scholarly journals A Dividing Rectangles Algorithm for Stochastic Simulation Optimization

2015 ◽  
pp. 47-61 ◽  
Author(s):  
Paul Nicholas
Keyword(s):  
Stochastic Simulation ◽  
Simulation Optimization

scholarly journals Balancing Search and Estimation in Random Search Based Stochastic Simulation Optimization

2016 ◽  
Vol 61 (11) ◽  
pp. 3593-3598 ◽  
Author(s):  
Chenbo Zhu ◽  
Jie Xu ◽  
Chun-Hung Chen ◽  
Loo Hay Lee ◽  
Jian-Qiang Hu
Keyword(s):  
Stochastic Simulation ◽  
Simulation Optimization ◽  
Random Search

A transient stochastic simulation–optimization model for operational fuel planning in-theater

2018 ◽  
Vol 264 (2) ◽  
pp. 637-652 ◽  
Author(s):  
Benjamin J. Lobo ◽  
Donald E. Brown ◽  
Matthew S. Gerber ◽  
Peter J. Grazaitis
Keyword(s):  
Stochastic Simulation ◽  
Optimization Model ◽  
Simulation Optimization

Stochastic Simulation Optimization for Route Selection Strategy Based on Flight Delay Cost

2018 ◽  
Vol 35 (06) ◽  
pp. 1850045
Author(s):  
Yong Tian ◽  
Bojia Ye ◽  
Marc Sáez Estupiñá ◽  
Lili Wan
Keyword(s):  
Stochastic Simulation ◽  
Simulation Optimization ◽  
Optimization Technique ◽  
Civil Aviation ◽  
Selection Strategy ◽  
Route Selection ◽  
Full Information ◽  
Adverse Weather ◽  
Delay Cost ◽  
Flight Delay

The continuous and strong growth of the civil aviation in the world combined with the severe adverse weather problem have made necessary the collaboration between the different civil aviation agents to improve the management of the capacity-demand imbalances in the airspace. In this paper, we consider a stochastic simulation optimization problem for air route selection strategy based on flight delay cost. The problem takes consideration of airspace capacity and demand uncertainty, three strategies, including collaborative reroute strategy (CRS), full information reroute strategy (FIRS) and hybrid stated route preference strategy (HSR), are employed to mitigate the flight delay cost. To find the best strategy, a discrete event simulation model is built by Arena Software, and the Monte Carlo method combined with the OCBA simulation optimization technique is employed for assessing a common severe convective weather scenario in the Central and Southern China airspace. Simulation results imply that HSR schemes show better system-wide performance than CRS and FIRS, these benefits are supposed to come from the batch allocations method. Although the airline can receive full information in advance, FIRS does not show obvious advantage in reducing the total airborne waiting time than CRS. For the system-wide performance FIRS is better than CRS, but not as good as HSR.

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