Sustainable supplier selection using integrated data envelopment analysis and differential evolution model

Document Type: Research Paper


Department of Industrial Engineering and Management, Khulna University of Engineering and Technology, Khulna-9203, Bangladesh.


Nowadays, increasing environmental and social awareness has led numerous industries to adopt Sustainable Supply Chain Management (SSCM). Sustainable Supplier Selection (SSS) is considered as a very important and primary step of achieving an SSCM. SSS is a Multi-Criteria Decision Making (MCDM) problem and is very intricate for its nature. This study aims to evaluate and rank sustainable suppliers using Data Envelopment Analysis (DEA) which is a popular model for measuring the productive efficiency of decision-making units effectively and is also able to handle MCDM problems. To avoid some inherent limitations of DEA, an evolutionary algorithm Differential Evolution (DE) is used to solve the DEA model. This integrated DEA-DE model provides more accurate efficiencies and is verified through a case study in a pharmaceutical company. Employing this easy and fast model to assess sustainable suppliers will help industries and suppliers to move forward towards achieving and maintaining sustainability and thus will increase the overall performance of SSCM.


Main Subjects

[1]  Kannan, V. R., & Tan, K. C. (2002). Supplier selection and assessment: Their impact on business performance. Journal of supply chain management38(3), 11-21.

[2]  Garfamy, R. M. (2011). Supplier selection and business process improvement: an exploratory multiple case study. International journal of operational research10(2), 240-255.

[3]  Zimmer, K., Fröhling, M., & Schultmann, F. (2016). Sustainable supplier management–a review of models supporting sustainable supplier selection, monitoring and development. International journal of production research54(5), 1412-1442.

[4]  Konys, A. (2019). Green supplier selection criteria: from a literature review to a comprehensive knowledge base. Sustainability11(15), 4208.

[5]  Dickson, G. W. (1966). An analysis of vendor selection systems and decisions. Journal of purchasing2(1), 5-17.

[6]  De Boer, L., Labro, E., & Morlacchi, P. (2001). A review of methods supporting supplier selection. European journal of purchasing & supply management7(2), 75-89.

[7]  Ha, S. H., & Krishnan, R. (2008). A hybrid approach to supplier selection for the maintenance of a competitive supply chain. Expert systems with applications34(2), 1303-1311.

[8]  Ho, W., Xu, X., & Dey, P. K. (2010). Multi-criteria decision making approaches for supplier evaluation and selection: A literature review. European journal of operational research202(1), 16-24.

[9]  Jayant, A. (2018). An analytical hierarchy process (AHP) based approach for supplier selection: An automotive industry case study. Int. J. Bus. Insights transform. (IJBIT)11, 36-45.

[10]    Marufuzzaman, M., Ahsan, K. B., & Xing, K. (2009). Supplier selection and evaluation method using analytical hierarchy process (AHP): a case study on an apparel manufacturing organisation. International journal of value chain management3(2), 224-240.

[11]    Giannakis, M., Dubey, R., Vlachos, I., & Ju, Y. (2020). Supplier sustainability performance evaluation using the analytic network process. Journal of cleaner production247, 119439.

[12]    Stojić, G., Stević, Ž., Antuchevičienė, J., Pamučar, D., & Vasiljević, M. (2018). A novel rough WASPAS approach for supplier selection in a company manufacturing PVC carpentry products. Information9(5), 121.

[13]    Pereira, T., Dias, E., & Fontes, D. B. (2019). A MCDA Model for olive oil supplier selection using MACBETH. International journal for quality research13(4).

[14]    Karande, P., & Chakraborty, S. (2015). Supplier selection using weighted utility additive method. Journal of the institution of engineers (India): Series C96(4), 397-406.

[15]    Arani, A. S., Nozari, H., & Jafari-Eskandari, M. (2017). Performance evaluation of suppliers with undesirable outputs using DEA. In data envelopment analysis and effective performance assessment (pp. 312-327). IGI Global.

[16] Sutrisno, S., Widowati, W., & Sunarsih, S. (2019, May). Genetic algorithm approach for large scale quadratic programming of probabilistic supplier selection and inventory management problem. International conference on science and science education.

[17]    Agrawal, N., & Kant, S. (2020). Supplier selection using Fuzzy-AHP: A case study. In Trends in manufacturing processes (pp. 119-127). Singapore: Springer.

