Share:


A fuzzy decision-making approach for evaluation and selection of third party reverse logistics provider using fuzzy ARAS

Abstract

Business environment is full of ups and down and this makes companies to develop different ways of using resources. By expanding life cycle of products, these ways can be cost effective and not harmful for environment. As Reverse Logistics (RL) uses a product after end of its life, it reduces pollution, therefore it has been considered as a part of sustainable development. The core goal of current research is developing a framework by which it evaluates Third Party RL Provider (3rdPRLP) using Multi-Criteria Decision-Making (MCDM) based on Fuzzy Additive Ratio ASsessment (FARAS). Thirty-seven criteria were identified, which are classified into seven main criteria. The main criteria were ranked as follows: product lifecycle position C1, RL process function C2, organizational performance C3, organizational role of RL C4, IT system and communication C5, general company consideration C6, geographical location C7. Market coverage, destination, financial considerations, integrated system, reclaim, efficiency and quality, and growth are each group’s dominant sub-criteria. In addition, the current research helps the logistics managers to better understand the key attributes’ complex relationships in the environment of decision-making.


First published online 21 January 2021

Keyword : reverse logistics, sustainable development, 3rdPRLPs, MCDM, fuzzy sets, FARAS

How to Cite
Rostamzadeh, R., Esmaeili, A., Sivilevičius, H., & Nobard, H. B. K. (2020). A fuzzy decision-making approach for evaluation and selection of third party reverse logistics provider using fuzzy ARAS. Transport, 35(6), 635-657. https://doi.org/10.3846/transport.2020.14226
Published in Issue
Dec 31, 2020
Abstract Views
1095
PDF Downloads
831
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Akhavan, P.; Barak, S.; Maghsoudlou, H.; Antuchevičienė, J. 2015. FQSPM-SWOT for strategic alliance planning and partner selection; case study in a holding car manufacturer company, Technological and Economic Development of Economy 21(2): 165–185. http://doi.org/10.3846/20294913.2014.965240

Abdulrahman, M. D.; Gunasekaran, A.; Subramanian, N. 2014. Critical barriers in implementing reverse logistics in the Chinese manufacturing sectors, International Journal of Production Economics 147: 460–471. http://doi.org/10.1016/j.ijpe.2012.08.003

Agrawal, S.; Singh, R. K.; Murtaza, Q. 2016a. Disposition decisions in reverse logistics: Graph theory and matrix approach, Journal of Cleaner Production 137: 93–104. http://doi.org/10.1016/j.jclepro.2016.07.045

Agrawal, S.; Singh, R. K.; Murtaza, Q. 2016b. Outsourcing decisions in reverse logistics: Sustainable balanced scorecard and graph theoretic approach, Resources, Conservation and Recycling 108: 41–53.
http://doi.org/10.1016/j.resconrec.2016.01.004

Aguezzoul, A. 2007. The third party logistics selection: a review of literature, in International Logistics and Supply Chain Congress 2007, 8–9 November 2007, Istanbul, Turkey, 1–7.

Aguezzoul, A. 2014. Third-party logistics selection problem: a literature review on criteria and methods, Omega 49: 69–78. http://doi.org/10.1016/j.omega.2014.05.009

Amini, M. M.; Retzlaff-Roberts, D.; Bienstock, C. C. 2005. Designing a reverse logistics operation for short cycle time repair services, International Journal of Production Economics 96(3): 367–380. http://doi.org/10.1016/j.ijpe.2004.05.010

Azadi, M.; Saen, R. F. 2011. A new chance-constrained data envelopment analysis for selecting third-party reverse logistics providers in the existence of dual-role factors, Expert Systems with Applications 38(10): 12231–12236. http://doi.org/10.1016/j.eswa.2011.04.001

Bai, C.; Sarkis, J. 2013. Flexibility in reverse logistics: a framework and evaluation approach, Journal of Cleaner Production 47: 306–318. http://doi.org/10.1016/j.jclepro.2013.01.005

Baležentis, A., Baležentis, T., Misiūnas, A. 2012. An integrated assessment of Lithuanian economic sectors based on financial ratios and fuzzy MCDM methods, Technological and Economic Development of Economy 18(1): 34–53. https://doi.org/10.3846/20294913.2012.656151

Bazan, E.; Jaber, M. Y.; Zanoni, S. 2016. A review of mathematical inventory models for reverse logistics and the future of its modeling: an environmental perspective, Applied Mathematical Modelling 40(5–6): 4151–4178. http://doi.org/10.1016/j.apm.2015.11.027

Bei, W.; Linyan, S. 2005. A review of reverse logistics, Applied Sciences (APPS) 7: 16–29.

