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Amin, Gholam R.; Emrouznejad, Ali; Gattoufi, Said
Languages: English
Types: Article
Subjects:
Many production systems have acquisition and merge operations to increase productivity. This paper proposes a novel method to anticipate whether a merger in a market is generating a major or a minor consolidation, using InvDEA model. A merger between two or more decision making units (DMUs) producing a single merged DMU that affects the efficiency frontier, defined by the pre-consolidation market conditions, is called a major consolidation. The corresponding alternative case is called a minor consolidation. A necessary and sufficient condition to distinguish the two types of consolidations is proven and two numerical illustrations in banking and supply chain management are discussed. The crucial importance of anticipating the magnitude of a consolidation in a market is outlined.
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    • Ahuja, R. K., & Orlin, J. B. (2001). Inverse Optimization. Operations Research, 49(5), 771-783.
    • Amarchinta, H. K., Grandhi, R. V., Clauer, A. H., Langer, K., & Stargel, D. S. (2010). Simulation of residual stress induced by a laser pending process through inverse optimization of material models. Journal of Materials Processing Technology, 210(14), 1997-2006.
    • Amin, G. R., & Al-Muharrami, S. (2016). A new inverse data envelopment analysis model for mergers with negative data. IMA Journal of Management Mathematics. http://dx.doi.org/10.1093/imaman/dpw016. in press.
    • Amin, G. R., & Emrouznejad, A. (2007a). Inverse forecasting: A new approach for predictive modeling. Computers & Industrial Engineering, 53(3), 491-498.
    • Amin, G. R., & Emrouznejad, A. (2007b). Inverse linear programming in DEA. International Journal of Operations Research, 4(2), 105-109.
    • Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078-1092.
    • Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444.
    • Chow, J. Y. J., & Recker, W. W. (2012). Inverse optimization with endogenous arrival time constraints to calibrate the household activity pattern problem. Transportation Research Part B: Methodological, 46(3), 463-479.
    • Emrouznejad, A., & De Witte, K. (2010). COOPER-framework: A unified process for non-parametric projects. European Journal of Operational Research, 207(3), 1573-1586.
    • Emrouznejad, A., Parker, B. R., & Tavares, G. (2008). Evaluation of research in efficiency and productivity: A survey and analysis of the first 30 years of scholarly literature in DEA. Socio-Economic Planning Sciences, 42(3), 151-157.
    • Gattoufi, S., Amin, G. R., & Emrouznejad, A. (2014). A new inverse DEA method for merging banks. IMA Journal of Management Mathematics, 25(1), 73-87.
    • Gattoufi, S., Oral, M., Kumar, A., & Reisman, A. (2004). Content analysis of data envelopment analysis literature and its comparison with that of other OR/MS fields. Journal of the Operational Research Society, 55, 911-935.
    • Ghiyasi, M. (2015). On inverse DEA model: The case of variable returns to scale. Computers & Industrial Engineering, 87, 407-409.
    • Huang, S., & Liu, Z. (1999). On the inverse problem of linear programming and its application to minimum weight perfect k-matching. European Journal of Operational Research, 112(2), 421-426.
    • Jiang, Y., Xiao, X., Zhang, L., & Zhang, J. (2011). A perturbation approach for a type of inverse linear programming problems. International Journal of Computer Mathematics, 88(3), 508-516.
    • Lertworasirikul, S., Charnsethikul, P., & Fang, S. C. (2011). Inverse data envelopment analysis model to preserve relative efficiency values: The case of variable returns to scale. Computers & Industrial Engineering, 61, 1017-1023.
    • Liu, Y., & Li, J. (2009). Modelling and analysis of split and merge production systems with Bernoulli reliability machines. International Journal of Production Research, 47(16), 4373-4397.
    • Liu, J. S., Lu, L. Y. Y., Lu, W. M., & Lin, B. J. Y. (2013). Data envelopment analysis 1978- 2010: A citation-based literature survey. Omega, 41, 3-15.
    • Mirhedayatian, S. M., Azadi, M., & Farzipoor Saen, R. (2014). A novel network data envelopment analysis model for evaluating green supply chain management. International Journal of Production Economics, 147, 544-554.
    • Pibernik, R., Zhang, Y., Kerschbaum, F., & Schröpfer, A. (2011). Secure collaborative supply chain planning and inverse optimization-The JELS model. European Journal of Operational Research, 208(1), 75-85.
    • Roch, J. R., Canelas, A., & Herskovits, J. (2012). Shape optimization for inverse electromagnetic casting problems. Inverse Problems in Science and Engineering, 20(7), 951-972.
    • Ruiz, C., Conejo, A. J., & Bertsimas, D. J. (2013). Revealing rival marginal offer prices via inverse optimization. IEEE Transactions on Power Systems, 28(3), 3056-3064.
    • Terekhov, A. V., & Zatsiorsky, V. M. (2011). Analytical and numerical analysis of inverse optimization problems: Conditions of uniqueness and computational methods. Biological Cybernetics, 104(1-2), 75-93.
    • Wang, M., Xu, F., & Wang, G. (2014). Sparse portfolio rebalancing model based on inverse optimization. Optimization Methods and Software, 29(2), 297-309.
    • Wei, Q., Zhang, J., & Zhang, X. (2000). An inverse DEA model for inputs/outputs estimate. European Journal of Operational Research, 121(1), 151-163.
    • Yan, H., Wei, Q., & Hao, G. (2002). DEA models for resource reallocation and production input/output estimation. European Journal of Operational Research, 136(1), 19-31.
    • Zhang, J., & Liu, Z. (1996). Calculating some inverse linear programming problems. Journal of Computational and Applied Mathematics, 72(2), 261-273.
    • Zhang, J., & Xu, C. (2010). Inverse optimization for linearly constrained convex separable programming problems. European Journal of Operational Research, 200 (3), 671-679.
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