[18]    Jain, V., Sangaiah, A. K., Sakhuja, S., Thoduka, N., & Aggarwal, R. (2018). Supplier selection using fuzzy AHP and TOPSIS: a case study in the Indian automotive industry. Neural computing and applications29(7), 555-564.

[19]    Sennaroglu, B., & Akıcı, O. (2019, July). Supplier selection using fuzzy analytic network process. International conference on intelligent and fuzzy systems (pp. 829-834). Cham: Springer.

[20]    Moheb-Alizadeh, H., & Handfield, R. (2019). Sustainable supplier selection and order allocation: A novel multi-objective programming model with a hybrid solution approach. Computers & industrial engineering129, 192-209.

[21]    Jadidi, O., Cavalieri, S., & Zolfaghari, S. (2015). An improved multi-choice goal programming approach for supplier selection problems. Applied mathematical modelling39(14), 4213-4222.

[22]    Kang, B., Hu, Y., Deng, Y., & Zhou, D. (2016). A new methodology of multicriteria decision-making in supplier selection based on-numbers. Mathematical problems in engineering2016.

[23]    Wang, C. N., Nguyen, V. T., Chyou, J. T., Lin, T. F., & Nguyen, T. N. (2019). Fuzzy multicriteria decision-Making model (MCDM) for raw materials supplier selection in plastics industry. Mathematics7(10), 981.

[24] Yu, Q., & Hou, F. (2016). An approach for green supplier selection in the automobile manufacturing industry. Kybernetes, 45(4), 571-588.

[25]    Bottani, E., Centobelli, P., Murino, T., & Shekarian, E. (2018). A QFD-ANP method for supplier selection with benefits, opportunities, costs and risks considerations. International journal of information technology & decision making17(03), 911-939.

[26]    Memari, A., Dargi, A., Jokar, M. R. A., Ahmad, R., & Rahim, A. R. A. (2019). Sustainable supplier selection: A multi-criteria intuitionistic fuzzy TOPSIS method. Journal of manufacturing systems50, 9-24.

[27]    Chen, Y., Wang, S., Yao, J., Li, Y., & Yang, S. (2018). Socially responsible supplier selection and sustainable supply chain development: A combined approach of total interpretive structural modeling and fuzzy analytic network process. Business strategy and the environment27(8), 1708-1719.

[28]    Quan, J., Zeng, B., & Liu, D. (2018). Green supplier selection for process industries using weighted grey incidence decision model. Complexity.

[29]    Shi, P., Yan, B., Shi, S., & Ke, C. (2015). A decision support system to select suppliers for a sustainable supply chain based on a systematic DEA approach. Information technology and management16(1), 39-49.

[30]    Ebrahimzadeh Shermeh, H., Alavidoost, M. H., & Darvishinia, R. (2018). Evaluating the efficiency of power companies using data envelopment analysis based on SBM models: a case study in power industry of Iran. Journal of applied research on industrial engineering5(4), 286-295.

[31]    Kohl, S., Schoenfelder, J., Fügener, A., & Brunner, J. O. (2019). The use of Data Envelopment Analysis (DEA) in healthcare with a focus on hospitals. Health care management science22(2), 245-286.

[32]    Edalatpanah, S. A. (2018). Neutrosophic perspective on DEA. Journal of applied research on industrial engineering5(4), 339-345.

[33]    Tavassoli, M., Saen, R. F., & Zanjirani, D. M. (2020). Assessing sustainability of suppliers: A novel stochastic-fuzzy DEA model. Sustainable production and consumption21, 78-91.

[34]    Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization11(4), 341-359.

[35]    Mogha, S. K., & Yadav, S. P. (2016). Application of differential evolution for data envelopment analysis. International journal of data science1(3), 247-258.

[36]    Jafarzadeh Ghoushchi, S., Dodkanloi Milan, M., & Jahangoshai Rezaee, M. (2017). Evaluation and selection of sustainable suppliers in supply chain using new GP-DEA model with imprecise data. Journal of industrial engineering international, 14(3), 613-625.

[37]    Kumar, A., Jain, V., Kumar, S., & Chandra, C. (2016). Green supplier selection: a new genetic/immune strategy with industrial application. Enterprise information systems10(8), 911-943.

[38]    Khoshfetrat, S., & Hosseinzadeh Lotfi, F. (2014). Introducing a nonlinear programming model and using genetic algorithm to rank the alternatives in analytic hierarchy process. Journal of applied research on industrial engineering1(1), 12-18.

[39]    Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision-making units. European journal of operational research, 2(6), 429-444.

[40]    Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management science30(9), 1078-1092.