Decomposition Approach

2012 ◽  
pp. 45-60
Author(s):  
Svenja Lagershausen
Keyword(s):  
Decomposition Approach

Parametrical Synthesis of Linear Controllers in Aperiodical Systems on Basis of Decomposition Approach

2019 ◽  
Vol 12 (4) ◽  
pp. 192
Author(s):  
Sergey Anatolevich Gayvoronskiy ◽  
Tatiana Ezangina ◽  
Maxim Pushkarev ◽  
Ivan Khozhaev
Keyword(s):  
Decomposition Approach ◽  
Linear Controllers

An improved decomposition approach toward energy rebound effects in China: Review since 1992

2021 ◽  
Vol 145 ◽  
pp. 111141
Author(s):  
Jiandong Chen ◽  
Ming Gao ◽  
Muhammad Shahbaz ◽  
Shulei Cheng ◽  
Malin Song
Keyword(s):  
Decomposition Approach ◽  
Rebound Effects

scholarly journals An analytics-based heuristic decomposition of a bilevel multiple-follower cutting stock problem

OR Spectrum ◽  
2021 ◽  
Author(s):  
Adejuyigbe O. Fajemisin ◽  
Laura Climent ◽  
Steven D. Prestwich
Keyword(s):  
Real Life ◽  
Forest Harvesting ◽  
Cutting Stock Problem ◽  
Cutting Stock ◽  
Decomposition Approach ◽  
New Class ◽  
Bilevel Problem ◽  
Programming Techniques ◽  
Bilevel Problems ◽  
Better Than

AbstractThis paper presents a new class of multiple-follower bilevel problems and a heuristic approach to solving them. In this new class of problems, the followers may be nonlinear, do not share constraints or variables, and are at most weakly constrained. This allows the leader variables to be partitioned among the followers. We show that current approaches for solving multiple-follower problems are unsuitable for our new class of problems and instead we propose a novel analytics-based heuristic decomposition approach. This approach uses Monte Carlo simulation and k-medoids clustering to reduce the bilevel problem to a single level, which can then be solved using integer programming techniques. The examples presented show that our approach produces better solutions and scales up better than the other approaches in the literature. Furthermore, for large problems, we combine our approach with the use of self-organising maps in place of k-medoids clustering, which significantly reduces the clustering times. Finally, we apply our approach to a real-life cutting stock problem. Here a forest harvesting problem is reformulated as a multiple-follower bilevel problem and solved using our approach.

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