Automobile components procurement using a DEA-TOPSIS-FMIP approach with all-unit quantity discount and fuzzy factors
Abstract
Components procurement is a crucial process in supply chain management of the automobile industry. The problem is further complicated by imprecise information and discount policies provided by suppliers. This paper aims to develop a computational approach for assisting automobile components procurement with all-unit quantity discount policy and fuzzy factors, from potential suppliers offering different product portfolios. We propose a two-stage approach consisting of a DEA-TOPSIS (data envelopment analysis procedures followed with a technique for order preference by similarity to an ideal solution) approach for screening suppliers, and subsequentially a fuzzy mixed integer programming (FMIP) model with multiple objectives for optimizing order allocations. The DEA-TOPSIS approach integrates suppliers’ comparative performance and diversity performance into an overall index that improves the ranking of potential suppliers, while the FMIP model features a soft time-window in delivery punctuality and an all-unit quantity discount function in cost. By applying it in a case of automobile components procurement, we show that this two-stage approach effectively supports decision makers in yielding procurement plans for various components offered by many potential suppliers. This paper contributes to integrating multi-attribute decision analysis approach in the form of DEA crossevaluation with TOPSIS and FMIP model for supporting components procurement decisions.
First published online 19 November 2020
Keyword : automobile components procurement, all-unit quantity discount, soft time-window, DEA cross-evaluation, TOPSIS, fuzzy mixed integer programming
How to Cite
Chen, J., Xu, Z., Gou, X., Huang, D., & Zhang, J. (2021). Automobile components procurement using a DEA-TOPSIS-FMIP approach with all-unit quantity discount and fuzzy factors. Technological and Economic Development of Economy, 27(2), 311-352. https://doi.org/10.3846/tede.2020.13176
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Charnes, A., Cooper W. W., & Rhodes, E. (1978). Measuring efficiency of decision-making units. European Journal of Operational Research, 2(6), 429–444. https://doi.org/10.1016/0377-2217(78)90138-8
Cheraghalipour, A., & Farsad, S. (2018). A bi-objective sustainable supplier selection and order allocation considering quantity discounts under disruption risks: a case study in plastic industry. Computers & Industrial Engineering, 118, 237–250. https://doi.org/10.1016/j.cie.2018.02.041
Corbett, C. J. (2000). A Suppliers optimal quantity discount policy under asymmetric information. Management Science, 46(3), 444–450. https://doi.org/10.1287/mnsc.46.3.444.12065
Doyle, J., & Green, R. (1994). Efficiency and cross-efficiency in DEA: Derivations, meanings and uses. Journal of the Operational Research Society, 45(5), 567–568. https://doi.org/10.1057/jors.1994.84
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Ghadimi, P., Dargi, A., & Heavey, C. (2017). Making sustainable sourcing decisions: practical evidence from the automotive industry. International Journal of Logistics-Research and Applications, 20(4), 297–321. https://doi.org/10.1080/13675567.2016.1227310
Ghaniabadi, M., & Mazinani, A. (2017). Dynamic lot sizing with multiple suppliers, backlogging and quantity discounts. Computers & Industrial Engineering, 110, 67–74. https://doi.org/10.1016/j.cie.2017.05.031
Gupta, P., Govindan, K., Mehlawat, M. K., & Kumar, S. (2016). A weighted possibilistic programming approach for sustainable vendor selection and order allocation in fuzzy environment. The International Journal of Advanced Manufacturing Technology, 86(5–8), 1785–1804. https://doi.org/10.1007/s00170-015-8315-4
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Hamdan, S., & Cheaitou, A. (2017b). Green supplier selection and order allocation with incremental quantity discounts. In 2017 7th International Conference on Modeling, Simulation, and Applied Optimization, (ICMSAO). https://doi.org/10.1109/ICMSAO.2017.7934913
Homfeldt, F., Rese A., Brenner, H., Baier, D., & Schaefer, T.F. (2017). Identification and generation of innovative ideas in the procurement of the automotive industry: the case of audi ag. International Journal of Innovation Management, 21(7), 1750053. https://doi.org/10.1142/S1363919617500530
Hwang, C. L., & Yoon, K. P. (1981). Multiple attribute decision making: methods and applications (pp. 58–191). Springer-Verlag. https://doi.org/10.1007/978-3-642-48318-9
Ioannou, G., Kritikos, M., & Prastacos, G. (2003). A problem generator-solver heuristic for vehicle routing with soft time windows. Omega, 31(1), 41–53. https://doi.org/10.1016/S0305-0483(02)00064-6
