Change, 85, 198-210. doi:10.1016/j.techfore.2013.08.007Chan, H. K., Wang, X., & Raffoni, A. (2014). An integrated approach for green design: Life-cycle, fuzzy AHP and environmental management accounting. The British Accounting Review, 46(4), 344-360. doi:10.1016/j.bar.2014.10.004Tan, R. R., Aviso, K. B., Huelgas, A. P., & Promentilla, M. A. B. (2014). 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