DOI: https://doi.org/10.15587/1729-4061.2014.27915

Fuzzy constraint handling technique used with genetic algorithms to optimize order quantity

Ольга В’ячеславівна Єгорова, Ігор Олегович Пасішний

Abstract


The paper presents the problem of determining the optimal vol­ume of ordering goods with the deferred delivery cost payment taking into account input control errors, the time factor when making finan­cial calculations and inflation. Solving this task is difficult non-trivial process that requires applying evolutionary optimization methods that do not depend on the choice of the starting point and do not need ad­ditional constrains on the objective function characteristics.

The most efficient way to find the optimum of constrained problems using evolutionary algorithms is applying adaptive and problem-oriented penalty functions. However, the main problem that accompanies their use is the solution quality sensitivity to the choice of the individual parameters of penalty elements, calculation methods of which are not always known.

The paper proposes using fuzzy penalty functions, the main idea of which is to replace the constraints as inequalities by a set of fuzzy vari­ables. The algorithm for implementing the method in full search space of possible solutions was explored. The aspects of software implemen­tation of the technology were examined. The experimental verification of the method was performed, and the results of a comparative analysis of the dynamic and adaptive penalty functions were given. 


Keywords


genetic algorithms; penalty functions; ordering goods; credit; deficit; natural loss; incoming inspection; lack

References


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ISSN (print) 1729-3774, ISSN (on-line) 1729-4061