Bellman, R. E.; Zadeh, L. A. 1970. Decision-making in a fuzzy environment, Management Science 17(4): B-141–B-164. https://doi.org/10.1287/mnsc.17.4.B141

Blumberg, D. F. 1999. Strategic examination of reverse logistics & repair service requirements, needs, market size, and opportunities, Journal of Business Logistics 20(2): 141–160.

Bottani, E.; Rizzi, A. 2006. A fuzzy TOPSIS methodology to support outsourcing of logistics services, Supply Chain Management 11(4): 294–308. http://doi.org/10.1108/13598540610671743

Bouzon, M.; Govindan, K.; Rodriguez, C. M. T. 2015. Reducing the extraction of minerals: reverse logistics in the machinery manufacturing industry sector in Brazil using ISM approach, Resources Policy 46: 27–36. http://doi.org/10.1016/j.resourpol.2015.02.001

Bouzon, M.; Govindan, K.; Rodriguez, C. M. T.; Campos, L. M. S. 2016. Identification and analysis of reverse logistics barriers using fuzzy Delphi method and AHP, Resources, Conservation and Recycling 108: 182–197. http://doi.org/10.1016/j.resconrec.2015.05.021

Buyukozkan, G.; Feyzioǧlu, O.; Nebol, E. 2008. Selection of the strategic alliance partner in logistics value chain, International Journal of Production Economics 113(1): 148–158. http://doi.org/10.1016/j.ijpe.2007.01.016

Buyukozkan, G.; Gocer, F. 2018. An extension of ARAS methodology under interval valued intuitionistic fuzzy environment for digital supply chain, Applied Soft Computing 69: 634–654. http://doi.org/10.1016/j.asoc.2018.04.040

Carter, C. R.; Ellram, L. M. 1998. Reverse logistics: a review of the literature and framework for future investigation, Journal of Business Logistics 19(1): 85–102.

Cheng, J.-H.; Chen, S.-S.; Chuang, Y.-W. 2008. An application of fuzzy Delphi and fuzzy AHP for multi-criteria evaluation model of fourth party logistics, WSEAS Transactions on Systems 7(5): 466–478. Available from Internet: http://www.wseas.us/e-library/transactions/systems/2008/26-120.pdf

Cheng, Y.-H.; Lee, F. 2010. Outsourcing reverse logistics of high-tech manufacturing firms by using a systematic decision-making approach: TFT-LCD sector in Taiwan, Industrial Marketing Management 39(7): 1111–1119. http://doi.org/10.1016/j.indmarman.2009.10.004

Dadelo, S.; Turskis, Z.; Zavadskas, E. K.; Dadelienė, R. 2012. Multiple criteria assessment of elite security personal on the basis of ARAS and expert methods, Economic Computation and Economic Cybernetics Studies and Research 46(4): 65–87.

Dahooie, J. H.; Zavadskas, E. K.; Abolhasani, M.; Vanaki, A.; Turskis, Z. 2018. A novel approach for evaluation of projects using an interval–valued fuzzy additive ratio assessment (ARAS) method: a case study of oil and gas well drilling projects, Symmetry 10(2): 45. http://doi.org/10.3390/sym10020045

De Almeida, A. T. 2007. Multicriteria decision model for outsourcing contracts selection based on utility function and ELECTRE method, Computers & Operations Research 34(12): 3569–3574. http://doi.org/10.1016/j.cor.2006.01.003

