Hospital Efficiency: An Empirical Analysis of District and Grant-in-Aid Hospitals in Gujarat

This study focuses on analysing the hospital efficiency of district level government hospitals and grant-in-aid hospitals in Gujarat. The study makes an attempt to provide an overview of the general status of the health care services provided by hospitals in the state of Gujarat in terms of their technical and allocative efficiency. One of the two thrusts behind addressing the issue of efficiency was to take stock of the state of healthcare services (in terms of efficiency) provided by grant-in-aid hospitals and district hospitals in Gujarat. The motivation behind addressing the efficiency issue is to provide empirical analysis of governments policy to provide grants to not-for-profit making institutions which in turn provide hospital care in the state. The study addresses the issue whether grant-in-aid hospitals are relatively more efficient than public hospitals. This comparison between grant-in-aid hospitals and district hospitals in terms of their efficiency has been of interest to many researchers in countries other than India, and no consensus has been reached so far as to which category is more efficient. The relative efficiency of government and not-for-profit sector has been reviewed in this paper. It is expected that the findings of the study would be useful to evaluate this policy and help policy makers to develop benchmarks in providing the grants to such institutions.

Food Supply without Risk: Multicriteria Analysis of Institutional Conditions of Exporters

[EN] International trade in food knows no borders, hence the need for prevention systems to avoid the consumption of products that are harmful to health. This paper proposes the use of multicriteria risk prevention tools that consider the socioeconomic and institutional conditions of food exporters. We propose the use of three decision-making methods-Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS), Elimination et Choix Traduisant la Realite (ELECTRE), and Cross-Efficiency (CE)-to establish a ranking of countries that export cereals to the European Union, based on structural criteria related to the detection of potential associated risks (notifications, food quality, corruption, environmental sustainability in agriculture, and logistics). In addition, the analysis examines whether the wealth and institutional capacity of supplier countries influence their position in the ranking. The research was carried out biannually over the period from 2012-2016, allowing an assessment to be made of the possible stability of the markets. The results reveal that suppliers' rankings based exclusively on aspects related to food risk differ from importers' actual choices determined by micro/macroeconomic features (price, production volume, and economic growth). The rankings obtained by the three proposed methods are not the same, but present certain similarities, with the ability to discern countries according to their level of food risk. The proposed methodology can be applied to support sourcing strategies. In the future, food safety considerations could have increased influence in importing decisions, which would involve further difficulties for low-income countries.Ministry of Science and Innovation (Spain) and European Commission-ERDF. Project "Strengthening innovation policy in the agri-food sector" (RTI2018-093791-B-C22).Puertas Medina, RM.; Martí Selva, ML.; García Alvarez-Coque, JM. (2020). Food Supply without Risk: Multicriteria Analysis of Institutional Conditions of Exporters. International Journal of Environmental research and Public Health. 