Applied Mathematics
Mohammed Musa; Jabbar Abbas
Abstract
In the context of game theory, cooperative game has been applied in several fields and can be successfully used to evaluate the players (people or companies) involved. In cooperative game theory, the core is a concept that represents the set of feasible allocations (or distributions of total payoff) ...
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In the context of game theory, cooperative game has been applied in several fields and can be successfully used to evaluate the players (people or companies) involved. In cooperative game theory, the core is a concept that represents the set of feasible allocations (or distributions of total payoff) among players that cannot be improved upon by any coalition of players. This paper aims to apply a mathematical model to modeling cooperation among power stations and fuel supply producers using a core value-based optimization algorithm. We use the cooperative game to show the potential cost in cooperation through an optimization algorithm to find the most feasible solution using the Python program as a working procedure. Then, we apply the working method to the case of fuel supply and electricity generation in Wasit Thermal Power Plant in cooperation. The outcomes of the proposed methodology will greatly help professionals to formulate and improve well-structured strategies for future electrical energy systems in the Wasit Thermal Power Plant.
Applied Mathematics
Fadheela Kareem; Jabbar Abbas
Abstract
In the context of the multiple-criteria decision aid (MCDA), several fuzzy integrals concerning capacities (non-additive measures) have been introduced by various researchers in the last sixty years. Recently, Lehrer has proposed a new integral for capacities known as concave integral. The concave ...
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In the context of the multiple-criteria decision aid (MCDA), several fuzzy integrals concerning capacities (non-additive measures) have been introduced by various researchers in the last sixty years. Recently, Lehrer has proposed a new integral for capacities known as concave integral. The concave integral is based on the decomposition of random variables into simple ingredients. The concave integral concerning capacity is defined as the maximum value obtained among all its decompositions. The paper aims to model a new integration based on the decomposition of random variables into simple ingredients for multi-criteria decision making support when underlying scales are bipolar. This paper proposes a generalization of the concave integral in terms of decompositions of the integrated function to be suitable for bipolar scales. We show that the random variable is analyzed as a combination of indicators, where each allowed decomposition has a value determined by the bi-capacity. Lastly, we illustrate our framework by a practical example.
