Main Article Content

Abstract

The relationship between the reference-uncooperative linear bilevel two-follower decision making and the multi-objective decision making has been recently considered (Sadeghi and Moslemi, 2019). In this paper, we address the foregoing relation for the


uncooperative linear bilevel multi-follower programming (ULBMFP) model with  followers. Furthermore, we consider some geometric properties of the feasible solutions set of the ULBMFP problem. Moreover an algorithm to find an optimal solution for the ULBMFP problem was proposed. Ultimately, some numerical examples to illustrate the proposed algorithm were provided.

Keywords

Biloevel programming Efficient set Multi-follower programming Multi-objective programming

Article Details

Author Biography

Fatemeh Moslemi, Department of Mathematics, Faculty of Mathematical Sciences and Computer, Shahid Chamran University of Ahvaz, Ahvaz, Iran

Department of Mathematics, Faculty of Mathematical Sciences and Computer, Shahid Chamran University of Ahvaz, Ahvaz, Iran

How to Cite
Moslemi, F., & Sadeghi, H. (2022). On solving uncooperative linear bilevel multi-follower programming problems. Pakistan Journal of Statistics and Operation Research, 18(1), 1-12. https://doi.org/10.18187/pjsor.v18i1.3261

References

  1. Alipour, M., Zare, K., & Seyedi, H. (2018). A Multi follower Bi-level Stochastic Programming Approach for Energy Management of Combined Heat and Power Micro-grids. Energy, 149, 135-146. DOI: https://doi.org/10.1016/j.energy.2018.02.013
  2. Bard, J. F. (1998). Practical Bilevel Optimization. Dordrecht: Kluwer Academic. DOI: https://doi.org/10.1007/978-1-4757-2836-1
  3. Bazaraa, M. S., Jarvis, J. J., & Sherali, H. D. (1977). Linear Programming and Network Flows. New York: Wiley.
  4. Bazaraa, M. S., Sherali, H. D., & Shetty, C. M. (2006). Nonlinear Programming, Theory and Algorithms. New York: Wiley. DOI: https://doi.org/10.1002/0471787779
  5. Benson, H. P. (1984a). Optimization over the efficient set. Journal of Mathematical Analysis and Applications, 98, 562-580.
  6. Benson, H. P. (1984b). Optimization over the efficient set. Journal of Mathematical Analysis And Applications, 98, 562-580. DOI: https://doi.org/10.1016/0022-247X(84)90269-5
  7. Calvete, H. I., & Galé, C. (2007). Linear bilevel multi-follower programming with independent followers. Journal of Global Optimization, 39, 409-417. DOI: https://doi.org/10.1007/s10898-007-9144-2
  8. Dempe, S. (2003). Foundations of Bilevel Programming. Dordrecht: Kluwer Academic.
  9. Ehrgott, M. (2005). Multicriteria Optimization. Berlin, Germany: Springer-Verlag.
  10. Faísca, N. P., Saraiva, P. M., Rustem, B., & Pistikopoulos, E. N. (2007). A multi-parametric programming approach for multilevel hierarchical and decentralized optimization problems. Computer Management Sciences, 6, 377-397. DOI: https://doi.org/10.1007/s10287-007-0062-z
  11. Fülöp, J. (1993). On the equivalency between a linear bilevel programming problem and linear optimization over the efficient set. Technical Report WP 93-1, Laboratory of Operations Research and Decision Systems, Computer and Automation Institute, Hungarian Academy of Sciences.
  12. Glackin, J., Ecker, J., & Kupferschmid, M. (2009). Solving bilevel linear programs using multiple objective linear programming. Journal of Optimization Theory and Applications, 140(2), 197-212. DOI: https://doi.org/10.1007/s10957-008-9467-2
  13. Horst, R., Thoai, N. V., Yamamoto, Y., & Zenke, D. (2007). On optimization over the efficient set in linear multicriteria programming. Journal of Optimization Theory and Applications, 134, 433-443. DOI: https://doi.org/10.1007/s10957-007-9219-8
