An integrated model of material supplier selection and order allocation using fuzzy extended AHP and multiobjective programming. (English) Zbl 1296.90077
Summary: This paper presents a supplier selection and order allocation (SSOA) model to solve the problem of a multiperiod supplier selection and then order allocation in the environment of short product life cycle and frequent material purchasing, for example, fast fashion environment in apparel industry. At the first stage, with consideration of multiple decision criteria and the fuzziness of the data involved in deciding the preferences of multiple decision variables in supplier selection, the fuzzy extent analytic hierarchy process (FEAHP) is adopted. In the second stage, supplier ranks are inputted into an order allocation model that aims at minimizing the risk of material purchasing and minimizing the total material purchasing costs using a dynamic programming approach, subject to constraints on deterministic customer demand and deterministic supplier capacity. Numerical examples are presented, and computational results are reported.
MSC:
| 90B90 | Case-oriented studies in operations research |
| 90C70 | Fuzzy and other nonstochastic uncertainty mathematical programming |
| 90C29 | Multi-objective and goal programming |
Software:
DENFISReferences:
| [1] | L. de Boer, E. Labro, and P. Morlacchi, “A review of methods supporting supplier selection,” European Journal of Purchasing and Supply Management, vol. 7, no. 2, pp. 75-89, 2001. · doi:10.1016/S0969-7012(00)00028-9 |
| [2] | A. Aamodt and E. Plaza, “Case-based reasoning: foundational issues, methodological variations, and system approaches,” AI Communications, vol. 7, no. 1, pp. 39-59, 1994. |
| [3] | Z. Degraeve, E. Labro, and F. Roodhooft, “Evaluation of vendor selection models from a total cost of ownership perspective,” European Journal of Operational Research, vol. 125, no. 1, pp. 34-58, 2000. · Zbl 0959.90027 · doi:10.1016/S0377-2217(99)00199-X |
| [4] | H. Deng, C. H. Yeh, and R. J. Willis, “Inter-company comparison using modified TOPSIS with objective weights,” Computers and Operations Research, vol. 27, no. 10, pp. 963-973, 2000. · Zbl 0970.90038 · doi:10.1016/S0305-0548(99)00069-6 |
| [5] | T. L. Saaty, “How to make a decision: the analytic hierarchy process,” European Journal of Operational Research, vol. 48, no. 1, pp. 9-26, 1990. · Zbl 0707.90002 · doi:10.1016/0377-2217(90)90057-I |
| [6] | R. E. Steuer and P. Na, “Multiple criteria decision making combined with finance: a categorized bibliographic study,” European Journal of Operational Research, vol. 150, no. 3, pp. 496-515, 2003. · Zbl 1044.90043 · doi:10.1016/S0377-2217(02)00774-9 |
| [7] | B. Srdjevic and Z. Srdjevic, “Bi-criteria evolution strategy in estimating weights from the ahp ratio-scale matrices,” Applied Mathematics and Computation, vol. 218, no. 4, pp. 1254-1266, 2011. · Zbl 1229.65070 · doi:10.1016/j.amc.2011.06.006 |
| [8] | A. Ishizaka, D. Balkenborg, and T. Kaplan, “Does AHP help us make a choice? An experimental evaluation,” Journal of the Operational Research Society, vol. 62, no. 10, pp. 1801-1812, 2011. · doi:10.1057/jors.2010.158 |
| [9] | P. J. M. van Laarhoven and W. Pedrycz, “A fuzzy extension of Saaty’s priority theory,” Fuzzy Sets and Systems, vol. 11, no. 1-3, pp. 229-241, 1983. · Zbl 0528.90054 · doi:10.1016/S0165-0114(83)80083-9 |
| [10] | J. J. Buckley, “Fuzzy hierarchical analysis,” Fuzzy Sets and Systems, vol. 17, no. 3, pp. 233-247, 1985. · Zbl 0602.90002 · doi:10.1016/0165-0114(85)90090-9 |
| [11] | F. T. S. Chan and N. Kumar, “Global supplier development considering risk factors using fuzzy extended AHP-based approach,” Omega, vol. 35, no. 4, pp. 417-431, 2007. · doi:10.1016/j.omega.2005.08.004 |
| [12] | Y. M. Wang, Y. Luo, and Z. Hua, “On the extent analysis method for fuzzy AHP and its applications,” European Journal of Operational Research, vol. 186, no. 2, pp. 735-747, 2008. · Zbl 1144.90011 · doi:10.1016/j.ejor.2007.01.050 |
| [13] | M. Dagdeviren and I. Yuksel, “Developing a fuzzy analytic hierarchy process (ahp) model for behavior-based safety management,” Information Sciences, vol. 178, no. 6, pp. 1717-1733, 2008. · doi:10.1016/j.ins.2007.10.016 |
| [14] | S. H. Ghodsypour and C. O’Brien, “A decision support system for supplier selection using an integrated analytic hierarchy process and linear programming,” International Journal of Production Economics, vol. 56-57, pp. 199-212, 1998. · doi:10.1016/S0925-5273(97)00009-1 |
| [15] | H. Fazlollahtabar, I. Mahdavi, M. T. Ashoori, S. Kaviani, and N. Mahdavi-Amiri, “A multi-objective decision-making process of supplier selection and order allocation for multi-period scheduling in an electronic market,” International Journal of Advanced Manufacturing Technology, vol. 52, no. 9-12, pp. 1039-1052, 2011. · doi:10.1007/s00170-010-2800-6 |
