Optimization of Quality Control Processes Using the NPGA Genetic Algorithm

Abstract

In the article, the problem of multi-criteria optimization of quality control mechanisms is analyzed. The presented method assumes the use of the NPGA genetic algorithm to simultaneously manage costs and the level of detecting non-conformities. The main assumption of the presented approach is to treat individual quality control procedures as vectors, whose elements are probability generating functions of defect detection. Each of these procedures generates certain operational costs and covers specific types of defects within its scope. The task of the presented algorithm is to indicate which procedure and to what extent should operate to ensure an appropriate level of non-conformity detection while minimizing costs. The article presents the theoretical foundations of the developed algorithm and the results of its implementation. The software has been developed in C++ with a particular focus on performance aspects. Its essence lies in the implementation of data structures introduced in the theoretical part, as well as methods for their rapid processing. Thanks to this approach, the entire program is scalable and can be used to solve multidimensional optimization problems. The presented approach may also find application in other areas of enterprise management. This will be possible primarily in cases where the effectiveness of procedures or devices is primarily evaluated based on probability. Therefore, the presented methods can provide effective optimization of other areas related to enterprise management.

Keywords

• quality control
• genetic algorithm
• enterprise management
• npga
• multi-criteria process optimization
• quality management