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 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

scholarly journals Mixed Spatial and Temporal Decompositions for Large-Scale Multistage Stochastic Optimization Problems

2020 ◽  
Vol 186 (3) ◽  
pp. 985-1005
Author(s):  
Pierre Carpentier ◽  
Jean-Philippe Chancelier ◽  
Michel De Lara ◽  
François Pacaud
Keyword(s):  
Stochastic Optimization ◽  
Large Scale ◽  
Optimization Problems ◽  
Multistage Stochastic Optimization

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.

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

CORRELATIONS IN STOCHASTIC PROGRAMMING: A CASE FROM STOCHASTIC SERVICE NETWORK DESIGN

2007 ◽  
Vol 24 (02) ◽  
pp. 161-179 ◽  
Author(s):  
ARNT-GUNNAR LIUM ◽  
TEODOR GABRIEL CRAINIC ◽  
STEIN W. WALLACE
Keyword(s):  
Stochastic Programming ◽  
Stochastic Optimization ◽  
Network Design ◽  
Design Model ◽  
Optimization Models ◽  
Major Goal ◽  
Deterministic Models ◽  
Service Network ◽  
Service Network Design ◽  
Solution Structures

Deterministic models, even if used repeatedly, will not capture the essence of planning in an uncertain world. Flexibility and robustness can only be properly valued in models that use stochastics explicitly, such as stochastic optimization models. However, it may also be very important to capture how the random phenomena are related to one another. In this article we show how the solution to a stochastic service network design model depends heavily on the correlation structure among the random demands. The major goal of this paper is to discuss why this happens, and to provide insights into the effects of correlations on solution structures. We illustrate by an example.

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