1. Home
  2. Issues
  3. Volume 29, Issue 1 (2018)
  4. Selection of Waste Lubricant Oil Regener ...

Informatica


Selection of Waste Lubricant Oil Regenerative Technology Using Entropy-Weighted Risk-Based Fuzzy Axiomatic Design Approach
Volume 29, Issue 1 (2018), pp. 41–74
Pub. online: 1 January 2018      Type: Research Article      Open accessOpen Access
Received
1 August 2017
Accepted
1 January 2018
Published
1 January 2018

Abstract

The selection of waste lubricant oil regenerative technology regarding the complexity of the technologies and financial issues is a complex problem. Some risk factors exist regarding the ratings of technologies on the effective criteria. The current study tackles the selection of the technology based on fuzzy axiomatic design approach considering risk factors. Shannon entropy significance coefficients are computed for criteria. The problem is first solved by considering all criteria and then supplementary solutions are presented by categorizing the criteria to technical and economic groups. Two types of risk factors are identified for the technologies, i.e. general and specific risk factors.

References

 
Abdulkareem, A.S., Afolabi, A.S., Ahanonu, S.O., Mokrani, T. (2014). Effect of treatment methods on used lubricating oil for recycling purposes. Energy Sources, Part A: Recovery. Utilization, and Environmental Effects, 36(9), 966–973.
 
Ahmed, J.S., Kasim, M.M., Langroudi, M.Z.A.E. (2014). An application of a defuzzification method for a group of dependent fuzzy numbers in solving a ranking problem. In: AIP Conference Proceedings, pp. 41–44.
 
Al-Zahrani, S.M., Putra, M.D. (2013). Used lubricating oil regeneration by various solvent extraction techniques. Journal of Industrial and Engineering Chemistry, 19(2), 536–539.
 
Alemi-Ardakani, M., Milani, A.S., Yannacopoulos, S., Shokouhi, G. (2016). On the effect of subjective, objective and combinative weighting in multiple criteria decision making: a case study on impact optimization of composites. Expert Systems with Applications, 46, 426–438.
 
Allahviranloo, T., Saneifard, R. (2012). Defuzzification method for ranking fuzzy numbers based on center of gravity. Iranian Journal of Fuzzy Systems, 9(6), 57–67.
 
American Petroleum Institute (2015). Annex E-API base oil interchangeaility guidelines for passenger car motor oils and diesel engine oils. API Handbooks, 1–13.
 
Behzadian, M., Kazemzadeh, R.B., Albadvi, A., Aghdasi, M. (2010). PROMETHEE: a comprehensive literature review on methodologies and applications. European Journal of Operational Research, 200(1), 198–215.
 
Büyüközkan, G., Göçer, F. (2017). Application of a new combined intuitionistic fuzzy MCDM approach based on axiomatic design methodology for the supplier selection problem. Applied Soft Computing, 52, 1222–1238.
 
Cabrerizo, F.J., Herrera-Viedma, E., Pedrycz, W. (2013). A method based on PSO and granular computing of linguistic information to solve group decision making problems defined in heterogeneous contexts. European Journal of Operational Research, 230(3), 624–633.
 
Cabrerizo, F.J., Ureña, R., Pedrycz, W., Herrera-Viedma, E. (2014). Building consensus in group decision making with an allocation of information granularity. Fuzzy Sets and Systems, 255, 115–127.
 
Çakır, S. (2016). An integrated approach to machine selection problem using fuzzy SMART-fuzzy weighted axiomatic design. Journal of Intelligent Manufacturing.
 
Capuano, N., Chiclana, F., Fujita, H., Herrera-Viedma, E., Loia, V. (2017). Fuzzy group decision making with incomplete information guided by social influence. IEEE Transactions on Fuzzy Systems.
 
Cavallaro, F., Zavadskas, E., Raslanas, S. (2016). Evaluation of combined heat and power (CHP) systems using fuzzy shannon entropy and fuzzy TOPSIS. Sustainability, 8(6), 556.
 