  14. Jorge, J. (2005). A bilinear algorithm for optimizing a linear function over the efficient set of a multiple objective linear programming problem. Journal of Global Optimization, 31, 1-16. DOI: https://doi.org/10.1007/s10898-003-3784-7
  15. Lu, J., Shi, C., & Zhang, G. (2006). On bilevel multi-follower decision making: General framework and solutions. Information Sciences, 176, 1607-1627. DOI: https://doi.org/10.1016/j.ins.2005.04.010
  16. Lu, J., Shi, C., Zhang, G., & Dillon, T. (2007a). Model and extended Kuhn-Tucker approach for bilevel multi-follower decision making in a referential-uncooperative situation. International Journal of Global Optimization, 38, 597-608.
  17. Lu, J., Shi, C., Zhang, G., & Dillon, T. (2007b). Model and Extended Kuhn-Tuker Approach for Bilevel Multi-follower Decision Making in a Refrential-Uncoopeartive Situation. Journal of Global Optimization, 38, 597-608. DOI: https://doi.org/10.1007/s10898-006-9098-9
  18. Ma, W., Wang, M., & Zhu, X. (2014). Improved particle swarm optimization based approach for bilevel programming problem-an application on supply chain model. International Journal of Machine Learning and Cyber, 5, 281-292. DOI: https://doi.org/10.1007/s13042-013-0167-3
  19. Metev, B. (2007). Multiobjective optimization methods help to minimize a function over the efficient set. Cybernetics and Information Technologies, 7, 22-28.
  20. Miao, C., Du, G., & Zandhang, R. J. J. T. (2017). Coordinated optimization of platform-driven product line planning by bilevel programming. International Journal of Production Research, 55, 3808-3831. DOI: https://doi.org/10.1080/00207543.2017.1294770
  21. Pieume, C. O., Fotso, L. P., & Siarry, P. (2008). An approach for finding efficient points in multiobjective linear programming. Journal of Information and Optimization Science, 29, 203-216. DOI: https://doi.org/10.1080/02522667.2008.10699800
  22. Pieume, C. O., Fotso, L. P., & Siarry, P. (2009). Solving bilevel programming problems with multicriteria optimization techniques. Opsearch, 42, 169-183. DOI: https://doi.org/10.1007/s12597-009-0011-4
  23. Sadeghi, H., & Moslemi, F. (2019). A multiple objective programming approach to linear bilevel multi-follower programming. AIMS Mathematics, 4, 763-778. DOI: https://doi.org/10.3934/math.2019.3.763
  24. Safaei, A. S., Farsad, S., & Paydar, M. M. (2018). Robust bi-level optimization of relief logistics operation. Applied Mathematical Modelling, 56, 359-380. DOI: https://doi.org/10.1016/j.apm.2017.12.003
  25. Sayin, S. (1996). An algorithm based on facial decomposition for finding the efficient set in multiple objective linear programming. Operations Research Letters, 19, 87-94. DOI: https://doi.org/10.1016/0167-6377(95)00046-1
  26. Shi, C., Zhang, G., & Lu, J. (2005). A Kth-best Approach for Linear Bilevel Multi-follower Programming. Journal of Global Optimization, 33, 563-578. DOI: https://doi.org/10.1007/s10898-004-7739-4
  27. Steure, R. (1986). Multiple Criteria Optimization: Theory, Computation, and Application. New York: Wiley.
  28. Thery, R., & Zarate, P. (2009). Energy planning: a multi-level and multicriteria decision making structure proposal. Central European Journal of Operations Research, 17, 265-274. DOI: https://doi.org/10.1007/s10100-009-0091-5
  29. Wen, U. P., & Hsu, S. T. (1991). Efficient solutions for the linear bilevel programming problem. European Journal of Operational Research, 62, 354-362. DOI: https://doi.org/10.1016/0377-2217(92)90124-R
  30. Zhang, G., Lu, J., & Gao, Y. (2016). Multi-Level Decision Making: Models, Methods and Applications. AN: Springer-Verlag Berlin.