A New Solution for Maintenance Scheduling of Distributed Generations based on Monte-Carlo Simulation and Game Theory

2010 ◽  
Vol 1 (08) ◽  
pp. 1135-1141
Author(s):  
M. Manbachi ◽  
A.H. Parsaeifard ◽  
M.R. Haghifam
Keyword(s):  
Game Theory ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Maintenance Scheduling ◽  
Distributed Generations

scholarly journals Formation-based modelling and simulation of success in soccer

2018 ◽  
Vol 17 (2) ◽  
pp. 204-215 ◽  
Author(s):  
J. Perl
Keyword(s):  
Neural Networks ◽  
Game Theory ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Artificial Neural Networks ◽  
Control Space ◽  
Strategic Behaviour ◽  
Flexible Response ◽  
Alternative Approach ◽  
The One

Abstract The players’ positions of tactical groups in soccer can be mapped to formation-patterns by means of artificial neural networks (Kohonen, 1995). This way, the hundreds of positional situations of one half of a match can be reduced to about 20 to 30 types of formations (Grunz, Perl & Memmert, 2012; Perl, 2015), the coincidences of which can be used for describing and simulating tactical processes of the teams (Memmert, Lemmink & Sampaio, 2017): Developing and changing formations in the interaction with the opponent activities can be understood as a tactical game in the success context of ball control, space control and finally generating dangerous situations. As such it can be simulated using mathematical approaches like Monte Carlo-simulation and game theory in order to generate optimal strategic patterns. However, in accordance with results from game theory it turns out that in most cases the one optimal strategy does not exist (e.g. see Durlauf & Blume, 2010). Instead, a variety of partial strategies with different frequencies were necessary – an approach that is mathematically interesting but has nothing to do with soccer reality. An alternative approach, which is developed in the following, is to interrupt the strictness of a single strategic concept by creative elements, which improves flexible response to opponent activities as well as prevents from being analyzed by the opponent team. The results of respective simulation reach from improving strategic behaviour to recognizing strategic patterns and in particular to analyzing role and meaning of creative elements.

scholarly journals Game theory modelling of retail marketing discount strategies

2019 ◽  
Vol 37 (5) ◽  
pp. 555-566
Author(s):  
Mark Taylor ◽  
Vince Kwasnica ◽  
Denis Reilly ◽  
Somasundaram Ravindran
Keyword(s):  
Game Theory ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Simulation Modelling ◽  
Marketing Strategies ◽  
Theoretical Research ◽  
Consumer Decision Making ◽  
Content Type ◽  
Retail Marketing ◽  
Payoff Matrices

Purpose The purpose of this paper is to use the game theory combined with Monte Carlo simulation modelling to support the analysis of different retail marketing strategies, in particular, using payoff matrices for modelling the likely outcomes from different retail marketing strategies. Design/methodology/approach Theoretical research was utilised to develop a practical approach for applying game theory to retail marketing strategies via payoff matrices combined with Monte Carlo simulation modelling. Findings Game theory combined with Monte Carlo simulation modelling can provide a formal approach to understanding consumer decision making in a retail environment, which can support the development of retail marketing strategies. Research limitations/implications Game theory combined with Monte Carlo simulation modelling can support the modelling of the interaction between retail marketing actions and consumer responses in a practical formal probabilistic manner, which can inform marketing strategies used by retail companies in a practical manner. Practical implications Game theory combined with Monte Carlo simulation modelling can provide a formalised mechanism for examining how consumers may respond to different retail marketing strategies. Originality/value The originality of this research is the practical application of game theory to retail marketing, in particular the use of payoff matrices combined with Monte Carlo simulation modelling to examine likely consumer behaviour in response to different retail marketing approaches.

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