Chakraborty, K., Mondal, S., Mukherjee, K. (2017). Analysis of product design characteristics for remanufacturing using fuzzy AHP and axiomatic design. Journal of Engineering Design, 28(5), 338–368.
 
Chari, K.R. et al. (2012). Compendium of Recycling and Destruction Tecnologies for Waste Oils. United Nations Environment Programme, USA.
 
Chen, X., Li, Z., Fan, Z.P., Zhou, X., Zhang, X. (2016). Matching demanders and suppliers in knowledge service: a method based on fuzzy axiomatic design. Information Sciences, 346–347, 130–145.
 
Cheng, J., Feng, Y., Lin, Z., Liu, Z., Tan, J. (2017). Anti-vibration optimization of the key components in a turbo-generator based on heterogeneous axiomatic design. Journal of Cleaner Production, 141, 1467–1477.
 
Chung, J., Kim, J., Kim, Y., Hwang, Y. (2013). Assessment and selection of best available technology (BAT) for wastewater facilities in the leather tanning and finishing industry. Resources, Conservation and Recycling, 70, 32–37.
 
Cid-López, A., Hornos, M.J., Carrasco, R.A., Herrera-Viedma, E. (2016). Applying a linguistic multi-criteria decision-making model to the analysis of ICT suppliers’ offers. Expert Systems with Applications, 57, 127–138.
 
Daim, T.U., Intarode, N. (2009). A framework for technology assessment: case of a Thai building material manufacturer. Energy for Sustainable Development, 13(4), 280–286.
 
Dalla, G., Khlebinskaia, O., Lodolo, A., Miertus, S. (2003). Compendium of Used Oil Regeneration Technologies. United Nations Industrial Development Organization, 10–15.
 
Duijm, N.J. (2002). Hazard analysis of technologies for disposing explosive waste. Journal of Hazardous Materials, 90(2), 123–135.
 
Fengqiang, L., Jiadong, Q., Yi, L. (2008). Computer-assisted stereotactic neurosurgery with framework neurosurgery navigation. Clinical Neurology and Neurosurgery, 110(7), 696–700.
 
Georgiadis, D.R., Mazzuchi, T.A., Sarkani, S. (2013). Using multi criteria decision making in analysis of alternatives for selection of enabling technology. Systems Engineering, 16(3), 287–303.
 
Georgopoulou, E., Mirasgedis, S., Sarafidis, Y., Hontou, V., Gakis, N., Lalas, D.P., Loukatos, A., Gargoulas, N., Mentzis, A., Economidis, D., Triantafilopoulos, T., Korizi, K., Mavrotas, G. (2007). A decision-aid framework to provide guidance for the enhanced use of best available techniques in industry. Environmental Management, 40(3), 413–429.
 
Gomes, C.F.S., Nunes, K.R.A., Helena Xavier, L., Cardoso, R., Valle, R. (2008). Multicriteria decision making applied to waste recycling in Brazil. Omega, 36(3), 395–404.
 
Gören, H.G., Kulak, O. (2014). A new fuzzy multi-criteria decision making approach: extended hierarchical fuzzy axiomatic design approach with risk factors. Lecture Notes in Business Information Processing, 184, 22–33.
 
Grimes, S.M., Thompson, F. (2016). Recovery of lubricant base oils using ionic liquid processes. Proceedings of the Institution of Civil Engineers – Waste and Resource Management, 169(2), 73–82.
 
Guo, Z., Liu, H., Zhang, D., Yang, J. (2017). Green supplier evaluation and selection in apparel manufacturing using a fuzzy multi-criteria decision-making approach. Sustainability, 9(4), 650.
 
Hafezalkotob, A., Hafezalkotob, A. (2015). Comprehensive MULTIMOORA method with target-based attributes and integrated significant coefficients for materials selection in biomedical applications. Materials & Design, 87, 949–959.
 