17(10):1-21. https://doi.org/10.3390/ijerph17103432S1211710Walker, E., & Jones, N. (2002). An assessment of the value of documenting food safety in small and less developed catering businesses. Food Control, 13(4-5), 307-314. doi:10.1016/s0956-7135(02)00036-1Sun, Y.-M., & Ockerman, H. W. (2005). A review of the needs and current applications of hazard analysis and critical control point (HACCP) system in foodservice areas. Food Control, 16(4), 325-332. doi:10.1016/j.foodcont.2004.03.012Rohr, J. R., Barrett, C. B., Civitello, D. J., Craft, M. E., Delius, B., DeLeo, G. A., … Tilman, D. (2019). Emerging human infectious diseases and the links to global food production. Nature Sustainability, 2(6), 445-456. doi:10.1038/s41893-019-0293-3De Jonge, J., van Trijp, J. C. M., van der Lans, I. A., Renes, R. J., & Frewer, L. J. (2008). How trust in institutions and organizations builds general consumer confidence in the safety of food: A decomposition of effects. Appetite, 51(2), 311-317. doi:10.1016/j.appet.2008.03.008Neill, C. L., & Holcomb, R. B. (2019). Does a food safety label matter? Consumer heterogeneity and fresh produce risk perceptions under the Food Safety Modernization Act. Food Policy, 85, 7-14. doi:10.1016/j.foodpol.2019.04.001Wood, V. R., & Robertson, K. R. (2000). Evaluating international markets. International Marketing Review, 17(1), 34-55. doi:10.1108/02651330010314704Jouanjean, M.-A., Maur, J.-C., & Shepherd, B. (2015). Reputation matters: Spillover effects for developing countries in the enforcement of US food safety measures. Food Policy, 55, 81-91. doi:10.1016/j.foodpol.2015.06.001Van Ruth, S. M., Huisman, W., & Luning, P. A. (2017). Food fraud vulnerability and its key factors. Trends in Food Science & Technology, 67, 70-75. doi:10.1016/j.tifs.2017.06.017Baylis, K., Nogueira, L., & Pace, K. (2010). Food Import Refusals: Evidence from the European Union. American Journal of Agricultural Economics, 93(2), 566-572. doi:10.1093/ajae/aaq149Bouzembrak, Y., & Marvin, H. J. P. (2016). Prediction of food fraud type using data from Rapid Alert System for Food and Feed (RASFF) and Bayesian network modelling. Food Control, 61, 180-187. doi:10.1016/j.foodcont.2015.09.026Tudela-Marco, L., Garcia-Alvarez-Coque, J. M., & Martí-Selva, L. (2016). Do EU Member States Apply Food Standards Uniformly? A Look at Fruit and Vegetable Safety Notifications. JCMS: Journal of Common Market Studies, 55(2), 387-405. doi:10.1111/jcms.12503Verhaelen, K., Bauer, A., Günther, F., Müller, B., Nist, M., Ülker Celik, B., … Wallner, P. (2018). Anticipation of food safety and fraud issues: ISAR - A new screening tool to monitor food prices and commodity flows. Food Control, 94, 93-101. doi:10.1016/j.foodcont.2018.06.029Garcia‐Alvarez‐Coque, J., Taghouti, I., & Martinez‐Gomez, V. (2020). Changes in Aflatoxin Standards: Implications for EU Border Controls of Nut Imports. Applied Economic Perspectives and Policy, 42(3), 524-541. doi:10.1093/aepp/ppy036Fischer, A. R. H., de Jong, A. E. I., de Jonge, R., Frewer, L. J., & Nauta, M. J. (2005). Improving Food Safety in the Domestic Environment: The Need for a Transdisciplinary Approach. Risk Analysis, 25(3), 503-517. doi:10.1111/j.1539-6924.2005.00618.xHoughton, J. R., Rowe, G., Frewer, L. J., Van Kleef, E., Chryssochoidis, G., Kehagia, O., … Strada, A. (2008). The quality of food risk management in Europe: Perspectives and priorities. Food Policy, 33(1), 13-26. doi:10.1016/j.foodpol.2007.05.001Demortain, D. (2012). Enabling global principle-based regulation: The case of risk analysis in the Codex Alimentarius. Regulation & Governance, 6(2), 207-224. doi:10.1111/j.1748-5991.2012.01144.xFAZIL, A., RAJIC, A., SANCHEZ, J., & MCEWEN, S. (2008). Choices, Choices: The Application of Multi-Criteria Decision Analysis to a Food Safety Decision-Making Problem. Journal of Food Protection, 71(11), 2323-2333. doi:10.4315/0362-028x-71.11.2323Ruzante, J. M., Davidson, V. J., Caswell, J., Fazil, A., Cranfield, J. A. L., Henson, S. J., … Farber, J. M. (2010). A Multifactorial Risk Prioritization Framework for Foodborne Pathogens. Risk Analysis, 30(5), 724-742. doi:10.1111/j.1539-6924.2009.01278.xMazzocchi, M., Ragona, M., & Zanoli, A. (2013). A fuzzy multi-criteria approach for the ex-ante impact assessment of food safety policies. Food Policy, 38, 177-189. doi:10.1016/j.foodpol.2012.11.011Govindan, K., Kadziński, M., & Sivakumar, R. (2017). Application of a novel PROMETHEE-based method for construction of a group compromise ranking to prioritization of green suppliers in food supply chain. Omega, 71, 129-145. doi:10.1016/j.omega.2016.10.004Segura, M., Maroto, C., & Segura, B. (2019). Quantifying the Sustainability of Products and Suppliers in Food Distribution Companies. Sustainability, 11(21), 5875. doi:10.3390/su11215875Lau, H., Nakandala, D., & Shum, P. K. (2018). A business process decision model for fresh-food supplier evaluation. Business Process Management Journal, 24(3), 716-744. doi:10.1108/bpmj-01-2016-0015Garcia-Alvarez-Coque, J.-M., Abdullateef, O., Fenollosa, L., Ribal, J., Sanjuan, N., & Soriano, J. M. (2020). Integrating sustainability into the multi-criteria assessment of urban dietary patterns. Renewable Agriculture and Food Systems, 36(1), 69-76. doi:10.1017/s174217051900053xGrant, W. (2012). Economic patriotism in European agriculture. Journal of European Public Policy, 19(3), 420-434. doi:10.1080/13501763.2011.640797Maye, D., & Kirwan, J. (2013). Food security: A fractured consensus. Journal of Rural Studies, 29, 1-6. doi:10.1016/j.jrurstud.2012.12.001Anthony, R. (2011). Taming the Unruly Side of Ethics: Overcoming Challenges of a Bottom-Up Approach to Ethics in the Areas of Food Policy and Climate Change. Journal of Agricultural and Environmental Ethics, 25(6), 813-841. doi:10.1007/s10806-011-9358-7MacMillan, T., & Dowler, E. (2011). Just and Sustainable? Examining the Rhetoric and Potential Realities of UK Food Security. Journal of Agricultural and Environmental Ethics, 25(2), 181-204. doi:10.1007/s10806-011-9304-8Jaud, M., Cadot, O., & Suwa-Eisenmann, A. (2013). Do food scares explain supplier concentration? An analysis of EU agri-food imports. European Review of Agricultural Economics, 40(5), 873-890. doi:10.1093/erae/jbs038Spink, J., Fortin, N. D., Moyer, D. C., Miao, H., & Wu, Y. (2016). Food Fraud Prevention: Policy, Strategy, and Decision-Making – Implementation Steps for a Government Agency or Industry. CHIMIA International Journal for Chemistry, 70(5), 320-328. doi:10.2533/chimia.2016.320Van Ruth, S. M., Luning, P. A., Silvis, I. C. J., Yang, Y., & Huisman, W. (2018). Differences in fraud vulnerability in various food supply chains and their tiers. Food Control, 84, 375-381. doi:10.1016/j.foodcont.2017.08.020Xidonas, P., & Psarras, J. (2009). Equity portfolio management within the MCDM frame: a literature review. International Journal of Banking, Accounting