Some experience in applying a stochastic method to location problems

1986 ◽  
pp. 229-232 ◽  
Author(s):  
P. M. Camerini ◽  
A. Colorni ◽  
F. Maffioli
Keyword(s):  
Stochastic Method ◽  
Location Problems

IDENTIFICATION OF WIEBE AND WOSCHNI PARAMETERS USING STOCHASTIC METHOD OF DIFFERENTIAL EVOLUTION

2020 ◽  
Author(s):  
Lucas Peixoto ◽  
Stephan Hennings Och ◽  
Carlos augusto Henning Laurindo ◽  
Paulo Philippi ◽  
Luís Mauro Moura
Keyword(s):  
Differential Evolution ◽  
Stochastic Method

Nappes d'huile: prediction de leurs deplacements comme agent efficace d'un plan de mesure d'urgence

1979 ◽  
Vol 14 (1) ◽  
pp. 89-109
Author(s):  
B. Coupal ◽  
M. de Broissia
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Finite Elements ◽  
Mass Balance ◽  
Stochastic Method ◽  
Stochastic Methods ◽  
Deterministic Approach ◽  
First Order ◽  
The Third ◽  
Comme Agent

Abstract The movement of oil slicks on open waters has been predicted, using both deterministic and stochastic methods. The first method, named slick rose, consists in locating an area specifying the position of the slick during the first hours after the spill. The second method combines a deterministic approach for the simulation of current parameters to a stochastic method simulating the wind parameters. A Markov chain of the first order followed by a Monte Carlo approach enables the simulation of both phenomena. The third method presented in this paper describes a mass balance on the spilt oil, solved by the method of finite elements. The three methods are complementary to each other and constitute an important point for a contingency plan.

Minmax Regret Maximal Covering Location Problems with Edge Demands

2020 ◽  
pp. 105181
Author(s):  
Marta Baldomero-Naranjo ◽  
Jörg Kalcsics ◽  
Antonio M. Rodríguez-Chía
Keyword(s):  
Location Problems ◽  
Maximal Covering ◽  
Minmax Regret
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