Hafezalkotob, A., Hafezalkotob, A. (2016a). Risk-based material selection process supported on information theory: a case study on industrial gas turbine. Applied Soft Computing.
 
Hafezalkotob, A., Hafezalkotob, A. (2016b). Extended MULTIMOORA method based on Shannon entropy weight for materials selection. Journal of Industrial Engineering International, 12(1), 1–13.
 
Hafezalkotob, A., Hafezalkotob, A. (2016c). Fuzzy entropy-weighted MULTIMOORA method for materials selection. Journal of Intelligent & Fuzzy Systems, 31(3), 1211–1226.
 
Hafezalkotob, A., Hafezalkotob, A. (2017). Interval target-based VIKOR method supported on interval distance and preference degree for machine selection. Engineering Applications of Artificial Intelligence, 57, 184–196.
 
Hatami-Marbini, A., Tavana, M., Moradi, M., Kangi, F. (2013). A fuzzy group Electre method for safety and health assessment in hazardous waste recycling facilities. Safety Science, 51(1), 414–426.
 
Hou, Y., Qin, J., Li, X., Liu, Y. (2016). Decision-making analysis of scheme selection under different preferences. Procedia CIRP, 53, 225–230.
 
Hsu, Y.-L., Liu, C.-C. (2011). Evaluation and selection of regeneration of waste lubricating oil technology. Environmental Monitoring and Assessment, 176(1–4), 197–212.
 
Hsu, Y.-L., Lee, C.-H., Kreng, V.B. (2010). The application of Fuzzy Delphi Method and Fuzzy AHP in lubricant regenerative technology selection. Expert Systems with Applications, 37(1), 419–425.
 
Hwang, C.L., Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications. Springer-Verlag, New York.
 
Ibáñez-Forés, V., Bovea, M.D., Azapagic, A. (2013). Assessing the sustainability of Best Available Techniques (BAT): methodology and application in the ceramic tiles industry. Journal of Cleaner Production, 51, 162–176.
 
Ibáñez-Forés, V., Bovea, M.D., Pérez-Belis, V. (2014). A holistic review of applied methodologies for assessing and selecting the optimal technological alternative from a sustainability perspective. Journal of Cleaner Production, 70, 259–281.
 
Jafari, A.J., Hassanpour, M. (2015). Analysis and comparison of used lubricants, regenerative technologies in the world. Resources, Conservation and Recycling, 103, 179–191.
 
Jahan, A., Edwards, K.L. (2015). A state-of-the-art survey on the influence of normalization techniques in ranking: improving the materials selection process in engineering design. Materials and Design, 65, 335–342.
 
Kahraman, C., Çebi, S. (2009). A new multi-attribute decision making method: Hierarchical fuzzy axiomatic design. Expert Systems with Applications, 36(3 Part 1), 4848–4861.
 
Kanokkantapong, V., Kiatkittipong, W., Panyapinyopol, B., Wongsuchoto, P., Pavasant, P. (2009). Used lubricating oil management options based on life cycle thinking. Resources, Conservation and Recycling, 53(5), 294–299.
 
Kartam, N.A., Kartam, S.A. (2001). Risk and its management in the Kuwaiti construction industry: a contractors’ perspective. International Journal of Project Management, 19(6), 325–335.
 
Khandekar, A.V., Chakraborty, S. (2015). Selection of material handling equipment using fuzzy axiomatic design principles. Informatica, 26(2), 259–282.
 
Khandekar, A.V., Antuchevičienė, J., Chakraborty, S. (2015). Small hydro-power plant project selection using fuzzy axiomatic design principles. Technological and Economic Development of Economy, 21(5), 756–772.
 
Khelifi, O., Giovanna, F.D., Vranes, S., Lodolo, A., Miertus, S. (2006). Decision support tool for used oil regeneration technologies assessment and selection. Journal of Hazardous Materials, 137(1), 437–442.
 
