Determination of manufacturing process failures priority under type 2 fuzzy environment: Application of genetic algorithm and Variable neighborhood search

The aim of this research is to propose the two-stage model to select a set of failures that need to be eliminated or reduced which leads to improved reliability and effectiveness of the manufacturing process in the automotive industry. In the first stage, uncertainties under the relative importance of risk factors and costs of manufacturing process downtime due to failure are modeled by type 2 fuzzy sets. The weights vector of risk factors is obtained by analytical hierarchical Process which is extended with type 2 fuzzy sets. Evaluation of risk factors at the level of each identified failure is based on failure mode and effect analysis which is widely used in practice. Determining the set of failures to be eliminated is set as a knapsack problem. The linear fitness function is defined as the ratio of the overall risk priority index and total costs. Maintenance costs incurred due to the realization of failure are limited by the available budget and in this knapsack problem are presented by a linear inequality. The solution to this problem is found by using the Genetic Algorithm and Variable Neighborhood Search. The model is verified with real-life data originating from automotive companies that exist in Serbia. Authors have managed to obtain suitable results on different knapsack problem instances. It is shown that the enhancement of the manufacturing process can be based on the proposed model.