الفهرس 
Title : Solving Multi–Level Multiple Criteria Decision Making Problems Using TOPSIS Approach
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Abstract
TOPSIS was first developed by C. L. Hwang and K. Yoon [38] for solving a MADM problems. It is based upon the principle that the chosen alternative should have the shortest distance from the PIS and the farthest from the NIS. The single criterion of the shortest distance from the given goal or the PIS may be not enough to decision makers. In practice, we might like to have a decision which not only makes as much profit as possible, but also avoids as much risk as possible. A similar concept has also been pointed out by M. Zeleny [99], (see Y. J. Lai et. al. 1994 [48]). This thesis develops algorithms to solve multi{u2013}level MODM problems with different kinds of parameters (fuzzy, stochastic, rough set) Using TOPSIS Approach. The thesis consists of six chapters. Chapter (1): illustrates the domain of our research and highlights the thesis motivation in addition to the thesis structure. Chapter (2): is divided into four sections. In the first section, i briefly explain and highlighting the literature of bi-level MCDM. In the second section, i briefly explain and highlighting the literature of multi-level MCDM. In the third section, i briefly explain and highlighting the literature of TOPSIS approach. In the forth section, i briefly explain and highlighting the literature of different softwares for different MODM methods. Chapter (3): presents three cases of the fuzzy multi-level MODM problems and the TOPSIS algorithm for every case. After each proposed algorithm, a numerical example has been illustrated and the results are discussed and compared to the results of the traditional methods. Chapter (4): presents two cases of the rough MODM problems and the TOPSIS algorithm for every case. After each proposed algorithm, a numerical example has been illustrated and the results are discussed and compared to the results of the traditional methods. Chapter (5): presents a solution algorithm for a special case of the stochastic multi-level MODM problems. After that, a numerical example has been illustrated and the results are discussed and compared to the results of the traditional methods. Chapter (6): concludes our work and highlighting its strengths and shortcomings. It also shares our thoughts about how we will continue our research in this domain