Kir, S., Yazgan, H.R. (2016). A sequence dependent single machine scheduling problem with fuzzy axiomatic design for the penalty costs. Computers and Industrial Engineering, 92, 95–104.
 
Kulak, O., Kahraman, C. (2005a). Fuzzy multi-attribute selection among transportation companies using axiomatic design and analytic hierarchy process. Information Sciences, 170(2–4), 191–210.
 
Kulak, O., Kahraman, C. (2005b). Multi-attribute comparison of advanced manufacturing systems using fuzzy vs. crisp axiomatic design approach. International Journal of Production Economics, 95(3), 415–424.
 
Kulak, O., Durmuşoǧlu, M.B., Kahraman, C. (2005). Fuzzy multi-attribute equipment selection based on information axiom. Journal of Materials Processing Technology, 169(3), 337–345.
 
Kulak, O., Cebi, S., Kahraman, C. (2010). Applications of axiomatic design principles: a literature review. Expert Systems with Applications, 37(9), 6705–6717.
 
Kulak, O., Goren, H.G., Supciller, A.A. (2015). A new multi criteria decision making approach for medical imaging systems considering risk factors. Applied Soft Computing, 35, 931–941.
 
Kupareva, A., Mäki-Arvela, P., Murzin, D.Y. (2013). Technology for rerefining used lube oils applied in Europe: A review. Journal of Chemical Technology and Biotechnology, 88(10), 1780–1793.
 
Li, C.-C., Dong, Y., Herrera, F., Herrera-Viedma, E., Martínez, L. (2017). Personalized individual semantics in computing with words for supporting linguistic group decision making. An application on consensus reaching. Information Fusion, 33(1), 29–40.
 
Liao, H., Xu, Z., Herrera-Viedma, E., Herrera, F. (2017). Hesitant fuzzy linguistic term set and its application in decision making: a state-of-the-art survey. International Journal of Fuzzy Systems. Accepted.
 
Lin, C.-L., Shih, Y.-H., Tzeng, G.-H., Yu, H.-C. (2016). A service selection model for digital music service platforms using a hybrid MCDM approach. Applied Soft Computing, 48, 385–403.
 
Liu, X., Wen, Z. (2012). Best available techniques and pollution control: a case study on China’s thermal power industry. Journal of Cleaner Production, 23(1), 113–121.
 
Liu, W., Dong, Y., Chiclana, F., Cabrerizo, F.J., Herrera-Viedma, E. (2017). Group decision-making based on heterogeneous preference relations with self-confidence. Fuzzy Optimization and Decision Making, 16(4), 429–447.
 
Mohammed, R.R., Ibrahim, I.A.R., Taha, A.H., McKay, G. (2013). Waste lubricating oil treatment by extraction and adsorption. Chemical Engineering Journal, 220, 343–351.
 
Molinos-Senante, M., Gómez, T., Caballero, R., Hernández-Sancho, F., Sala-Garrido, R. (2015). Assessment of wastewater treatment alternatives for small communities: an analytic network process approach. Science of The Total Environment, 532, 676–687.
 
Morente-Molinera, J.A., Kou, G., González-Crespo, R., Corchado, J.M., Herrera-Viedma, E. (2017a). Solving multi-criteria group decision making problems under environments with a high number of alternatives using fuzzy ontologies and multi-granular linguistic modelling methods. Knowledge-Based Systems, 137, 54–64.
 
Morente-Molinera, J.A., Mezei, J., Carlsson, C., Herrera-Viedma, E. (2017b). Improving supervised learning classification methods using multigranular linguistic modeling and fuzzy entropy. IEEE Transactions on Fuzzy Systems, 25(5), 1078–1089.
 
Okmen, O., Oztas, A. (2008). Construction project network evaluation with correlated schedule risk analysis model. Journal of Construction Engineering and Management, 134(1), 49–63.
 
