Developing an approach for decision making units integration using multi objective particle swarm optimization
-
2014-12-30 https://doi.org/10.14419/ijet.v4i1.3775
-
Decision Making Unit Integration, Multi Objective Particle Swarm Optimization, Data Envelopment Analysis, DMU Efficiency. -
Abstract
In recent years, integration between companies, suppliers or organizational departments attracted much attention. Decision making about integration encounters with major concerns. One of these concerns is which units should be integrated and what is the effect of integration on performance measures. In this paper the problem of decision making unit (DMU) integration is considered. It is tried to integrate DMUs so that the considered criteria are satisfied. In this research two criteria are considered that are mean of efficiencies of DMUs and the difference between DMUs that have largest and smallest efficiencies. For this purpose multi objective particle swarm optimization (MOPSO) is applied. A case with 17 DMUs is considered. The results show that integration has increased both considered criteria effectively. Additionally this approach can presents different alternatives for decision maker (DM) that enables DM to select the final decision for integration.
-
References
[1] Iyer, K.N.S., Germain, R., Claycomb, C., B2B e-commerce supply chain integration and performance: A contingency fit perspective on the role of environment, Information and Management 46 (2009), pp. 313-322 http://dx.doi.org/10.1016/j.im.2009.06.002.
[2] Li, G., Yang, H., Sun, L., Sohal, A.S., The impact of IT implementation on supply chain integration and performance, International Journal of Production Economics, 120 (2009), pp. 125-138 http://dx.doi.org/10.1016/j.ijpe.2008.07.017.
[3] Sohail, M.S., Sayeed, M.A., Al-Fadhil, A., Sahin, O.G., Strategic supply chain integration and its impact on organisation performance: Perspectives from an emerging nation, International Journal of Integrated Supply Management 5 (2009), pp. 62-80 http://dx.doi.org/10.1504/IJISM.2009.026205.
[4] Kim, S.W., An investigation on the direct and indirect effect of supply chain integration on firm performance, International Journal of Production Economics 119 (2009), pp. 328-346 http://dx.doi.org/10.1016/j.ijpe.2009.03.007.
[5] Tsanos, C.S., Zografos, K.G., Modeling the relationship between supply chain integration and integrated supply chain performance, (2008), Proceedings of 2008 IEEE International Conference on Service Operations and Logistics and Informatics, IEEE/SOLI 2008 2, art. no. 4682884, pp. 2119-2124
[6] Han, J., Omta, S.W.F., Trienekens, J.H., The joint impact of supply chain integration and quality management on the performance of pork processing firms in China, International Food and Agribusiness Management Review 10 (2007), pp. 67-95
[7] Rai, A., Patnayakuni, R., Seth, N., Firm performance impacts of digitally enabled supply chain integration capabilities, MIS Quarterly: Management Information Systems 30 (2006), pp. 225-246
[8] Liu, C.M., Hsu, H. S., Wang, S.T., Lee, H. K., A performance evaluation model based on AHP and DEA, Journal of the Chinese Institute of Industrial Engineers 22 (2005), pp. 243-251 http://dx.doi.org/10.1080/10170660509509294.
[9] Hu, C. C., Li, H.H., A company performance measurement by roic and dea-an example of Taiwan IC design house, Journal of the Chinese Institute of Industrial Engineers 21 (2004), pp. 369-383 http://dx.doi.org/10.1080/10170660409509417.
[10] Bao, C.P., Lee, K.C., Pu, H.L., A study of the input/output variable characteristics in DEA, Journal of the Chinese Institute of Industrial Engineers, 27 (2010), pp. 429-437. http://dx.doi.org/10.1080/10170669.2010.525393.
[11] Hatefi, S.M., Jolai, F. (2010). A new model for classifying inputs and outputs and evaluating the performance of DMUs based on translog output distance function, Applied Mathematical Modelling 34 (2010), pp. 1439-1449. http://dx.doi.org/10.1016/j.apm.2009.08.017.
[12] Hatefi, S.M., Torabi, S.A., A common weight MCDA–DEA approach to construct composite indicators, Ecological Economics, 70 (2010): 114-120. http://dx.doi.org/10.1016/j.ecolecon.2010.08.014.
[13] Hatefi, S.M., Razmi, J., An integrated methodology for supplier selection and order allocation in the presence of imprecise data, International Journal of Industrial and Systems Engineering 15 (2013): 51-68. http://dx.doi.org/10.1504/IJISE.2013.055511.
[14] Hatefi, S.M. and Jolai, F., A model for classification of intermediate measures and evaluating the performance of chain and its members, International Journal of Operational Research 17 (2013): 199-214. http://dx.doi.org/10.1504/IJOR.2013.053623.
[15] Torabi, S.A., Hatefi, S.M., Saleck Pay, B., ABC inventory classification in the presence of both quantitative and qualitative criteria, Computers & Industrial Engineering, 63 (2012): 530-537. http://dx.doi.org/10.1016/j.cie.2012.04.011.
[16] Hatefi, S. M., Torabi, S. A., Bagheri, P., Multi-criteria ABC inventory classification with mixed quantitative and qualitative criteria, International Journal of Production Research 52 (2014): 776-786. http://dx.doi.org/10.1080/00207543.2013.838328.
-
Downloads
-
How to Cite
Haeri, A., Rezaie, K., & Hatefi, S. M. (2014). Developing an approach for decision making units integration using multi objective particle swarm optimization. International Journal of Engineering & Technology, 4(1), 48-53. https://doi.org/10.14419/ijet.v4i1.3775Received date: 2014-10-31
Accepted date: 2014-11-24
Published date: 2014-12-30