A novel algorithm for generating Pareto frontier of bi-level multi-objective rough nonlinear programming problem

This paper discusses a new algorithm for generating the Pareto frontier for bi-level multi-objective rough nonlinear programming problem (BL-MRNPP). In this algorithm, the uncertainty exists in constraints which are modeled as a rough set. Initially, BL-MRNPP is transformed into four deterministic m...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Ain Shams Engineering Journal Jg. 12; H. 2; S. 2125 - 2133
Hauptverfasser: Elsisy, M.A., El Sayed, M.A., Abo-Elnaga, Y.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.06.2021
Elsevier
Schlagworte:
Bi-level programming
KKT optimality
Multi-objective programming
Rough set
Multi-objective programming
Bi-level programming
KKT optimality
Rough set
ISSN:2090-4479
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:This paper discusses a new algorithm for generating the Pareto frontier for bi-level multi-objective rough nonlinear programming problem (BL-MRNPP). In this algorithm, the uncertainty exists in constraints which are modeled as a rough set. Initially, BL-MRNPP is transformed into four deterministic models. The weighted method and the Karush-Kuhn-Tucker optimality condition are combined to obtain the Pareto front of each model. The nature of the problem solutions is characterized according to newly proposed definitions. The location of efficient solutions depending on the lower/upper approximation set is discussed. The aim of the proposed solution procedure for the BL-MRNPP is to avoid solving four problems. A numerical example is solved to indicate the applicability of the proposed algorithm.
ISSN:2090-4479
DOI:10.1016/j.asej.2020.11.006