Land, A. H., & Doig, A. G. (1960). An automatic method of solving discrete programming problem. Econometrica, 28(3), 497–520. https://doi.org/10.2307/1910129
Jadidi, O., Cavalieri, S., & Zolfaghari, S. (2015). An improved multi-choice goal programming approach for supplier selection problems. Applied Mathematical Modelling, 39(14), 4213–4222. https://doi.org/10.1016/j.apm.2014.12.022
Jahangoshai Rezaee, M., Yousefi, S., & Hayati, J. (2017). A multi-objective model for closed-loop supply chain optimization and efficient supplier selection in a competitive environment considering quantity discount policy. Journal of Industrial Engineering International, 13(2), 199–213. https://doi.org/10.1007/s40092-016-0178-2
Jauhar, S. K., & Pant, M. (2017). Integrating DEA with de and mode for sustainable supplier selection. Journal of Computational Science, 21, 299–306. https://doi.org/10.1016/j.jocs.2017.02.011
Jin, Y., Ryan, J. K., & Yund, W. (2014). Two stage procurement processes with competitive suppliers and uncertain supplier quality. IEEE Transactions on Engineering Management, 61(1), 147–158. https://doi.org/10.1109/TEM.2013.2266276
Keshavarz Ghorabaee, M. K., Amiri, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2017). A new multi-criteria model based on interval type-2 fuzzy sets and edas method for supplier evaluation and order allocation with environmental considerations. Computers & Industrial Engineering, 112, 156–174. https://doi.org/10.1016/j.cie.2017.08.017
Kumar, M., Vrat, P., & Shankar, R. (2004). A fuzzy goal programming approach for vendor selection problem in a supply chain. Computers & Industrial Engineering, 46(1), 69–85. https://doi.org/10.1016/j.cie.2003.09.010
Manerba, D., & Perboli, G. (2019). New solution approaches for the capacitated supplier selection problem with total quantity discount and activation costs under demand uncertainty. Computers & Industrial Engineering, 101, 29–42. https://doi.org/10.1016/j.cor.2018.08.010
Mazdeh, M. M., Emadikhiav, M., & Parsa, I. (2015). A heuristic to solve the dynamic lot sizing problem with supplier selection and quantity discounts. Computers and Industrial Engineering, 85, 33–43. https://doi.org/10.1016/j.cie.2015.02.027
Memon, M. S., Lee, Y. H., & Mari, S. I. (2015). Group multi-criteria supplier selection using combined grey systems theory and uncertainty theory. Expert Systems with Applications, 42(21), 7951–7959. https://doi.org/10.1016/j.eswa.2015.06.018
Mirzaee, H., Naderi, B., & Pasandideh, S. H. R. (2018). A preemptive fuzzy goal programming model for generalized supplier selection and order allocation with incremental discount. Computers & Industrial Engineering, 122, 292–302. https://doi.org/10.1016/j.cie.2018.05.042
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 Engineering, 129, 192–209. https://doi.org/10.1016/j.cie.2019.01.011
Nazari-Shirkouhi, S., Shakouri, H., Javadi, B., & Keramati, A. (2013). Supplier selection and order allocation problem using a two-phase fuzzy multi-objective linear programming. Applied Mathematical Modelling, 37(22), 9308–9323. https://doi.org/10.1016/j.apm.2013.04.045
Niroomand, S., Mahmoodirad, A., & Mosallaeipour, S. (2019). A hybrid solution approach for fuzzy multiobjective dual supplier and material selection problem of carton box production systems. Expert Systems, 36(1), 1–17. https://doi.org/10.1111/exsy.12341
Pascual, R., Santelices, G., Lüer-Villagra, A., Vera, J., & Cawley, A. M. (2017). Optimal repairable spareparts procurement policy under total business volume discount environment. Reliability Engineering & System Safety, 159, 276–282. https://doi.org/10.1016/j.ress.2016.10.034
Park, S. J., Ok, C., & Ha, C. (2017). A stochastic simulation-based holistic evaluation approach with DEA for vendor selection. Computers & Operations Research, 100, 368–378. https://doi.org/10.1016/j.cor.2017.08.005
Razmi, J., & Maghool, E. (2010). Multi-item supplier selection and lot-sizing planning under multiple price discounts using augmented e-constraint and Tchebycheff method. International Journal of Advanced Manufacturing Technology, 49(1–4), 379–392. https://doi.org/10.1007/s00170-009-2392-1
Riedl, D. F., Kaufmann, L., Zimmermann, C., & Perols, J. L. (2013). Reducing uncertainty in supplier selection decisions: antecedents and outcomes of procedural rationality. Journal of Operations Management, 31(1–2), 24–36. https://doi.org/10.1016/j.jom.2012.10.003
Sabouhi, F., Pishvaee, M. S., & Pishvaee, M. S. (2018). Resilient supply chain design under operational and disruption risks considering quantity discount: A case study of pharmaceutical supply chain. Computers & Industrial Engineering, 126, 657–672. https://doi.org/10.1016/j.cie.2018.10.001
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