and Finance, 1(3), 285. doi:10.1504/ijbaaf.2009.022717Melo, M. T., Nickel, S., & Saldanha-da-Gama, F. (2009). Facility location and supply chain management – A review. European Journal of Operational Research, 196(2), 401-412. doi:10.1016/j.ejor.2008.05.007Mandic, K., Delibasic, B., Knezevic, S., & Benkovic, S. (2014). Analysis of the financial parameters of Serbian banks through the application of the fuzzy AHP and TOPSIS methods. Economic Modelling, 43, 30-37. doi:10.1016/j.econmod.2014.07.036Uygun, Ö., Kaçamak, H., & Kahraman, Ü. A. (2015). An integrated DEMATEL and Fuzzy ANP techniques for evaluation and selection of outsourcing provider for a telecommunication company. Computers & Industrial Engineering, 86, 137-146. doi:10.1016/j.cie.2014.09.014Wanke, P., Azad, M. D. A. K., & Barros, C. P. (2016). Predicting efficiency in Malaysian Islamic banks: A two-stage TOPSIS and neural networks approach. Research in International Business and Finance, 36, 485-498. doi:10.1016/j.ribaf.2015.10.002Stojčić, M., Zavadskas, E., Pamučar, D., Stević, Ž., & Mardani, A. (2019). Application of MCDM Methods in Sustainability Engineering: A Literature Review 2008–2018. Symmetry, 11(3), 350. doi:10.3390/sym11030350Xu, L., Shah, S. A. A., Zameer, H., & Solangi, Y. A. (2019). Evaluating renewable energy sources for implementing the hydrogen economy in Pakistan: a two-stage fuzzy MCDM approach. Environmental Science and Pollution Research, 26(32), 33202-33215. doi:10.1007/s11356-019-06431-0Huang, I. B., Keisler, J., & Linkov, I. (2011). Multi-criteria decision analysis in environmental sciences: Ten years of applications and trends. Science of The Total Environment, 409(19), 3578-3594. doi:10.1016/j.scitotenv.2011.06.022Pons, O., de la Fuente, A., & Aguado, A. (2016). The Use of MIVES as a Sustainability Assessment MCDM Method for Architecture and Civil Engineering Applications. Sustainability, 8(5), 460. doi:10.3390/su8050460Shishegaran, A., Shishegaran, A., Mazzulla, G., & Forciniti, C. (2020). A Novel Approach for a Sustainability Evaluation of Developing System Interchange: The Case Study of the Sheikhfazolah-Yadegar Interchange, Tehran, Iran. International Journal of Environmental Research and Public Health, 17(2), 435. doi:10.3390/ijerph17020435Wu, H.-Y., Chen, J.-K., Chen, I.-S., & Zhuo, H.-H. (2012). Ranking universities based on performance evaluation by a hybrid MCDM model. Measurement, 45(5), 856-880. doi:10.1016/j.measurement.2012.02.009Shakouri G., H., & Tavassoli N., Y. (2012). Implementation of a hybrid fuzzy system as a decision support process: A FAHP–FMCDM–FIS composition. Expert Systems with Applications, 39(3), 3682-3691. doi:10.1016/j.eswa.2011.09.063Mavi, R. K., Goh, M., & Mavi, N. K. (2016). Supplier Selection with Shannon Entropy and Fuzzy TOPSIS in the Context of Supply Chain Risk Management. Procedia - Social and Behavioral Sciences, 235, 216-225. doi:10.1016/j.sbspro.2016.11.017Montgomery, B., Dragićević, S., Dujmović, J., & Schmidt, M. (2016). A GIS-based Logic Scoring of Preference method for evaluation of land capability and suitability for agriculture. Computers and Electronics in Agriculture, 124, 340-353. doi:10.1016/j.compag.2016.04.013Debnath, A., Roy, J., Kar, S., Zavadskas, E., & Antucheviciene, J. (2017). A Hybrid MCDM Approach for Strategic Project Portfolio Selection of Agro By-Products. Sustainability, 9(8), 1302. doi:10.3390/su9081302Seyedmohammadi, J., Sarmadian, F., Jafarzadeh, A. A., Ghorbani, M. A., & Shahbazi, F. (2018). Application