Supplier Selection Models for Complementary, Substitutable, and Conditional Products

Document Type : Research Paper

Authors

1 Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

2 Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

3 Institute for Management and Planning Studies, Tehran, Iran

Abstract

The supplier selection process, as one of the components of the supply chain management (SCM), refers to evaluating and selecting suitable suppliers based on relevant criteria. This study presents two supplier selection models to supply complementary, substitutable, and conditional products. For this purpose, two multi-objective mixed-integer non-linear programming (MOMINLP) models are formulated to select the suppliers with the highest scores, the lowest total cost, and the highest quality. To identify the criteria weights and to score the suppliers, first, one of the effective multiple criteria decision-making (MCDM) methods, called the Best-Worst Method (BWM), is employed. Then, the weighted relative deviations from the ideal values of the criteria are minimized to solve the multi-objective models. Finally, two case studies are represented to show the practical application of the proposed methodology in the decision-making process.

Keywords

References

Amorim, P., Curcio, E., Almada-Lobo, B., Barbosa-Póvoa, A.P. and Grossmann, I.E. (2016). Supplier selection in the processed food industry under uncertainty. European Journal of Operational Research, Vol. 252(3), pp. 801-814.
Azadnia, A.H. (2016). A multi-objective mathematical model for sustainable supplier selection and order lot-sizing under inflation. International Journal of Engineering-Transactions B: Applications, Vol. 29(8), pp.  1141.
Bohner, C. and Minner, S. (2017). Supplier selection under failure risk, quantity, and business volume discounts. Computers & Industrial Engineering, Vol. 104, pp. 145-155.
Chai, J. and Ngai, E.W. (2020). Decision-making techniques in supplier selection: Recent accomplishments and what lies ahead. Expert Systems with Applications, Vol. 140, pp. 112903.
Dahel, N.E. (2003). Vendor selection and order quantity allocation in volume discount environments. Supply Chain Management: An International Journal, Vol. 8(4), pp. 335-342.
Davoudabadi, R., Mousavi, S.M. and Sharifi, E. (2020). An integrated weighting and ranking model based on entropy, DEA, and PCA considering two aggregation approaches for resilient supplier selection problem. Journal of Computational Science, Vol. 40, pp. 101074.
El-Hiri, M., En-Nadi, A. and Chafi, A. (2019). Suppliers Selection in Consideration of Risks by a Neural Network. International Journal of Engineering, Vol. 32(10), pp.1454-1463.
Fakhrzad, M. B. and Lotfi, R. (2018). Green vendor managed inventory with backorder in two echelon supply chain with epsilon-constraint and NSGA-II approach. Journal of Industrial Engineering Research in Production Systems, Vol. 5(11), pp. 193-209.
Fathollah Bayati, M. and Sadjadi, S.J. (2016). Two-tier supplier base efficiency evaluation via network dea: A game theory approach. International Journal of Engineering, Vol. 29(7), pp. 931-939.
Forghani, A., Sadjadi, S.J. and Farhang Moghadam, B. (2018). A supplier selection model in pharmaceutical supply chain using PCA, Z-TOPSIS and MILP: A case study. PloS one, Vol. 13(8), pp. 1-17.
Forghani, A., Sadjadi, S.J., and Farhang Moghadam, B. (2021), A scientometric study on supplier selection. Journal of Optimization in Industrial Engineering, Vol. 14(1), pp. 149-158.
Gaballa, A. (1974). Minimum cost allocation of tenders. Journal of the Operational Research Society, Vol. 25(3), pp. 389-398.
Giannakis, M., Dubey, R., Vlachos, I. and Ju, Y. (2020). Supplier sustainability performance evaluation using the analytic network process. Journal of Cleaner Production, Vol. 247, pp. 119439.
Guo, C. and Li, X. (2014). A multi-echelon inventory system with supplier selection and order allocation under stochastic demand. International Journal of Production Economics, Vol. 151, pp. 37-47.