Ecer, F. 2018. An integrated аuzzy AHP and ARAS model to evaluate mobile banking services, Technological and Economic Development of Economy 24(2): 670–695. http://doi.org/10.3846/20294913.2016.1255275

Efendigil, T.; Onut, S.; Kongar, E. 2008. A holistic approach for selecting a third-party reverse logistics provider in the presence of vagueness, Computers & Industrial Engineering 54(2): 269–287. http://doi.org/10.1016/j.cie.2007.07.009

Falsini, D.; Fondi, F.; Schiraldi, M. M. 2012. A logistics provider evaluation and selection methodology based on AHP, DEA and linear programming integration, International Journal of Production Research 50(17): 4822–4829. http://doi.org/10.1080/00207543.2012.657969

Fernandez, I.; Kekale, T. 2008. Better models with Delphi and analytic hierarchy process approaches: the case of reverse logistics, International Journal of Logistics Systems and Management 4(3): 282–296. http://doi.org/10.1504/IJLSM.2008.017477

Fisk, G.; Chandran, R. 1975. How to trace and recall products, Harvard Business Review 53(6): 90–96.

Fleischmann, M.; Bloemhof-Ruwaard, J. M.; Dekker, R.; Van Der Laan, E.; Van Nunen, J. A. E. E.; Van Wassenhove, L. N. 1997. Quantitative models for reverse logistics: a review, European Journal of Operational Research 103(1): 1–17. http://doi.org/10.1016/S0377-2217(97)00230-0

Ghadikolaei, A. S.; Esbouei, S. K.; Antuchevičienė, J. 2014. Applying fuzzy MCDM for financial performance evaluation of Iranian companies, Technological and Economic Development of Economy 20(2): 274–291. http://doi.org/10.3846/20294913.2014.913274

Giuntini, R.; Andel, T. 1995a. Advance with reverse logistics, Transportation and Distribution 36(2): 73–77.

Giuntini, R.; Andel, T. 1995b. Master the six R’s of reverse logistics, Transportation and Distribution 36(3): 93–98.

Giuntini, R.; Andel, T. 1995c. Reverse logistics role models, Transportation and Distribution 36(4): 97–98.

Govindan, K.; Paam, P.; Abtahi, A.-R. 2016. A fuzzy multi-objective optimization model for sustainable reverse logistics network design, Ecological Indicators 67: 753–768. http://doi.org/10.1016/j.ecolind.2016.03.017

Govindan, K.; Popiuc, M. N. 2014. Reverse supply chain coordination by revenue sharing contract: A case for the personal computers industry, European Journal of Operational Research 233(2): 326–336. http://doi.org/10.1016/j.ejor.2013.03.023

Govindan, K.; Soleimani, H. 2017. A review of reverse logistics and closed-loop supply chains: a Journal of Cleaner Production focus, Journal of Cleaner Production 142: 371–384. http://doi.org/10.1016/j.jclepro.2016.03.126

Gol, H.; Catay, B. 2007. Third‐party logistics provider selection: insights from a Turkish automotive company, Supply Chain Management 12(6): 379–384. http://doi.org/10.1108/13598540710826290

Guarnieri, P.; Sobreiro, V. A.; Nagano, M. S.; Marques Serrano, A. L. 2015. The challenge of selecting and evaluating third-party reverse logistics providers in a multicriteria perspective: a Brazilian case, Journal of Cleaner Production 96: 209–219. http://doi.org/10.1016/j.jclepro.2014.05.040

Guide, V. D. R.; Harrison, T. P.; Van Wassenhove, L. N. 2003. The challenge of closed-loop supply chains, Interfaces 33(6): 3–6. http://doi.org/10.1287/inte.33.6.3.25182

Haji Vahabzadeh, A.; Asiaei, A.; Zailani, S. 2015. Reprint of “Green decision-making model in reverse logistics using FUZZY–VIKOR method”, Resources, Conservation and Recycling 104: 334–347. http://doi.org/10.1016/j.resconrec.2015.10.028

Jayaraman, V.; Patterson, R. A.; Rolland, E. 2003. The design of reverse distribution networks: Models and solution procedures, European Journal of Operational Research 150(1): 128–149. http://doi.org/10.1016/S0377-2217(02)00497-6