of SAW, TOPSIS and fuzzy TOPSIS models in cultivation priority planning for maize, rapeseed and soybean crops. Geoderma, 310, 178-190. doi:10.1016/j.geoderma.2017.09.012Rostamzadeh, R., Ghorabaee, M. K., Govindan, K., Esmaeili, A., & Nobar, H. B. K. (2018). Evaluation of sustainable supply chain risk management using an integrated fuzzy TOPSIS- CRITIC approach. Journal of Cleaner Production, 175, 651-669. doi:10.1016/j.jclepro.2017.12.071Raut, R. D., Gardas, B. B., Kharat, M., & Narkhede, B. (2018). Modeling the drivers of post-harvest losses – MCDM approach. Computers and Electronics in Agriculture, 154, 426-433. doi:10.1016/j.compag.2018.09.035Qureshi, M. R. N., Singh, R. K., & Hasan, M. A. (2017). Decision support model to select crop pattern for sustainable agricultural practices using fuzzy MCDM. Environment, Development and Sustainability, 20(2), 641-659. doi:10.1007/s10668-016-9903-7Srinivasa Rao, C., Kareemulla, K., Krishnan, P., Murthy, G. R. K., Ramesh, P., Ananthan, P. S., & Joshi, P. K. (2019). Agro-ecosystem based sustainability indicators for climate resilient agriculture in India: A conceptual framework. Ecological Indicators, 105, 621-633. doi:10.1016/j.ecolind.2018.06.038Paul, M., Negahban-Azar, M., Shirmohammadi, A., & Montas, H. (2020). Assessment of agricultural land suitability for irrigation with reclaimed water using geospatial multi-criteria decision analysis. Agricultural Water Management, 231, 105987. doi:10.1016/j.agwat.2019.105987Balezentis, T., Chen, X., Galnaityte, A., & Namiotko, V. (2020). Optimizing crop mix with respect to economic and environmental constraints: An integrated MCDM approach. Science of The Total Environment, 705, 135896. doi:10.1016/j.scitotenv.2019.135896Jahan, A., & Edwards, K. L. (2013). VIKOR method for material selection problems with interval numbers and target-based criteria. Materials & Design, 47, 759-765. doi:10.1016/j.matdes.2012.12.072Pourhejazy, P., Kwon, O., Chang, Y.-T., & Park, H. (2017). Evaluating Resiliency of Supply Chain Network: A Data Envelopment Analysis Approach. Sustainability, 9(2), 255. doi:10.3390/su9020255Stewart, T. J. (1996). Relationships between Data Envelopment Analysis and Multicriteria Decision Analysis. Journal of the Operational Research Society, 47(5), 654-665. doi:10.1057/jors.1996.77Li, X.-B., & Reeves, G. R. (1999). A multiple criteria approach to data envelopment analysis. European Journal of Operational Research, 115(3), 507-517. doi:10.1016/s0377-2217(98)00130-1Zavadskas, 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. doi:10.3846/20294913.2014.892037Mousavi-Nasab, S. H., & Sotoudeh-Anvari, A. (2017). A comprehensive MCDM-based approach using TOPSIS, COPRAS and DEA as an auxiliary tool for material selection problems. Materials & Design, 121, 237-253. doi:10.1016/j.matdes.2017.02.041Bouyssou, D. (1999). Using DEA as a tool for MCDM: some remarks. Journal of the Operational Research Society, 50(9), 974-978. doi:10.1057/palgrave.jors.2600800Özcan, T., Çelebi, N., & Esnaf, Ş. (2011). Comparative analysis of multi-criteria decision making methodologies and implementation of a warehouse location selection problem. Expert Systems with Applications, 38(8), 9773-9779. doi:10.1016/j.eswa.2011.02.022LOKEN, E. (2007). Use of multicriteria decision analysis methods for energy planning problems. Renewable and Sustainable Energy Reviews, 11(7), 1584-1595. doi:10.1016/j.rser.2005.11.005Darji, V. P., & Rao, R. V. (2014). Intelligent Multi Criteria Decision Making Methods for