Market-oriented transmission expansion planning using non-linear programming and multi-criteria data envelopment analysis

2019 ◽  
Vol 19 ◽  
pp. 100234 ◽  
Author(s):  
Vinod Kumar Yadav ◽  
Kanwardeep Singh ◽  
Shubham Gupta
Keyword(s):  
Linear Programming ◽  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Non Linear Programming ◽  
Expansion Planning ◽  
Transmission Expansion Planning ◽  
Transmission Expansion ◽  
Non Linear

scholarly journals An Enhanced Genetic Algorithm to Solve the Static and Multistage Transmission Network Expansion Planning

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Luis A. Gallego ◽  
Marcos J. Rider ◽  
Marina Lavorato ◽  
Antonio Paldilha-Feltrin
Keyword(s):  
Genetic Algorithm ◽  
Linear Programming ◽  
Heuristic Algorithms ◽  
Initial Population ◽  
Expansion Planning ◽  
Transmission Expansion Planning ◽  
Transmission Expansion ◽  
Local Improvement ◽  
Linear Programming Problems

An enhanced genetic algorithm (EGA) is applied to solve the long-term transmission expansion planning (LTTEP) problem. The following characteristics of the proposed EGA to solve the static and multistage LTTEP problem are presented, (1) generation of an initial population using fast, efficient heuristic algorithms, (2) better implementation of the local improvement phase and (3) efficient solution of linear programming problems (LPs). Critical comparative analysis is made between the proposed genetic algorithm and traditional genetic algorithms. Results using some known systems show that the proposed EGA presented higher efficiency in solving the static and multistage LTTEP problem, solving a smaller number of linear programming problems to find the optimal solutions and thus finding a better solution to the multistage LTTEP problem.

Data Envelopment Analysis Development in Banking Sector

2018 ◽  
pp. 462-484
Author(s):  
Sepideh Kaffash ◽  
Mehran Torshizi
Keyword(s):  
Linear Programming ◽  
Data Envelopment Analysis ◽  
Banking Sector ◽  
Programming Model ◽  
Real Life ◽  
Relative Performance ◽  
Data Envelopment ◽  
Non Linear Programming ◽  
Dea Models ◽  
The Subject

Data Envelopment Analysis is a non-linear programming model introduced by Charnes, Cooper and Rhodes in 1978. It is used widely in literature to measure the relative performance of units in several various fields including the banking. The fascinating real life cases and problems needed to be solved, the nature of data and the types of indicators in the banking field makes it one the most popular fields for DEA researchers theoretically and empirically. DEA and its applications have been the subject of several reviews. However, in this paper the authors specifically review the classic and new DEA models and the applications of them in the banking field.

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