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Automobile components procurement using a DEA-TOPSIS-FMIP approach with all-unit quantity discount and fuzzy factors

    Jiajia Chen   Affiliation
    ; Zeshui Xu Affiliation
    ; Xunjie Gou   Affiliation
    ; Dongbin Huang Affiliation
    ; Jianchuan Zhang   Affiliation

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
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Apr 12, 2021
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References

Amid, A., Ghodsypour, S. H., & O’Brien, C. (2006). Fuzzy multiobjective linear model for supplier selection in a supply chain. International Journal of Production Economics, 104(2), 394–407. https://doi.org/10.1016/j.ijpe.2005.04.012

Ayhan, M. B., & Kilic, H. S. (2015). A two stage approach for supplier selection problem in multi-item/ multi-supplier environment with quantity discounts. Computers and Industrial Engineering, 85, 1–12. https://doi.org/10.1016/j.cie.2015.02.026

Amid, A., Ghodsypour, S. H., & O’Brien, C. (2009). A weighted additive fuzzy multiobjective model for the supplier selection problem under price breaks in a supply Chain. International Journal of Production Economics, 121(2), 323–332. https://doi.org/10.1016/j.ijpe.2007.02.040

Aktin, T., & Gergin, Z. (2016). Mathematical modelling of sustainable procurement strategies: Three case studies. Journal of Cleaner Production, 113, 767–780. https://doi.org/10.1016/j.jclepro.2015.11.057

Arabzad, S. M., Ghorbani, M., Razmi, J., & Shirouyehzad, H. (2015). Employing fuzzy TOPSIS and SWOT for supplier selection and order allocation problem. The International Journal of Advanced Manufacturing Technology, 76(5–8), 803–818. https://doi.org/10.1007/s00170-014-6288-3

Bai, C. G., & Sarkis, J. (2018). Integrating sustainability into supplier selection: a grey-based topsis analysis. Technological and Economic Development of Economy, 24(6), 2202–2224. https://doi.org/10.3846/tede.2018.5582

Bellman, R. E., & Zadeh, L. A. (1970). Decision-making in fuzzy environment. Management Science, 17(4), 199–203. https://doi.org/10.1287/mnsc.17.4.B141

Bodaghi, G., Jolai, F., & Rabbani, M. (2018). An integrated weighted fuzzy multi-objective model for supplier selection and order scheduling in a supply chain. International Journal of Production Research, 56(10), 3590–3614. https://doi.org/10.1080/00207543.2017.1400706

Bohner, C., & Minner, S. (2017). Supplier selection under failure risk, quantity and business volume discounts. Computers & Industrial Engineering, 104, 145–155. https://doi.org/10.1016/j.cie.2016.11.028

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

Freeman, J., & Chen, T. (2015). Green supplier selection using an AHP-Entropy-TOPSIS framework. Supply Chain Management, 20(3), 327–340. https://doi.org/10.1108/SCM-04-2014-0142

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

Hamdan, S., & Cheaitou, A. (2017a). Dynamic green supplier selection and order allocation with quantity discounts and varying supplier availability. Computers and Industrial Engineering, 110, 573–589. https://doi.org/10.1016/j.cie.2017.03.028

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

Sakawa, M. (1993). Fuzzy sets and interactive multiobjective optimization (pp. 71–101). Plenum Press. https://doi.org/10.1007/978-1-4899-1633-4

Saradhi, B. P., Shankar, N. R., & Suryanarayana, C. (2016). Novel distance measure in fuzzy TOPSIS for supply chain strategy based supplier selection. Mathematical Problems in Engineering, 1, 1–17. https://doi.org/10.1155/2016/7183407

Shadkam, E., & Bijari, M. (2017). Multi-objective simulation optimization for selection and determination of order quantity in supplier selection problem under uncertainty and quality criteria. International Journal of Advanced Manufacturing Technology, 93(1–4), 161–173. https://doi.org/10.1007/s00170-015-7986-1

Stein, J. (2011). Ford purchasing exec behrendt: Consolidation still necessary in supply base. Crain’s Detroit Business. http://www.crainsdetroit.com/article/20110803/FREE/110809962/fordpurchasingexec-behrendt-consolidation-still-necessary-in-supply-base

Stević, Ž., Tanackov, I., Vasiljević, M., Novarlić, B., & Stojić, G. (2016). An integrated fuzzy AHP and topsis model for supplier evaluation. Serbian Journal of Management, 11(1), 15–27. https://doi.org/10.5937/sjm11-10452