Material Selection in Sugar Industry. Procedia Materials Science, 5, 2585-2594. doi:10.1016/j.mspro.2014.07.519Ceballos, B., Lamata, M. T., & Pelta, D. A. (2016). A comparative analysis of multi-criteria decision-making methods. Progress in Artificial Intelligence, 5(4), 315-322. doi:10.1007/s13748-016-0093-1Sen, B., Bhattacharjee, P., & Mandal, U. K. (2016). A comparative study of some prominent multi criteria decision making methods for connecting rod material selection. Perspectives in Science, 8, 547-549. doi:10.1016/j.pisc.2016.06.016Wu, D. (2006). A note on DEA efficiency assessment using ideal point: An improvement of Wang and Luo’s model. Applied Mathematics and Computation, 183(2), 819-830. doi:10.1016/j.amc.2006.06.030Kou, G., Peng, Y., & Wang, G. (2014). Evaluation of clustering algorithms for financial risk analysis using MCDM methods. Information Sciences, 275, 1-12. doi:10.1016/j.ins.2014.02.137Roy, B. (1991). The outranking approach and the foundations of electre methods. Theory and Decision, 31(1), 49-73. doi:10.1007/bf00134132YOON, K., & HWANG, C.-L. (1985). Manufacturing plant location analysis by multiple attribute decision making: part I—single-plant strategy. International Journal of Production Research, 23(2), 345-359. doi:10.1080/00207548508904712Doyle, J., & Green, R. (1994). Efficiency and Cross-efficiency in DEA: Derivations, Meanings and Uses. Journal of the Operational Research Society, 45(5), 567-578. doi:10.1057/jors.1994.84Martí, L., Martín, J. C., & Puertas, R. (2017). A Dea-Logistics Performance Index. Journal of Applied Economics, 20(1), 169-192. doi:10.1016/s1514-0326(17)30008-9Canadá y la UE: Si Quierohttps://www.Euroganadería.euKARABIYIK, C., & KUTLU KARABIYIK, B. (2018). Benchmarking International Trade Performance of OECD Countries: TOPSIS and AHP Approaches. Gaziantep University Journal of Social Sciences. doi:10.21547/jss.267381Lin, M.-C., Wang, C.-C., Chen, M.-S., & Chang, C. A. (2008). Using AHP and TOPSIS approaches in customer-driven product design process. Computers in Industry, 59(1), 17-31. doi:10.1016/j.compind.2007.05.013Lourenzutti, R., & Krohling, R. A. (2016). A generalized TOPSIS method for group decision making with heterogeneous information in a dynamic environment. Information Sciences, 330, 1-18. doi:10.1016/j.ins.2015.10.005Roy, B. (1968). Classement et choix en présence de points de vue multiples. Revue française d’informatique et de recherche opérationnelle, 2(8), 57-75. doi:10.1051/ro/196802v100571Jaini, N., & Utyuzhnikov, S. (2016). Trade-off ranking method for multi-criteria decision analysis. Journal of Multi-Criteria Decision Analysis, 24(3-4), e1600. doi:10.1002/mcda.1600Farrell, M. J. (1957). The Measurement of Productive Efficiency. Journal of the Royal Statistical Society. Series A (General), 120(3), 253. doi:10.2307/2343100Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444. doi:10.1016/0377-2217(78)90138-8Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science, 30(9), 1078-1092. doi:10.1287/mnsc.30.9.1078Angulo-Meza, L., & Lins, M. P. E. (2002). Annals of Operations Research, 116(1/4), 225-242. doi:10.1023/a:1021340616758Falagario, M., Sciancalepore, F., Costantino, N., & Pietroforte, R. (2012). Using a DEA-cross efficiency approach in public procurement tenders. European Journal of Operational Research, 218(2), 523-529. doi:10.1016/j.ejor.2011.10.031Puertas, R., & Marti, L. (2019). Sustainability in Universities: DEA-GreenMetric. Sustainability, 11(14), 3766. doi:10.3390/su1114376