Hamdan, S. and Cheaitou, A. (2017). Supplier selection and order allocation with green criteria: An MCDM and multi-objective optimization approach. Computers & Operations Research, Vol. 81, pp. 282-304.
Ho, H.P. (2019). The supplier selection problem of a manufacturing company using the weighted multi-choice goal programming and MINMAX multi-choice goal programming. Applied Mathematical Modelling, Vol. 75, pp. 819-836.
Hwang, C.L. and Yoon, K. (1981). Methods for multiple attribute decision making. In Multiple attribute decision making, Springer, Berlin, Heidelberg. pp.58-191.
Kilic, H.S. and Yalcin, A.S. (2020). Modified two-phase fuzzy goal programming integrated with IF-TOPSIS for green supplier selection. Applied Soft Computing, Vol. 93, pp. 106371.
Kull, T.J. and Talluri, S. (2008). A supply risk reduction model using integrated multicriteria decision making. IEEE Transactions on Engineering Management, Vol. 55(3), pp. 409-419.
Kuo, R.J. and Lin, Y.J. (2012). Supplier selection using analytic network process and data envelopment analysis. International Journal of Production Research, Vol. 50(11), pp. 2852-2863.
Lee, A.H., Kang, H.Y., Lai, C.M. and Hong, W.Y. (2013).  An integrated model for lot sizing with supplier selection and quantity discounts. Applied Mathematical Modelling, Vol. 37(7), pp. 4733-4746.
Lin, C.T., Chen, C.B. and Ting, Y.C. (2011). An ERP model for supplier selection in electronics industry. Expert Systems with Applications, Vol. 38(3), pp. 1760-1765.
Lotfi, R., Yadegari, Z., Hosseini, S.H., Khameneh, A.H., Tirkolaee, E.B. and Weber, G.W. (2022). A robust time-cost-quality-energy-environment trade-off with resource-constrained in project management: a case study for a bridge construction project. Journal of Industrial & Management Optimization. Vol. 18(1), pp. 375-396.
Marler, R.T. and Arora, J.S. (2004). Survey of multi-objective optimization methods for engineering. Structural and multidisciplinary optimization, Vol. 26(6), pp. 369-395.
Memari, A., Dargi, A., Jokar, M.R.A., Ahmad, R. and Rahim, A.R.A. (2019). Sustainable supplier selection: A multi-criteria intuitionistic fuzzy TOPSIS method. Journal of Manufacturing Systems, Vol. 50, pp. 9-24.
Opricovic, S. (1998). Multicriteria optimization of civil engineering systems. Faculty of Civil Engineering, Belgrade, Vol. 2(1), pp. 5-21.
Ordoobadi, S.M. (2010). Application of AHP and Taguchi loss functions in supply chain. Industrial Management and Data Systems, Vol. 110(8), pp. 1251-1269.
Pramanik, D., Haldar, A., Mondal, S.C., Naskar, S.K. and Ray, A. (2017). Resilient supplier selection using AHP-TOPSIS-QFD under a fuzzy environment. International Journal of Management Science and Engineering Management, Vol. 12(1), pp. 45-54.
Razmi, J. and Keramati, A. (2011). Minimizing the supplying cost of leverage items: A mathematical approach. International Journal of Engineering, Vol. 24(3), pp. 259-273.
Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, Vol. 53, pp. 49-57.
Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega, Vol. 64, pp. 126-130.
Saaty, T.L. (1980). The analytic hierarchy process, New York: McGraw-Hill.
Saaty, T.L. (1996). Decision making with dependence and feedback: The analytic network process, Vol. 4922(2), Pittsburgh: RWS publications.
Shaw, K., Shankar, R., Yadav, S.S. and Thakur, L.S. (2012). Supplier selection using fuzzy AHP and fuzzy multi-objective linear programming for developing low carbon supply chain. Expert systems with applications, Vol. 39(9), pp. 8182-8192.
Wadhwa, V. and Ravindran, A.R. (2007). Vendor selection in outsourcing. Computers & operations research, Vol. 34(12), pp. 3725-3737.
Yazdani, M., Chatterjee, P., Zavadskas, E.K. and Zolfani, S.H. (2017). Integrated QFD-MCDM framework for green supplier selection. Journal of Cleaner Production, Vol. 142, pp. 3728-3740.
Zare Mehrjerdi, Y. and Lotfi, R. (2019). Development of a mathematical model for sustainable closed-loop supply chain with efficiency and resilience systematic framework. International Journal of Supply and Operations Management, Vol. 6(4), pp. 360-388.
International Journal of Supply and Operations Management
Volume 9, Issue 2
May 2022
Pages 149-161

Files

Share

How to cite

Statistics