Fuzzy Bi-level Decision-Making Techniques: A Survey

Zhang, Guangquan; Han, Junqiang; Lu, Jie

doi:10.1080/18756891.2016.1180816

Cited by 23 publications

(8 citation statements)

References 62 publications

(45 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The bilevel programming model is a system optimization model with a two-tier hierarchical structure. The upper and lower levels have their own objective functions and constraints [28,29]. The objective functions and constraints of the upper-level problem are not only related to the upper decision variables but also depending on the optimal solution of the lower level problem, while the optimal solution of the lower level problem is affected by the upper decision variables.…”

Section: Traffic Signal Timing Bi-level Programming Modelmentioning

confidence: 99%

Research on Phase Combination and Signal Timing Based on Improved K-Medoids Algorithm for Intersection Signal Control

Shen

Zhu

et al. 2020

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Aiming at the problem of intersection signal control, a method of traffic phase combination and signal timing optimization based on the improved K-medoids algorithm is proposed. Firstly, the improvement of the traditional K-medoids algorithm embodies in two aspects, namely, the selection of the initial medoids and the parameter k, which will be applied to the cluster analysis of historical saturation data. The algorithm determines the initial medoids based on a set of probabilities calculated from the distance and determines the number of clusters k based on an exponential function, weight adjustment, and elbow ideas. Secondly, a phase combination model is established based on the saturation and green split data, and the signal timing is optimized through a bilevel programming model. Finally, the algorithm is evaluated over a certain intersection in Hangzhou, and results show that this algorithm can reduce the average vehicle delay and queue length and improve the traffic capacity of the intersection in the peak hour.

show abstract

Section: Traffic Signal Timing Bi-level Programming Modelmentioning

confidence: 99%

Research on Phase Combination and Signal Timing Based on Improved K-Medoids Algorithm for Intersection Signal Control

Shen

Zhu

et al. 2020

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…Since problem P0 is equivalent to problem P2, a bi-level optimization problem in which the upper level is problem P1 and the lower level is problem P2 is obtained. From literature, 34,35 it can be seen that even the bi-level liner optimization problem is hard to solve. And the most effective method for solving such problem is to transform it into a single-level problem.…”

Section: Definitionmentioning

confidence: 99%

A multi-user multi-operator computing pricing method for Internet of things based on bi-level optimization

Jiang

Wang

et al. 2020

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

The Internet of mobile things is a promising paradigm that generates, stores, and processes amount of real-time data to render rich services for mobile users. Along with the increase of mobile devices in the field of Internet of things, more and more intelligent applications, such as face recognition and virtual reality, have emerged. These applications typically consume large amounts of computing and energy resources. However, due to the physical size limitations of Internet of things terminals, their computing capacity and power are limited, where users’ needs for application processing delay and power consumption cannot be met. Therefore, the concept of edge cloud computing has been proposed, which enhances the computing capacity of Internet of things terminals by offloading user tasks to edge servers for computation. When there are multiple operators, it is important to understand how users choose an operator to perform computation and how operators can reasonably price the computing capacity to meet their own interests. Therefore, we study the computation pricing and user decision-making problems of Internet of things under multi-user and multi-operator scenarios. The problem is divided into three phases and modeled as a two-level optimization problem. While an operator’s goal is to minimize the loss of his interests, the user’s goal is to minimize the computation cost (energy consumption and price). First, since the lower-level user decision-making problem is an integer linear programming problem, we transform it into an equivalent continuous linear programming problem by relaxation. Second, we transform the bi-level optimization problem into an equivalent single-level optimization problem by substituting the lower problem’s Karush–Kuhn–Tucker conditions into an upper problem. Finally, we use a spatial branch and bound algorithm to solve the problem. Experimental results show that the proposed algorithm can effectively maintain the benefits of both operators and users in the field of Internet of things.

show abstract

“…Multi-objective programming is an optimization problem characterized by a set of objective functions and a set of well-defined constraints to be satisfied [8][9][10]. The goal of multi-objective *Corresponding author.…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Multi-objective Programming Model to Improve the Reliability of Solution

Jin¹,

Li²,

Feng³

et al. 2020

IJCIS

View full text Add to dashboard Cite

Randomness is a common uncertainty encountered in practical multi-objectives decision-making. But it is always a challenge for decision-makers to process randomness in multi-objective programming problems. This paper takes the decision-making objectives as fuzzy events and aims to solve numerical multi-objective programming problems under random environment. We first analyze the effects of randomness on multi-objective decision-making results. With the expectation value and the probability of fuzzy events as quantitative index of randomness, we then establish a two-stage random multi-objective programming model based on reliability (i.e., TS-MOPM). Specifically, we give several probability calculation methods of fuzzy events with common distributions, and further present the corresponding calculation procedures for solving TS-MOPM. Finally, a case study is implemented to test the proposed model TS-MOPM. Theoretical analysis and case study indicate that our model has better interpretability and operability. The research results enrich the existing random multi-objective programming methods to some extent.

show abstract

Fuzzy Bi-level Decision-Making Techniques: A Survey

Cited by 23 publications

References 62 publications

Research on Phase Combination and Signal Timing Based on Improved K-Medoids Algorithm for Intersection Signal Control

Research on Phase Combination and Signal Timing Based on Improved K-Medoids Algorithm for Intersection Signal Control

A multi-user multi-operator computing pricing method for Internet of things based on bi-level optimization

A Two-Stage Multi-objective Programming Model to Improve the Reliability of Solution

Contact Info

Product

Resources

About