Kannan, G. 2009. Fuzzy approach for the selection of third party reverse logistics provider, Asia Pacific Journal of Marketing and Logistics 21(3): 397–416. http://doi.org/10.1108/13555850910973865

Kannan, G.; Pokharel, S.; Kumar, P. S. 2009. A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider, Resources, Conservation and Recycling 54(1): 28–36. http://doi.org/10.1016/j.resconrec.2009.06.004

Kaufmann, A.; Gupta, M. M. 1991. Introduction to Fuzzy Arithmetic: Theory and Applications. Van Nostrand Reinhold. 361 p.

Keršulienė, V.; Turskis, Z. 2014. A hybrid linguistic fuzzy multiple criteria group selection of a chief accounting officer, Journal of Business Economics and Management 15(2): 232–252. https://doi.org/10.3846/16111699.2014.903201

Keršulienė, V.; Turskis, Z. 2011. Integrated fuzzy multiple criteria decision making model for architect selection, Technological and Economic Development of Economy 17(4): 645–666.

Khodaverdi, R.; Hashemi, S. H. 2015. A grey-based decisionmaking approach for selecting a reverse logistics provider in a closed loop supply chain, International Journal of Management and Decision Making 14(1): 32–43. https://doi.org/10.1504/IJMDM.2015.067376

Kleindorfer, P. R.; Partovi, F. Y. 1990. Integrating manufacturing strategy and technology choice, European Journal of Operational Research 47(2): 214–224. http://doi.org/10.1016/0377-2217(90)90280-O

Kutut, V.; Zavadskas, E. K.; Lazauskas, M. 2013. Assessment of priority options for preservation of historic city centre buildings using MCDM (ARAS), Procedia Engineering 57: 657–661. https://doi.org/10.1016/j.proeng.2013.04.083

Liu, H.-C.; Liu, L.; Liu, N.; Mao, L.-X. 2012. Risk evaluation in failure mode and effects analysis with extended VIKOR method under fuzzy environment, Expert Systems with Applications 39(17): 12926–12934. http://doi.org/10.1016/j.eswa.2012.05.031

Meade, L.; Sarkis, J. 2002. A conceptual model for selecting and evaluating third‐party reverse logistics providers, Supply Chain Management 7(5): 283–295. http://doi.org/10.1108/13598540210447728

Menon, M. K.; McGinnis, M. A.; Ackerman, K. B. 1998. Selection criteria for providers of third-party logistics services: an exploratory study, Journal of Business Logistics 19(1): 121–137.

Min, H.; Ko, H.-J. 2008. The dynamic design of a reverse logistics network from the perspective of third-party logistics service providers, International Journal of Production Economics 113(1): 176–192. http://doi.org/10.1016/j.ijpe.2007.01.017

Murphy, P. 1986. A preliminary study of transportation and warehousing aspects of reverse distribution, Transportation Journal 25(4): 12–21.

Nguyen, H.-T.; Dawal, S. Z.; Nukman, Y.; Rifai, A. P.; Aoyama, H. 2016. An integrated MCDM model for conveyor equipment evaluation and selection in an FMC based on a fuzzy AHP and fuzzy ARAS in the presence of vagueness, PLoS ONE 11(4): e0153222. http://doi.org/10.1371/journal.pone.0153222

Pati, R. K.; Vrat, P.; Kumar, P. 2008. A goal programming model for paper recycling system, Omega 36(3): 405–417. http://doi.org/10.1016/j.omega.2006.04.014

Percin, S.; Min, H. 2013. A hybrid quality function deployment and fuzzy decision-making methodology for the optimal selection of third-party logistics service providers, International Journal of Logistics Research and Applications: a Leading Journal of Supply Chain Management 16(5): 380–397. http://doi.org/10.1080/13675567.2013.815696

Prakash, C.; Barua, M. K. 2016a. A combined MCDM approach for evaluation and selection of third-party reverse logistics partner for Indian electronics industry, Sustainable Production and Consumption 7: 66–78. http://doi.org/10.1016/j.spc.2016.04.001