| [16] | Y. Tang, Z. Wang, and J. A. Fang, “Controller design for synchronization of an array of delayed neural networks using a controllable probabilistic PSO,” Information Sciences, vol. 181, no. 20, pp. 4715-4732, 2011. · doi:10.1016/j.ins.2010.09.025 |
| [17] | S. H. Ghodsypour and C. O’Brien, “The total cost of logistics in supplier selection, under conditions of multiple sourcing, multiple criteria and capacity constraint,” International Journal of Production Economics, vol. 73, no. 1, pp. 15-27, 2001. · doi:10.1016/S0925-5273(01)00093-7 |
| [18] | Z. Guo, W. Wong, S. Leung, and M. Li, “Applications of artificial intelligence in the apparel industry: a review,” Textile Research Journal, vol. 81, no. 18, pp. 1871-1892, 2011. · doi:10.1177/0040517511411968 |
| [19] | Y. Tang, H. Gao, J. Kurths, and J. Fang, “Evolutionary pinning control and its application in uav coordination,” IEEE Transactions on Industrial Informatics, vol. 8, no. 4, pp. 828-838, 2012. · doi:10.1109/TII.2012.2187911 |
| [20] | Y. Tang, Z. Wang, W. Wong, J. Kurths, and J. Fang, “Feedback learning particle swarm optimization,” Applied Soft Computing, vol. 11, no. 8, pp. 4713-4725, 2011. · doi:10.1016/j.asoc.2011.07.012 |
| [21] | W. Zhu, Y. Tang, J. Fang, and W. Zhang, “Adaptive population tuning scheme for differential evolution,” Information Sciences, vol. 223, pp. 164-191, 2013. · doi:10.1016/j.ins.2012.09.019 |
| [22] | W. Zhu, J. Fang, Y. Tang, W. Zhang, and Y. Xu, “Identification of fractional-order systems via a switching differential evolution subject to noise perturbations,” Physics Letters A, vol. 376, no. 45, pp. 3113-33120, 2012. · doi:10.1016/j.physleta.2012.09.042 |
| [23] | W. Zhu, J. Fang, Y. Tang, W. Zhang, and W. Du, “Digital IIR filters design using differential evolution algorithm with a controllable probabilistic population size,” PLoS One, vol. 7, Article ID e40549, 2012. |
| [24] | E. A. Demirtas and Ö. Üstün, “An integrated multiobjective decision making process for supplier selection and order allocation,” Omega, vol. 36, no. 1, pp. 76-90, 2008. · doi:10.1016/j.omega.2005.11.003 |
| [25] | B. Soylu and S. Kapan Ulusoy, “A preference ordered classification for a multi-objective maxmin redundancy allocation problem,” Computers and Operations Research, vol. 38, no. 12, pp. 1855-1866, 2011. · Zbl 1215.90040 · doi:10.1016/j.cor.2011.02.024 |
| [26] | Y. Tang, Z. Wang, W. K. Wong, J. Kurths, and J. Fang, “Multiobjective synchronization of coupled systems,” Chaos, vol. 21, no. 2, article 025114, 2011. · Zbl 1317.34125 · doi:10.1063/1.3595701 |
| [27] | N. Xu and L. Nozick, “Modeling supplier selection and the use of option contracts for global supply chain design,” Computers and Operations Research, vol. 36, no. 10, pp. 2786-2800, 2009. · Zbl 1160.90325 · doi:10.1016/j.cor.2008.12.013 |
| [28] | H. M. Wagner and T. M. Whitin, “Dynamic version of the economic lot size model,” Management Science, vol. 5, pp. 89-96, 1958. · Zbl 0977.90500 · doi:10.1287/mnsc.5.1.89 |
| [29] | C. Basnet and J. M. Y. Leung, “Inventory lot-sizing with supplier selection,” Computers & Operations Research, vol. 32, no. 1, pp. 1-14, 2005. · Zbl 1076.90002 · doi:10.1016/S0305-0548(03)00199-0 |
| [30] | B. Alidaee and G. A. Kochenberger, “A note on a simple dynamic programming approach to the single-sink, fixed-charge transportation problem,” Transportation Science, vol. 39, no. 1, pp. 140-143, 2005. · doi:10.1287/trsc.1030.0055 |
| [31] | S. Li, A. Murat, and W. Huang, “Selection of contract suppliers under price and demand uncertainty in a dynamic market,” European Journal of Operational Research, vol. 198, no. 3, pp. 830-847, 2009. · Zbl 1176.90444 · doi:10.1016/j.ejor.2008.09.038 |
| [32] | T. Sawik, “Selection of a dynamic supply portfolio in make-to-order environment with risks,” Computers & Operations Research, vol. 38, no. 4, pp. 782-796, 2011. · Zbl 1202.90166 · doi:10.1016/j.cor.2010.09.011 |
| [33] | R. H. Lin, “An integrated FANP-MOLP for supplier evaluation and order allocation,” Applied Mathematical Modelling, vol. 33, no. 6, pp. 2730-2736, 2009. · Zbl 1205.90051 · doi:10.1016/j.apm.2008.08.021 |
| [34] | F. Mafakheri, M. Breton, and A. Ghoniem, “Supplier selection-order allocation: a two-stage multiple criteria dynamic programming approach,” International Journal of Production Economics, vol. 132, no. 1, pp. 52-57, 2011. · doi:10.1016/j.ijpe.2011.03.005 |
| [35] | L. A. Zadeh, “Fuzzy sets,” Information and Computation, vol. 8, pp. 338-353, 1965. · Zbl 0139.24606 · doi:10.1016/S0019-9958(65)90241-X |
| [36] | N. K. Kasabov and Q. Song, “Denfis: dynamic evolving neural-fuzzy inference system and its application for time-series prediction,” IEEE Transactions on Fuzzy Systems, vol. 10, no. 2, pp. 144-154, 2002. · doi:10.1109/91.995117 |
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.
