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Distribution of the problems of design training of production based on genetic algorithm

DOI 10.18127/j20700814-201809-10

Keywords:

A.M. Kostigov - Ph.D.(Eng.), Dean of Faculty of Electrical Engineering, Professor, Department of Microprocessor Units of Automation, Perm National Research Polytechnic University
E-mail: dekan@etf.pstu.ru
D.S. Dudarev - Assistant, Department of Microprocessor Units of Automation, Perm National Research Polytechnic University
E-mail: dudarevds@gmail.com
K.S. Babin - Post-graduate Student, Department of Microprocessor Units of Automation, Perm National Research Polytechnic University
E-mail: zav@msa.pstu.ru
S.V. Bochkarev - Dr.Sc.(Eng.), Associate Professor, Professor, Department of Microprocessor Units of Automation,
Perm National Research Polytechnic University
E-mail: bochkarev@msa.pstu.ru


Genetic algorithm is a heuristic search algorithm used for solving optimization and simulation through the random selection, combination and variation of the unknown parameters using the mechanisms, which resemble biological evolution.
Decision multiparameter optimization problems can be represented as a chromosome consisting of genes. Then the genes correspond to a particular parameter, and the values of the gene (alleles) – the value of the parameter. Using the analogy of genetic changes in nature, with the help of genetic operators, such as crossing-over (crossing), mutation and selection, you can search for solving optimization problems. To assess the quality of the solutions using a fitness function or utility function.
Consider the distribution of work to the optimum workload of staff, reducing material and time costs in the design bureau, whose work is represented in a hierarchical scheme in fig. 1.
In the implementation of the GA will use enumerable chromosome unique genes of N loci, genes which correspond to serial numbers of employees and their alleles correspond to serial numbers distributed tasks.

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