Prakash, C.; Barua, M. K. 2016b. An analysis of integrated robust hybrid model for third-party reverse logistics partner selection under fuzzy environment, Resources, Conservation and Recycling 108: 63–81. http://doi.org/10.1016/j.resconrec.2015.12.011

Rajesh, R.; Pugazhendhi, S.; Ganesh, K. 2009. An analytic model for selection of and allocation among third party logistics service providers, International Journal of Enterprise Network Management 3(3): 268–288. http://doi.org/10.1504/IJENM.2009.032398

Ravi, V.; Shankar, R.; Tiwari, M. K. 2005. Analyzing alternatives in reverse logistics for end-of-life computers: ANP and balanced scorecard approach, Computers & Industrial Engineering 48(2): 327–356. http://doi.org/10.1016/j.cie.2005.01.017

Rogers, D. S.; Tibben-Lembke, R. 2001. An examination of reverse logistics practices, Journal of Business Logistics 22(2): 129–148. http://doi.org/10.1002/j.2158-1592.2001.tb00007.x

Rostamzadeh, R.; Esmaeili, A.; Shahriyari Nia, A.; Šaparauskas, J.; Keshavarz Ghorabaee, M. 2017. A fuzzy ARAS method for supply chain management performance measurement in SMEs under uncertainty, Transformations in Business & Economics 16(2A): 319–348.

Rostamzadeh, R.; Ismail, K.; Khajeh Noubar, H. B. 2014a. An application of a hybrid MCDM method for the evaluation of entrepreneurial intensity among the SMEs: a case study, The Scientific World Journal 2014: 703650. http://doi.org/10.1155/2014/703650

Rostamzadeh, R.; Ismail, K.; Zavadskas, E. K. 2014b. Multi criteria decision making for assisting business angels in investments, Technological and Economic Development of Economy 20(4): 696–720. http://doi.org/10.3846/20294913.2014.984364

Sarkis, J. 1998. Evaluating environmentally conscious business practices, European Journal of Operational Research 107(1): 159–174. http://doi.org/10.1016/S0377-2217(97)00160-4

Senthil, S.; Murugananthan, K.; Ramesh, A. 2018. Analysis and prioritisation of risks in a reverse logistics network using hybrid multi-criteria decision making methods, Journal of Cleaner Production 179: 716–730. http://doi.org/10.1016/j.jclepro.2017.12.095

Senthil, S.; Srirangacharyulu, B.; Ramesh, A. 2014. A robust hybrid multi-criteria decision making methodology for contractor evaluation and selection in third-party reverse logistics, Expert Systems with Applications 41(1): 50–58. http://doi.org/10.1016/j.eswa.2013.07.010

Shariati, S.; Yazdani-Chamzini, A.; Salsani, A.; Tamošaitienė, J. 2014. Proposing a new model for waste dump site selection: case study of Ayerma phosphate mine, Inžinerinė Ekonomika – Engineering Economics 25(4): 410–419. https://doi.org/10.5755/j01.ee.25.4.6262

Shemshadi, A.; Shirazi, H.; Toreihi, M.; Tarokh, M. J. 2011. A fuzzy VIKOR method for supplier selection based on entropy measure for objective weighting, Expert Systems with Applications 38(10): 12160–12167. http://doi.org/10.1016/j.eswa.2011.03.027

Sink, H. L.; Langley, C. J. 1997. A managerial framework for the acquisition of third-party logistics services, Journal of Business Logistics 18(2): 163–189.