A state-of-art survey on TQM applications using MCDM techniques

In today’s competitive economy, quality plays an essential role for the success business units and there are considerable efforts made to control and to improve quality characteristics in order to satisfy customers’ requirements. However, improving quality is normally involved with various criteria and we need to use Multi Criteria Decision Making (MCDM) to handle such cases. In this state-of the-art literature survey, 45 articles focused on solving quality problems by MCDM methods are investigated. These articles were published between 1994 and 2013.Seven areas were selected for categorization: (1) AHP, Fuzzy AHP, ANP and Fuzzy ANP, (2) DEMATEL and Fuzzy DEMATEL, (3) GRA, (4) Vikor and Fuzzy Vikor, (5) TOPSIS, Fuzzy TOPSIS and combination of TOPSIS and AHP, (6) Fuzzy and (7) Less frequent and hybrid procedures. According to our survey, Fuzzy based methods were the most popular technique with about 40% usage among procedures. Also AHP and ANP were almost 20% of functional methods. This survey ends with giving recommendation for future researches

Defuzzification of groups of fuzzy numbers using data envelopment analysis

Defuzzification is a critical process in the implementation of fuzzy systems that converts fuzzy numbers to crisp representations. Few researchers have focused on cases where the crisp outputs must satisfy a set of relationships dictated in the original crisp data. This phenomenon indicates that these crisp outputs are mathematically dependent on one another. Furthermore, these fuzzy numbers may exist as a group of fuzzy numbers. Therefore, the primary aim of this thesis is to develop a method to defuzzify groups of fuzzy numbers based on Charnes, Cooper, and Rhodes (CCR)-Data Envelopment Analysis (DEA) model by modifying the Center of Gravity (COG) method as the objective function. The constraints represent the relationships and some additional restrictions on the allowable crisp outputs with their dependency property. This leads to the creation of crisp values with preserved relationships and/or properties as in the original crisp data. Comparing with Linear Programming (LP) based model, the proposed CCR-DEA model is more efficient, and also able to defuzzify non-linear fuzzy numbers with accurate solutions. Moreover, the crisp outputs obtained by the proposed method are the nearest points to the fuzzy numbers in case of crisp independent outputs, and best nearest points to the fuzzy numbers in case of dependent crisp outputs. As a conclusion, the proposed CCR-DEA defuzzification method can create either dependent crisp outputs with preserved relationship or independent crisp outputs without any relationship. Besides, the proposed method is a general method to defuzzify groups or individuals fuzzy numbers under the assumption of convexity with linear and non-linear membership functions or relationships

An integrated performance measurement framework for restaurant chains: A case study in Istanbul

Companies that continue to operate in a competitive market strive the most efficient use of their resources in order to remain competitive. Nowadays, with increasing customer feedback, properly analyzing customer needs and requests and producing services in accordance with expectations have become increasingly important due to the large number of companies competing in the same market, and this is especially important to be at the forefront of competitors in the food services industry. There are risks and uncertainties owing to the continuously changing demand for food service enterprises, the difficulty to regulate interest and comparable charges, the competitive environment, and currency rate hikes. In light of all of these circumstances, restaurants require a versatile tool to effectively measure and analyze their performance. Therefore, this study combines Principal Component Analysis (PCA) and Categorical Data Envelopment Analysis (CAT-DEA) to analyze the performance of 15 dealers in Istanbul, divided into three categories: steakhouse, kebab, and meatball-doner. The results demonstrate that each category has just one efficient restaurant, for a total of three efficient restaurants out of fifteen. In addition to the suggested CAT-DEA-based framework, three research hypotheses are constructed and analyzed to investigate the link between restaurant performance and various environmental factors (or relevant indicators) in the food service industry.Rekabetçi bir piyasada faaliyet göstermeye devam eden şirketler, rekabetçi kalabilmek için kaynaklarını en verimli şekilde kullanmaya çalışırlar. Artan müşteri geri bildirimleri ile birlikte, aynı pazarda rekabet eden çok sayıda firma nedeniyle, müşteri ihtiyaç ve isteklerini doğru analiz etmek ve beklentilere uygun hizmet üretmek giderek daha önemli hale geldi ve bu durum özellikle gıda hizmetleri endüstrisinde rekabette ön planda olmak için önemlidir. Yiyecek hizmeti işletmelerine yönelik sürekli değişen talep, faiz ve karşılaştırılabilir ücretlerin düzenlenmesindeki zorluk, rekabet ortamı ve kur artışları nedeniyle bu sektörde riskler ve belirsizlikler bulunmaktadır. Tüm bu koşullar ışığında restoranlar, performanslarını etkin bir şekilde ölçmek ve analiz etmek için çok yönlü bir araca ihtiyaç duyarlar. Bu nedenle, bu çalışma, İstanbul'da et lokantası, kebap ve köfte-döner olmak üzere üç kategoriye ayrılmış 15 bayinin performansını analiz etmek için Temel Bileşenler Analizi (PCA) ve Kategorik Veri Zarflama Analizini (CAT-DEA) birleştirmektedir. Sonuçlar, her bir kategorinin yalnızca bir verimli restorana sahip olduğunu ve on beş bayiden toplamda üç bayinin verimli olduğunu göstermektedir. Önerilen CAT-DEA tabanlı yaklaşıma ek olarak, yemek hizmeti endüstrisinde restoran performansı ile çeşitli çevresel faktörler (veya ilgili göstergeler) arasındaki bağlantıyı araştırmak için üç araştırma hipotezi oluşturulmuş ve analiz edilmiştir

A hybrid unsupervised learning and multi-criteria decision making approach for performance evaluation of Indian banks