Parnell, G.S., Bresnick, T.A., Tani, S.N., Johnson, E.R. (2013). Handbook of Decision Analysis. John Wiley & Sons, Inc., Hoboken, NJ, USA.
 
Pohekar, S.D., Ramachandran, M. (2004). Application of multi-criteria decision making to sustainable energy planning – a review. Renewable and Sustainable Energy Reviews, 8(4), 365–381.
 
Rastogi, M., Chauhan, A., Vaish, R., Kishan, A. (2015). Selection and performance assessment of phase change materials for heating, ventilation and air-conditioning applications. Energy Conversion and Management, 89, 260–269.
 
Rauch, E., Matt, D.T., Dallasega, P. (2016). Application of axiomatic design in manufacturing system design: a literature review. Procedia CIRP, 53, 1–7.
 
Ravindran, A.R., Bilsel, R.U., Wadhwa, V., Yang, T. (2010). Risk adjusted multicriteria supplier selection models with applications. International Journal of Production Research, 48(2), 405–424.
 
Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49–57.
 
Rincón, J., Cañizares, P., García, M.T. (2007). Regeneration of used lubricant oil by ethane extraction. Journal of Supercritical Fluids, 39(3), 315–322.
 
Shahdany, M.H., Roozbahani, A. (2016). Selecting an appropriate operational method for main irrigation canals within multicriteria decision-making methods. Journal of Irrigation and Drainage Engineering, 142(4), 4015064.
 
Shannon, C.E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(July 1928), 379–423.
 
Sharma, S.K., Swain, N. (2015). Risk management in construction projects. Technological and Economic Development of Economy, 21(1), 65–78.
 
Shehabuddeen, N., Probert, D., Phaal, R. (2006). From theory to practice: challenges in operationalising a technology selection framework. Technovation, 26(3), 324–335.
 
Subramanyan, H., Sawant, P.H., Bhatt, V. (2012). Construction project risk assessment: development of model based on investigation of opinion of construction project experts from India. Journal of construction engineering and management, 138.
 
Suh, N.P. (1990). The Principles of Design. Oxford University Press, New York.
 
Suh, N.P. (1998). Axiomatic design theory for systems. Research in Engineering Design, 10(4), 189–209.
 
Suh, N.P. (2001). Axiomatic Design: Advances and Applications. Oxford University Press, New York.
 
Suh, N.P., Cochran, D.S., Paulo, C.L. (1998). Manufacturing system design. CIRP Annals, 47(2), 627–639.
 
Taylan, O., Kaya, D., Demirbas, A. (2016). An integrated multi attribute decision model for energy efficiency processes in petrochemical industry applying fuzzy set theory. Energy Conversion and Management, 117, 501–512.
 
Trinkūnienė, E., Podvezko, V., Zavadskas, E.K., Jokšenė, I., Vinogradova, I., Trinkūnas, V. (2017). Evaluation of quality assurance in contractor contracts by multi-attribute decision-making methods. Economic Research-Ekonomska Istraživanja, 30(1), 1152–1180.
 
Vranes, S., Giovanna, F.D., Khelifi, O., Lodolo, A., Miertus, S. (1999). Software support for waste oil management.
 
Wen, Z., Yu, Y., Yan, J. (2015). Best available techniques assessment for coal gasification to promote cleaner production based on the ELECTRE-II method. Journal of Cleaner Production, 129, 12–22.
 
Wood, D.A. (2016). Supplier selection for development of petroleum industry facilities, applying multi-criteria decision making techniques including fuzzy and intuitionistic fuzzy TOPSIS with flexible entropy weighting. Journal of Natural Gas Science and Engineering, 28, 594–612.
 
Zadeh, L.A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.
 
Zavadskas, E.K., Podvezko, V. (2016). Integrated determination of objective criteria weights in MCDM. International Journal of Information Technology and Decision Making, 15(2), 267–283.
 
