Abstract
Uncertain network optimization is the study of network optimization with uncertain data which we often meet in decision making under the presence of uncertainties. The main purpose of this manuscript is to present a state-of-the-art review on the recent advances in uncertain network optimization and to show the general uncertain network optimization models based on an uncertainty theory. Some classical network optimization topics in an uncertain environment are revisited, and some challenging topics in future research are addressed in the field of uncertain network optimization. This paper divides uncertain network optimization into two main directions. One line of research deals with network optimization problems with an uncertain topology structure, and another line of research concerns the handling of network optimization with uncertain weights.
Highlights
In real life, we are faced with so many networks such as road network, telecommunication network, television network, computer network, logistics network, social network, and so on
Concluding remarks The uncertain network optimization problem arises from various applications in real life, which is of both theoretical interest in mathematics and applied aspect in practice
Due to the existence of uncertainty almost everywhere and almost every time, uncertain network optimization will be widely applied in such various disciplines
Summary
We are faced with so many networks such as road network, telecommunication network, television network, computer network, logistics network, social network, and so on. Preliminary As an efficient tool of modeling the behavior of uncertain phenomena, the uncertainty theory is employed to deal with uncertain network optimization problems. An uncertain variable is defined by Liu [21] as a measurable function from an uncertainty space to the set of real numbers. Liu [58] provided the following useful theorem to determine the distribution function of the strictly increasing function of uncertain variables. Taking advantage of this theorem, we can transform an indeterminacy model into a deterministic one. (Liu [58]) Let ξ1, ξ2, · · · , ξn be independent uncertain variables with uncertainty distributions 1, 2, · · · , n, respectively.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
