Discrete Convex Simulation Optimization

2020 ◽  
Author(s):  
Haixiang Zhang ◽  
Zeyu Zheng
Keyword(s):  
Simulation Optimization ◽  
Discrete Convex

Stochastic Localization Methods for Discrete Convex Simulation Optimization

2020 ◽  
Author(s):  
Haixiang Zhang ◽  
Zeyu Zheng ◽  
Javad Lavaei
Keyword(s):  
Simulation Optimization ◽  
Discrete Convex

Convergence of a Single Run Simulation Optimization Algorithm

1988 ◽  
Author(s):  
Ying Tat Leung ◽  
Rajan Suri
Keyword(s):  
Optimization Algorithm ◽  
Simulation Optimization

Simulation Optimization: A Review on Theory and Applications

2013 ◽  
Vol 39 (11) ◽  
pp. 1957 ◽  
Author(s):  
Long-Fei WANG ◽  
Le-Yuan SHI
Keyword(s):  
Simulation Optimization

Discrete Convex Analysis and Its Applications in Operations: A Survey

2019 ◽  
Author(s):  
Xin Chen ◽  
Menglong Li
Keyword(s):  
Convex Analysis ◽  
Discrete Convex Analysis ◽  
Discrete Convex

Evaluation of bi-Pass for Parallel Simulation Optimization

2020 ◽  
Author(s):  
Linda Pei ◽  
Barry L. Nelson ◽  
Susan R. Hunter
Keyword(s):  
Simulation Optimization ◽  
Parallel Simulation

Aircraft Assembly Ramp-Up Planning Using a Hybrid Simulation-Optimization Approach

2020 ◽  
Author(s):  
Amos H.C. Ng ◽  
Jacob Bernedixen ◽  
Martin Andersson ◽  
Sunith Bandaru ◽  
Thomas Lezama
Keyword(s):  
Simulation Optimization ◽  
Hybrid Simulation ◽  
Optimization Approach ◽  
Aircraft Assembly

scholarly journals Simulation optimization research of anti-freezing heat pipe for preheating fresh air in wellbore

2021 ◽  
Vol 647 ◽  
pp. 012093
Author(s):  
Yaping Zhang ◽  
Fangfang Qu ◽  
Yao Chen ◽  
Zhiyin He ◽  
Haochen Jiang ◽  
...  
Keyword(s):  
Heat Pipe ◽  
Simulation Optimization

scholarly journals Combined Aggregated Sampling Stochastic Dynamic Programming and Simulation-Optimization to Derive Operation Rules for Large-Scale Hydropower System

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 625
Author(s):  
Xinyu Wu ◽  
Rui Guo ◽  
Xilong Cheng ◽  
Chuntian Cheng
Keyword(s):  
Dynamic Programming ◽  
Stochastic Dynamic Programming ◽  
Large Scale ◽  
Simulation Optimization ◽  
Optimal Operation ◽  
Stochastic Dynamic ◽  
Operation Model ◽  
Two Stage ◽  
Operation Rules ◽  
Reservoir Systems

Simulation-optimization methods are often used to derive operation rules for large-scale hydropower reservoir systems. The solution of the simulation-optimization models is complex and time-consuming, for many interconnected variables need to be optimized, and the objective functions need to be computed through simulation in many periods. Since global solutions are seldom obtained, the initial solutions are important to the solution quality. In this paper, a two-stage method is proposed to derive operation rules for large-scale hydropower systems. In the first stage, the optimal operation model is simplified and solved using sampling stochastic dynamic programming (SSDP). In the second stage, the optimal operation model is solved by using a genetic algorithm, taking the SSDP solution as an individual in the initial population. The proposed method is applied to a hydropower system in Southwest China, composed of cascaded reservoir systems of Hongshui River, Lancang River, and Wu River. The numerical result shows that the two-stage method can significantly improve the solution in an acceptable solution time.

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