Sturgeon, T. J., Memedovic, O., Biesebroeck, J. V., & Gereffi, G. (2013). Globalisation of the automotive industry: main features and trends. International Journal of Technological Learning Innovation & Development, 2(1), 7–24. https://doi.org/10.1504/IJTLID.2009.021954

Taleizadeh, A. A., Stojkovska, I., & Pentico, D. W. (2015). An economic order quantity model with partial backordering and incremental discount. Computers and Industrial Engineering, 82, 21–32. https://doi.org/10.1016/j.cie.2015.01.005

Talluri, S., Decampos, H. A., & Hult, G. T. M. (2013). Supplier rationalization: a sourcing decision model. Decision Sciences, 44(1), 57–86. https://doi.org/10.1111/j.1540-5915.2012.00390.x

Tian, G., Zhang, H., Feng, Y., Jia, H., Zhang, C., & Jiang, Z., et al. (2017). Operation patterns analysis of automotive components remanufacturing industry development in China. Journal of Cleaner Production, 164, 1363–1375. https://doi.org/10.1016/j.jclepro.2017.07.028

Tiwari, R. N., Dharmar, S., & Rao, J. R. (1987). Fuzzy goal programming-an additive model. Fuzzy Sets and Systems, 24(1), 27–34. https://doi.org/10.1016/0165-0114(87)90111-4

Trautrims, A., Maccarthy, B., & Okade, C. (2017). Building an innovation-based supplier portfolio: the use of patent analysis in strategic supplier selection in the automotive sector. International Journal of Production Economics, 194, 228–236. https://doi.org/10.1016/j.ijpe.2017.05.008

Tsai, W. C., & Wang, C. H. (2010). Decision making of sourcing and order allocation with price discounts. Journal of Manufacturing Systems, 29(1), 47–54. https://doi.org/10.1016/j.jmsy.2010.08.002

Ucal Sari, I. (2018) Development of an integrated discounting strategy based on vendors expectations using FAHP and fuzzy goal programming. Technological and Economic Development of Economy, 24(2), 635–652. https://doi.org/10.3846/20294913.2016.1213205

Wang, H. H., Yu, Y. M., Zhang, W., & Hua, Z. S. (2019). Procurement strategies for lost-sales inventory systems with all-units discounts. European Journal of Operational Research, 272(2), 539–548. https://doi.org/10.1016/j.ejor.2018.06.053

Wu, J. (2015). A SD-IITFOWA operator and TOPSIS based approach for MAGDM problems with intuitionistic trapezoidal fuzzy numbers. Technological and Economic Development of Economy, 21(1), 28–47. https://doi.org/10.3846/20294913.2014.946982

Yang, W., Chen, Z. P., & Zhang, F. (2017). New group decision making method in intuitionistic fuzzy setting based on TOPSIS. Technological and Economic Development of Economy, 23(3), 441–461. https://doi.org/10.3846/20294913.2015.1072754

Yu, M. C., & Lin, H. C. (2012). Fuzzy multi-objective vendor selection under lean procurement. European Journal of Operational Research, 219(2), 305–311. https://doi.org/10.1016/j.ejor.2011.12.028

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

Zarbakhshnia, N., & Jaghdani, T. J. (2018). Sustainable supplier evaluation and selection with a novel two-stage DEA model in the presence of uncontrollable inputs and undesirable outputs: a plastic case study. The International Journal of Advanced Manufacturing Technology, 97, 2933–2945. https://doi.org/10.1007/s00170-018-2138-z

Zhang, J. L., & Chen, J. (2013). Supplier selection and procurement decisions with uncertain demand, fixed selection costs and quantity discounts. Computers & Operations Research, 40(11), 2703–2710. https://doi.org/10.1016/j.cor.2013.05.016

Zhou, X., Pedrycz, W., Kuang, Y., & Zhang, Z. (2016). Type-2 fuzzy multi-objective DEA model: an application to sustainable supplier evaluation. Applied Soft Computing, 46, 424–440. https://doi.org/10.1016/j.asoc.2016.04.038

Zimmermann, H. J. (1978). Fuzzy programming and linear programming with several objective functions. Fuzzy Sets & Systems, 1(1), 45–55. https://doi.org/10.1016/0165-0114(78)90031-3