Document Type : Research Paper

Authors

Abstract

Supply chain management (SCM) is one of the most important competitive strategies used by modern companies. The main goal of supply chain management is integration different suppliers to fulfill market demand. Therefore, evaluation and selection of suppliers has critical role and significant effect on supply chain management. This paper presents hybrid model based on clustering approach and suppliers' selection. At first, K-harmonic means clustering method which is one of the most popular methods in clustering analysis is used for clustering suppliers. Then, according to theoutput of clustering, a multi-objective model is considered to select the best supplier. Since the model belongs to the class of NP-hard optimization problems, two meta-heuristic algorithms named Non-dominated Sorting Genetic Algorithm (NSGAII) and Non-dominated Ranked Genetic Algorithm (NRGA) is used for solving model in reasonable time. Computational results show that the clustering analysis can be considered as an effective way to the suppliers' selection. Also, several data sets are applied to evaluate the effect of clustering analysis on suppliers' selection

Keywords

مؤمنی، منصور 1640 ، خوشه بندی داده تهران، منصور مومنیAssaoui, N., Haouari, M., Hassini, E., (2007), " Supplire Selection and Order Lot Sizing Modeling": A review, Computers & Operations Research, 34, 3516- 3540.
Ben-Arieh, D., Gullipalli, D.K., (2012), "Data Envelopment Analysis of clinics with sparse data: Fuzzy clustering approach" ,Computers& Industrial Engineering, 63, 13–21 .
Brucker, P,(1987), On the Complexity of Clustering Problems, Optimization and Operations ResearchLecture Notes inEconomics and Mathematical Systems, 157, 1978, pp 45-54
Che, Z.H.,2010, "Using fuzzy analytic hierarchy process and particle swarm optimisation for balanced and defective supply chain problems considering WEEE/RoHS directives", International Journal of Production Research, 48, 3355–3381.
Che, Z.H., (2012), Clustering and selecting suppliers based on simulated annealing algorithms, Computers and Mathematics with Applications, 63, 228-238.
De Boer, L.; Labro, E. and Morlacchi, P.,2001,A review of methods supporting supplier selection", European Journal of Purchasing and Supply Management, 7(2), pp. 75-89.
Deb, K., Agrawal, S., Pratap, A., and Meyarivan, T. A. (2000). fast elitist non-dominated sorting genetic algorithm for multi-objective optimization": NSGA-II. In Proceedings of the parallel problem solving from nature VI (PPSN-VI) conference , 849–858.
Deb, K. (2001),Multi-objective optimization using evolutionary algorithms". Chichester: Wiley.
Dulmin, R. and Mininno, V.,(2003).Supplier selection using a multi-criteria decision aid method", Journal of Purchasing and Supply Management, 9, pp. 177-187.
011 مطالعات مدیریت صنعتی، سال سیزدهم، شماره 63 ، بهار 49
Jadaan , Al., Rao, O., Rajamani, C.R.,2007.Non-Dominated ranked genetic algorithm for solving Multi-Objective optimization problems: NRGA, Journal of Theoritical and Applied Information Technology, 60-67 .
Jiang, H., Shenghe, Y, Jing, L., Yang, F., Hu.,2010Ant clustering algorithm with K-harmonic means clustering, Expert Systems with Applications, 37, 8679–8684.
Kaufman L. and Rousseeuw P. J.,1990FindinGroups in Data. An IintroductiontoCluster AnalysisWiley-Interscience,
Mehdizadeh, E., Tavakkoli Moghaddam, R., (2008), Fuzzy Particle Swarm Optimization Algorithm for a Supplier Clustering Problem Journal of Industrial Engineering 1, 17-24.
Po, R.W., Guh, Y.Y., Yang, M.S.,2009.A new clustering approach using data envelopment analysis. European Journal of Operational Researcha,199, 276–284.
Razi, F. (2014). A supplier selection using a hybrid grey based hierarchical clustering and artificial bee colony.Decision Science Letters , 3(3), 259-268.
Schott, JR., (1995), Fault tolerant design using single and multi-criteria genetic algorithms optimization. Master's thesis, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA.
Shligram,P.,2008.A two objective model for decision making in a supply chainInt.J.Production Economics, 111, 378-388.
Wang, S.T., Wang, Z.J.,2005. Study of the application of PSO algorithms for nonlinear problems, Journal of Huazhong University of Science and Technology ,33, 4–7
Zitzler, E., (1999), Evolutionary Algorithms for Multi-objective Optimization: Methods and Applications. PhD. Thesis, Dissertation ETH No. 13398, SwissFederal Institute of Technology (ETH),Zürich, Switzerland.