Paper Info
Title
Efficient solution concepts and their application in uncertain multiobjective programming.
Abstract
Graphical abstractDisplay Omitted HighlightsThe model of uncertain multiobjective programming based on uncertainty theory is originally presented, and six concepts of efficient solutions are defined.The relations among the efficiency concepts are established under the assumed conditions.We apply the uncertain multiobjective optimization methods to a real-life problem, i.e., the uncertain multiobjective redundancy allocation problem.A modified multiobjective artificial bee colony (MOABC) algorithm is designed to generate Pareto efficient set to the UMRA problem. Based on uncertainty theory, we investigate the relations among efficiency concepts of the multiobjective programming (MOP) with uncertain vectors. We first propose the uncertain MOP model, and study its convexity. Then, we define different efficiency concepts such as expected-value efficiency, expected-value proper efficiency, and establish their relations under the assumed conditions, which are illustrated through two numerical examples. Finally, in the uncertain environment, we apply the theoretical results to a redundancy allocation problem with two objectives in reparable parallel-series systems, and discuss how to obtain different types of efficient solutions according to the decision-maker's preferences.
Year
DOI
Venue
2017
10.1016/j.asoc.2016.07.021
Appl. Soft Comput.
Keywords
Field
DocType
Uncertainty theory,Uncertain multiobjective programming,Pareto efficiency,Redundancy allocation problem
Mathematical optimization,Convexity,Multiobjective programming,Multi-objective optimization,Redundancy (engineering),Artificial intelligence,Pareto efficiency,Pareto principle,Machine learning,Mathematics,Uncertainty theory
Journal
Volume
Issue
ISSN
56
C
1568-4946
Citations 
PageRank 
References 
3
0.42
12
Authors
4
Name
Order
Citations
PageRank
Mingfa Zheng181.25
Yuan Yi2163.46
Zutong Wang3353.91
Tianjun Liao4553.00