Efficient and stable performance of the banking system underpins sustainable growth of any economy. Of late, several economic reforms have been initiated in India for facilitating growth and withstanding dynamics of global economy. In this context, the current study compares the performance of the selected private and public sector banks in India on a five year time horizon in order to discern any changes in the performance over a period of time. First, the performance of the selected banks are examined in terms of management efficiency perspective using a Multi-Criteria Decision Making (MCDM) technique such as Combinative Distance-based Assessment (CODAS) when an Entropy method is also employed for determining criteria weight. The study also applies k-Means Clustering for checking consistency of performance based ranking with asset management efficiency. Finally, the paper delves into the relationship between financial and market performance. The study has found consistent results and observed private sector banks perform better than the public sector

Selection of biogas, solar, and wind power plants’ locations: An MCDA approach

This study discusses a multi-criteria approach to locating biogas, solar and wind power plants that significantly addresses the challenge of global warming caused by power generation. Because the utility of locations to build renewable energy power plants depends on economic, social and environmental dimensions, after reviewing literature, the sustainable frameworks of criteria affecting the location of biogas, solar and wind power plants were examined in this paper. The offered frameworks are applied to determining the site of biogas, solar, and wind power plants in Iran. The provinces of Iran are assessed as alternatives in this paper. To compute the weight of criteria in the offered framework, data from a sample of experts in Iran are used via an online survey form designed based on the best-worst method (BWM). Using the results of the BWM and the performance data, the overall score are calculated for the various provinces of Iran. The results of this study indicate that energy saving, effect on resources and natural reserves and wind flow, respectively, are the most effective factors for determining the place of biogas, solar and wind power plants, and South Khorasan, Khuzestan, and Khuzestan show the best result for establishing biogas, solar, and wind power plants in Iran respectively

Evaluating the criteria for financial holding company operating ability based on the DEMATEL approach–the case of Taiwan

Evaluating criteria selection has significant impacts on data envelopment analysis (DEA) efficiency estimates. Selecting the proper evaluation criteria lead to successful and meaningful results of decision-making. This study aims to use the Decision Making Trial and Evaluation Laboratory (DEMATEL) method to evaluate the most important constructs and criteria and also establish causality relationships among others for financial holding companies (FHCs) of banks’ operating ability in Taiwan. In this research 15 criteria were confirmed through reviewing various articles associated with this issue. Then, the information from the questionnaire was turned into the DEMATEL questionnaire and was distributed among nine experts and also members of the FHCs of Taiwan. The research results show that employees, total assets, total liabilities, non-interest income, income on investments, net profits before tax, net worth, and EPS are eight causal criteria. Furthermore, operating expenses, capital, interest expenses, interest income, operating income, return on assets (ROA), and return on equity (ROE) are seven effect criteria

A combined machine learning algorithms and DEA method for measuring and predicting the efficiency of Chinese manufacturing listed companies

Data Envelopment Analysis (DEA) is a linear programming methodology for measuring the efficiency of Decision Making Units (DMUs) to improve organizational performance in the private and public sectors. However, if a new DMU needs to be known its efficiency score, the DEA analysis would have to be re-conducted, especially nowadays, datasets from many fields have been growing rapidly in the real world, which will need a huge amount of computation. Following the previous studies, this paper aims to establish a linkage between the DEA method and machine learning (ML) algorithms, and proposes an alternative way that combines DEA with ML (ML-DEA) algorithms to measure and predict the DEA efficiency of DMUs. Four ML-DEA algorithms are discussed, namely DEA-CCR model combined with back-propagation neural network (BPNN-DEA), with genetic algorithm (GA) integrated with back-propagation neural network (GANN-DEA), with support vector machines (SVM-DEA), and with improved support vector machines (ISVM-DEA), respectively. To illustrate the applicability of above models, the performance of Chinese manufacturing listed companies in 2016 is measured, predicted and compared with the DEA efficiency scores obtained by the DEA-CCR model. The empirical results show that the average accuracy of the predicted efficiency of DMUs is about 94%, and the comprehensive performance order of four ML-DEA algorithms ranked from good to poor is GANN-DEA, BPNN-DEA, ISVM-DEA, and SVM-DEA