Sink, H. L.; Langley, C. J.; Gibson, B. J. 1996. Buyer observations of the US third‐party logistics market, International Journal of Physical Distribution & Logistics Management 26(3): 38–46. https://doi.org/10.1108/09600039610115009

So, S.-H.; Kim, J.; Cheong, K.; Cho, G. 2006. Evaluating the service quality of third-party logistics service providers using the analytic hierarchy process, Journal of Information Systems and Technology Management 3(3): 261–270. http://doi.org/10.4301/S1807-17752006000300001

Stanujkic, D. 2015. Extension of the ARAS method for decision-making problems with interval-valued triangular fuzzy numbers, Informatica 26(2): 335–355. https://doi.org/10.15388/Informatica.2015.51

Stock, J.; Speh, T.; Shear, H. 2002. Many happy (product) returns, Harvard Business Review 7. Available from Internet: https://hbr.org/2002/07/many-happy-product-returns

Subramoniam, R.; Huisingh, D.; Chinnam, R. B.; Subramoniam, S. 2013. Remanufacturing decision-making framework (RDMF): research validation using the analytical hierarchical process, Journal of Cleaner Production 40: 212–220. http://doi.org/10.1016/j.jclepro.2011.09.004

Tate, K. 1996. The elements of a successful logistics partnership, International Journal of Physical Distribution & Logistics Management 26(3): 7–13. https://doi.org/10.1108/09600039610115045

Tavana, 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. http://doi.org/10.1016/j.asoc.2015.12.005

Trochu, J.; Chaabane, A.; Ouhimmou, M. 2018. Reverse logistics network redesign under uncertainty for wood waste in the CRD industry, Resources, Conservation and Recycling: 32–47. http://doi.org/10.1016/j.resconrec.2017.09.011

Tupėnaitė, L.; Zavadskas, E. K.; Kaklauskas, A.; Turskis, Z.; Seniut, M. 2010. Multiple criteria assessment of alternatives for built and human environment renovation, Journal of Civil Engineering and Management 16(2): 257–266. http://doi.org/10.3846/jcem.2010.30

Turskis, Z.; Lazauskas, M.; Zavadskas, E. K. 2012. Fuzzy multiple criteria assessment of construction site alternatives for non hazardous waste incineration plant in Vilnius city, applying ARAS-F and AHP methods, Journal of Environmental Engineering and Landscape Management 20(2): 110–120. https://doi.org/10.3846/16486897.2011.645827

Turskis, Z.; Zavadskas, E. K. 2010. A new fuzzy additive ratio assessment method (ARAS‐F). Case study: the analysis of fuzzy multiple criteria in order to select the logistic centers location, Transport 25(4): 423–432. https://doi.org/10.3846/transport.2010.52

Yang, M. G.; Hong, P.; Modi, S. B. 2011. Impact of lean manufacturing and environmental management on business performance: an empirical study of manufacturing firms, International Journal of Production Economics 129(2): 251–261. http://doi.org/10.1016/j.ijpe.2010.10.017

Zadeh, L. A. 1965. Fuzzy sets, Information and Control 8(3): 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X

Zamani, M.; Rabbani, A.; Yazdani-Chamzini, A.; Turskis, Z. 2014. An integrated model for extending brand based on fuzzy ARAS and ANP methods, Journal of Business Economics and Management 15(3): 403–423. http://doi.org/10.3846/16111699.2014.923929

Zavadskas, E. K., Turskis. Z. 2010. A new additive ratio assessment (ARAS) method in multicriteria decision‐making, Technological and Economic Development of Economy 16(2): 159–172. https://doi.org/10.3846/tede.2010.10

Zavadskas, E. K.; Turskis, Z.; Vilutienė, T.; Lepkova, N. 2017. Integrated group fuzzy multi-criteria model: case of facilities management strategy selection, Expert Systems with Applications 82: 317–331. https://doi.org/10.1016/j.eswa.2017.03.072

Zhang, Y.; Feng, Y. 2007. A selection approach of reverse logistics provider based on fuzzy AHP, in Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007), 24–27 August 2007, Haikou, China, 479–482. http://doi.org/10.1109/FSKD.2007.119

Zimmermann, H.-J. 1987. Fuzzy Sets, Decision Making, and Expert Systems. Springer Science + Business Media, LLC. 336 p. https://doi.org/10.1007/978-94-009-3249-4

Zimmermann, H.-J. 1991. Fuzzy Set Theory and Its Applications. Springer Science + Business Media, LLC. 399 p. https://doi.org/10.1007/978-94-015-7949-0