Non-smooth Optimality for Robust Multi-objective Optimization Problems
Subject Areas :
Statistics
Maryam Saadati
1
,
Morteza Oveisiha
2
1 - Department of Pure Mathematics, Faculty of Science, Imam Khomeini International University, Qazvin, Iran
2 - Department of Pure Mathematics, Faculty of Science, Imam Khomeini International University, Qazvin, Iran
Received: 2021-08-10
Accepted : 2022-04-27
Published : 2022-10-23
Keywords:
جوابهای کارای استوار ضعیف,
زیردیفرانسیل حدی,
بهینه سازی چندهدفه استوار غیرهموار,
تحدب تعمیم یافته,
شرایط بهینگی,
Abstract :
This article is concerned with non-smooth/nonconvex robust multi-objective optimization problems involving uncertain inequality and equality constraints. Employing some advanced tools of variational analysis such as the approximate extremal principle and the weak fuzzy sum rule for the Frechet subdifferential, we first drive a fuzzy necessary optimality condition of a non-smooth/nonconvex multi-objective optimization problem without any constrained qualification in the sense of the Frechet subdifferential. Then by exploiting the obtained fuzzy optimality condition, the non-smooth version of Fermat’s rule and formulae for the limiting subdifferential of an infinite family of non-smooth functions, we establish a necessary optimality condition in terms of the limiting subdifferential for weakly robust efficient solutions of the reference problem. Further,we present an example to illustrate this condition for an uncertain multi-objective optimization problem involving equality and inequality constraints.Finally sufficient conditions for weakly robust efficient solutions and robust efficient solutions of the problems are provided by presenting new concepts of generalized convexity.
References:
Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57.
Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega, 64, 126-130.
Beemsterboer, D. J. C., Hendrix, E. M. T., & Claassen, G. D. H. (2018). On solving the best-worst method in multi-criteria decision-making. IFAC-PapersOnLine, 51(11), 1660-1665.
Beale, E. M. L., & Tomlin, J. A. (1970). Special facilities in a general mathematical programming system for non-convex problems using ordered sets of variables. OR, 69(447-454), 99.
Aboutorab, H., Saberi, M., Asadabadi, M. R., Hussain, O., & Chang, E. (2018). ZBWM: The Z-number extension of Best Worst Method and its application for supplier development. Expert Systems with Applications, 107, 115-125.
Liang, F., Brunelli, M., & Rezaei, J. (2019). Consistency issues in the best worst method: Measurements and thresholds. Omega, 102175.
Mohammadi, M., & Rezaei, J. (2019). Bayesian best-worst method: A probabilistic group decision making model. Omega, 102075
AMIRI, M., & EMAMAT, M. S. M. M. (2020). A Goal Programming Model for BWM. INFORMATICA, 31(1), 21-34.
Vafadarnikjoo, A., Tavana, M., Botelho, T., & Chalvatzis, K. (2020). A neutrosophic enhanced best–worst method for considering decision-makers’ confidence in the best and worst criteria. Annals of Operations Research, 1-28.
Leyffer, S., Sartenaer, A., & Wanufelle, E. (2008). Branch-and-refine for mixed-integer nonconvex global optimization. Preprint ANL/MCS-P1547-0908, Mathematics and Computer Science Division, Argonne National Laboratory, 39, 40-78.
انصاری, محمدرضا, حسنیفرد, فاطمه. (1396). حل یک مسئله بهینهسازی غیرخطی، عدد صحیح و غیرمحدب با استفاده از روشهای محدبسازی مبتنی بر مجموعه منظم خاص. فصلنامه سیستمهای مختلط و غیرخطی, 1(1), 71-85.
Kang, C., Guo, M., & Wang, J. (2017). Short-term hydrothermal scheduling using a two-stage linear programming with special ordered sets method. Water Resources Management, 31(11), 3329-3341.
Hua, H., Hovestadt, L., Tang, P., & Li, B. (2019). Integer programming for urban design. European Journal of Operational Research, 274(3), 1125-1137.
Huchette, J., & Vielma, J. P. (2019). A combinatorial approach for small and strong formulations of disjunctive constraints. Mathematics of Operations Research, 44(3), 793-820.
Epelle, E. I., & Gerogiorgis, D. I. (2020). A Computational Performance Comparison of MILP vs. MINLP Formulations for Oil Production Computers & Chemical Engineering, 106903.
Akbari-Dibavar, A.,Mohammadi-Ivatloo, B., & Zare, K. (2020). Optimal stochastic bilevel scheduling of pumped hydro storage systems in a pay-as-bid energy market environment. Journal of Energy Storage, 31, 101608.
MirHassani, S. A., &Hooshmand, F. (2019). Methods and Models in Mathematical Programming. Springer International Publishing.