Compromise Solutions for a Special Type of Stochastic Multi-Level Linear Multiple Objective Decision Making Problems

Compromise Solutions for a Special Type of Stochastic Multi-Level Linear Multiple Objective Decision Making Problems

Tarek H.M. Abou-El-EnienShereen F. El-Feky 

Department of Operations Research & Decision Support, Faculty of Computers & Information, Cairo University, 5 Dr. Ahmed Zoweil St., Orman 12613, Giza, Egypt

Teaching Assistant at Faculty of Computer Science, Department of Computer Science, Modern Science and Arts University, 6th of October city-Giza, Egypt

Corresponding Author Email: 
t.hanafy@fci-cu.edu.eg
Page: 
222-231
|
DOI: 
https://doi.org/10.18280/ama_a.550407
Received: 
29 August 2018
| |
Accepted: 
16 November 2018
| | Citation

OPEN ACCESS

Abstract: 

We introduce the concept of the technique for order preference by similarity to ideal solution (TOPSIS) to develop a methodology to find compromise solutions for the multi-level linear multiple objective decision making (MLLMODM) problems of block angular structure with stochastic parameters in the right hand side of the independent constraints (SMLLMODM) of mixed (Maximize/Minimize)-type. We propose a modified formulas for the distance function from the positive ideal solution (PIS) and the distance function from the negative ideal solution (NIS). We present a new interactive hybrid algorithm based on the proposed TOPSIS approach, the chance constrained programming method and the decomposition method to generate a compromise solutions for these types of mathematical optimization problems. Also, we give an illustrative numerical example to clarify the main results developed in the paper. The solutions of the numerical example by the proposed interactive hybrid algorithm is compared with the solutions of the ideal point (IP) method. In general, the results show that, the proposed hybrid TOPSIS method is a good tool to generate compromise solutions for the SMLLMODM problems of mixed type.

Keywords: 

compromise programming, stochastic programming, decomposition techniques, multiple objective decision making, multi-level programming

1. Introduction
2. Formulation of the Problem
3. TOPSIS for (SMLLMODM) of Block Angular Structure
4. Illustrative Numerical Example for the Hybrid Algorithm
5. Conclusions and Future Works
Appendix
  References

[1] Liu GP, Yang JB, Whidborne JF. (2003). Multiobjective Optimisation and Control, Research Studies Press LTD, Baldock, UK. 

[2] Zeleny M. (1982). Multiple Criteria Decision Making, McGraw-Hill, New York. 

[3] Abou-El-Enien THM. (2011). On the solution of a special type of large scale integer linear vector optimization problems with uncertain data through TOPSIS approach. International Journal of Contemporary Mathematical Sciences 6(14): 657–669. 

[4] Abou-El-Enien THM. (2013). TOPSIS Algorithms for multiple objectives decision making: Large scale programming approach. LAP LAMBERT Academic Publishing, Germany.

[5]      Abou-El-Enien THM, El-Feky SF. (2015). BI-level, multi-level multiple criteria decision making and TOPSIS approach –theory, applications and software: A literature review (2005-2015). Global Journal of Advanced Research 2(7): 1179-1195. 

[6] Abou-El-Enien THM. (2015). An Interactive decomposition algorithm for two-level large scale linear multiobjective optimization problems with stochastic parameters using TOPSIS method. International Journal of Engineering Research and Applications 5(4) (Part-2): 61-76. 

[7] Abo–Sinna MA, Abou-El-Enien THM. (2005). An algorithm for solving large scale multiple objective decision making problems using TOPSIS approach. AMSE journals, Advances in Modelling and Analysis A 42(6): 31-48.

[8] Lai YJ, Liu TY, Hwang CL. (1994). TOPSIS for MODM. European Journal of Operational Research 76: 486-500.

[9]       Dempe S. (2002). Foundations of Bilevel Programming. Kluwer Academic Publishers, Boston.

[10] Migdalas A, Pardalos PM, Varbrand P. (1998). Multilevel Optimization: Algorithms and Applications. Kluwer Academic Publishers, Boston. 

[11] Caballero R, Cerda E, Munoz M, Rey L, Stancu-Minasian I. (2001). Efficient solution concepts and their relations in stochastic multiobjective programming. Journal of Optimization Theory and Applications 110(1): 53–74.

[12] BenAbdelaziz F. (2012). Solution approaches for the multiobjective stochastic programming. European Journal of Operational Research 216(1): 1-16. https://doi.org/10.1016/j.ejor.2011.03.033 

[13] Masmoudi M, BenAbdelaziz, F. (2018). Portfolio selection problem: a review of deterministic and stochastic multiple objective programming models. Annals of Operations Research 267(1): 335-352.

[14] Rao SS. (2009). Engineering Optimization, John Wiley & Sons, Inc., New York. 

[15] Conejo AJ, Castillo E, Minguez R, Garcia-Bertrand R. (2006). Decomposition Techniques in Mathematical Programming, Springer-Verlag, Germany, 

[16] Lasdon LS, Waren AD, Ratner MW. (1980). GRG2 User's Guide Technical Memorandum, University of Texas. 

[17] Lai YJ, Hwang CL. (1994). Fuzzy Multiple Objective Decision Making: Methods and Applications, Springer-Verlag, Heidelbeg. 

[18] Zimmermann HJ. (1996). Fuzzy Set Theory and Its Applications, Kluwer Academic Publishers, Boston.  

[19] Pramanik ST, Roy K. (2006). Fuzzy goal programming approach to multilevel programming problems. Euro. J. Oper. Res 176: 1151–1166.

[20] Sinha S. (2003). Fuzzy programming approach to multi-level programming problems. Fuzzy Sets and Syst. 136: 189–202.