Zhang, H., Gu, C.L., Gu, L.W., Zhang, Y. (2011). The evaluation of tourism destination competitiveness by TOPSIS and information entropy – a case in the Yangtze River Delta of China. Tourism Management, 32(2), 443–451.
 
Zheng, P., Wang, Y., Xu, X., Xie, S.Q. (2017). A weighted rough set based fuzzy axiomatic design approach for the selection of AM processes. The International Journal of Advanced Manufacturing Technology, 91(5–8), 1977–1990.

Biographies

Ijadi Maghsoodi Abteen
Aimaghsoodi@srbiau.ac.ir

A. Ijadi Maghsoodi is a MSc student in industrial engineering at the Islamic Azad University, Science and Research Branch, Tehran, Iran. He has earned a BSc degree in industrial engineering from Islamic Azad University, Qazvin Branch, Qazvin, Iran. He has industrial experience in lubricating oils industry and regenerative waste products at Monad Oil Company in UAE. His research interests are focused on MCDM, intelligent decision support systems, statistical learning, and data-mining.

Hafezalkotob Arian
ar_hafezalkotob@azad.ac.ir

Ar. Hafezalkotob is a lecturer at Islamic Azad University, South Tehran Branch, Iran from 2014–2017. He received a MSc degree in mechanical engineering (applied design), in 2010, and a BSc degree in mechanical engineering (solids design), in 2007, both from Islamic Azad University, Tehran, Iran. He is also a member of young researchers and elite club, South Tehran Branch, Islamic Azad University, Tehran, Iran. He has authored several research papers published in highly-ranked journals including Elsevier’s ASOC, APM, EAAI, and JMAD as well as IOS-Press’s JIFS. His research interests include the fields of MCDM under risk and uncertainty, fuzzy and interval sets, design optimization, material selection, machine selection, composites, biomedical prostheses, buckling, finite element method, and artificial neural networks.

Azizi Ari Iman
Imanazizi65@gmail.com

I. Azizi-Ari received his BSc in industrial engineering from the Islamic Azad University, South Tehran Branch, Tehran, Iran. Currently, he is a MSc student in industrial engineering at the Islamic Azad University, Science and Research Branch, Tehran, Iran. He has a background in quality management systems, supervising and leading projects in the field of quality assurance and control in companies such as Shatel and National Iranian Gas Company. His current research interests are human resource developments, quality assurance, quality control, and MCDM.

Ijadi Maghsoodi Sasan
Sasansim@yahoo.co.uk

S. Ijadi Maghsoodi is the general-manager and CEO of the Monad Oil F.Z.C in UAE and PAK oil company in Kazakhstan. He earned BSc and MSc degrees in civil and construction engineering from the Swansea University, Wales, UK. He has long experience in managing development projects regarding recycling technologies.

Hafezalkotob Ashkan
a_hafez@azad.ac.ir

As. Hafezalkotob is currently an associate professor at the Industrial Engineering College of South Tehran Branch of Islamic Azad University. He received his BSc degree in industrial engineering in 2004, MSc degree in industrial engineering in 2007, and PhD degree in industrial engineering in 2012 from Iran University of Science and Technology, Tehran, Iran. He has authored papers published in highly-ranked journals including TRE, IJPE, JCLP, ASOC, EAAI, CAIE, APM, AMC, JMAD, IJFS, MPE, JMSY, as well as some other journals and conferences proceedings. His research interests include supply chain management, decision-making techniques, game theory, and mathematical modelling.


Full article Related articles Cited by PDF XML
Full article Related articles Cited by PDF XML

Copyright
© 2018 Vilnius University
Open access article under the CC BY license.

Keywords
waste lubricant oil regenerative technology MCDM FAD Shannon entropy risk factors

Metrics
since January 2020
1703

Article info
views

885

Full article
views

860

PDF
downloads

260

XML
downloads


Share


RSS