Analysis of modification of the evolutionary algorithm for sequencing production tasks

Analysis of modification of the evolutionary algorithm for sequencing production tasks

Piotr Ciepliński, Sławomir Golak, Tadeusz Wieczorek

Department of Industrial Computer Science, Faculty of Materials Engineering, Silesian University of Technology, Katowice, Poland.



Evolutionary algorithms are one of the heuristic techniques used to solve task sequencing problems. An important example of such a problem is the issue of sequencing production tasks. The combinatorial optimization of task sequences allows the minimization of the cost or time of a set of production tasks by reducing the components of these values which are present in the transitions between tasks.
This paper aims to analyze the influence of the production nature expressed by a set of production task parameters and a definition of the task transition cost on the effectiveness of the modification of the evolutionary algorithm based on new directed stochastic mutation operators. The research carried out included the influence of the space dimension of the task parameters, the number of levels of the value of the cost function, and a definition of this function. The results obtained allow us to assess the effectiveness of the directed mutation in task sequencing for productions of various natures.

Cite as:

Ciepliński, P., Golak, S., & Wieczorek, T. (2022). Analysis of modification of the evolutionary algorithm for sequencing production tasks. Computer Methods in Materials Science, 22(3), 157-166.

Article (PDF):


Evolutionary algorithm, Task sequencing, Mutation operator


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