Stochastic Optimization Models for Workforce Planning, Operations, and Risk Management

2018 ◽  
Vol 10 (1) ◽  
pp. 40-57 ◽  
Author(s):  
Michael J. Davis ◽  
Yingdong Lu ◽  
Mayank Sharma ◽  
Mark S. Squillante ◽  
Bo Zhang
Keyword(s):  
Risk Management ◽  
Stochastic Optimization ◽  
Workforce Planning ◽  
Optimization Models

Some matheuristic algorithms for multistage stochastic optimization models with endogenous uncertainty and risk management

2020 ◽  
Vol 285 (3) ◽  
pp. 988-1001 ◽  
Author(s):  
Laureano F. Escudero ◽  
M. Araceli Garín ◽  
Juan F. Monge ◽  
Aitziber Unzueta
Keyword(s):  
Risk Management ◽  
Stochastic Optimization ◽  
Optimization Models ◽  
Endogenous Uncertainty ◽  
Multistage Stochastic Optimization

scholarly journals New Developments in Uncertainty: Linking Risk Management, Reliability, Statistics and Stochastic Optimization

2014 ◽  
Author(s):  
Stan Uryasev ◽  
Tyrrel R. Rockafellar
Keyword(s):  
Risk Management ◽  
Stochastic Optimization ◽  
New Developments

Stochastic optimization models for a bike-sharing problem with transshipment

2019 ◽  
Vol 276 (1) ◽  
pp. 272-283 ◽  
Author(s):  
Francesca Maggioni ◽  
Matteo Cagnolari ◽  
Luca Bertazzi ◽  
Stein W. Wallace
Keyword(s):  
Stochastic Optimization ◽  
Optimization Models ◽  
Bike Sharing

A multi-stage stochastic optimization model for energy systems planning and risk management

2016 ◽  
Vol 110 ◽  
pp. 49-56 ◽  
Author(s):  
Emilio L. Cano ◽  
Javier M. Moguerza ◽  
Antonio Alonso-Ayuso
Keyword(s):  
Risk Management ◽  
Stochastic Optimization ◽  
Optimization Model ◽  
Energy Systems ◽  
Systems Planning ◽  
Multi Stage

scholarly journals Operation Optimization of Natural Gas Transmission Pipelines Based on Stochastic Optimization Algorithms: A Review

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Xia Wu ◽  
Changjun Li ◽  
Yufa He ◽  
Wenlong Jia
Keyword(s):  
Natural Gas ◽  
Stochastic Optimization ◽  
Optimization Model ◽  
Optimization Algorithms ◽  
Optimization Models ◽  
Stochastic Algorithms ◽  
Operation Optimization ◽  
Natural Gas Pipelines ◽  
Computation Efficiency ◽  
Stochastic Optimization Algorithms

Operation optimization of natural gas pipelines has received increasing attentions, due to such advantages as maximizing the operating economic benefit and the gas delivery amount. This paper provides a review on the most relevant research progress related to the steady-state operation optimization models of natural gas pipelines as well as corresponding solution methods based on stochastic optimization algorithms. The existing operation optimization model of the natural gas pipeline is a mixed-integer nonlinear programming (MINLP) model involving a nonconvex feasible region and mixing of continuous, discrete, and integer optimization variables, which represents an extremely difficult problem to be solved by use of optimization algorithms. A survey on the state of the art demonstrates that many stochastic algorithms show better performance of solving such optimization models due to their advantages of handling discrete variables and of high computation efficiency over classical deterministic optimization algorithms. The essential progress mainly with regard to the applications of the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Simulated Annealing (SA) algorithms, and their extensions is summarized. The performances of these algorithms are compared in terms of the quality of optimization results and the computation efficiency. Furthermore, the research challenges of improving the optimization model, enhancing the stochastic algorithms, developing an online optimization technology, researching the transient optimization, and studying operation optimization of the integrated energy network are discussed.

A Distance For Multistage Stochastic Optimization Models

2012 ◽  
Vol 22 (1) ◽  
pp. 1-23 ◽  
Author(s):  
Georg Ch. Pflug ◽  
Alois Pichler
Keyword(s):  
Stochastic Optimization ◽  
Optimization Models ◽  
Multistage Stochastic Optimization

Stochastic Optimization Models for Cancer Chemotherapy

2014 ◽  
Vol 4 (28) ◽  
pp. 4097-4108 ◽  
Author(s):  
P. Rao ◽  
J. Jayabharathiraj ◽  
R. Vardhan
Keyword(s):  
Stochastic Optimization ◽  
Cancer Chemotherapy ◽  
Optimization Models
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