Modelling the estimation of the optimum number of required equipment and manpower for operational processes under uncertainty conditions (case study: Textile industry)

Document Type: Research Paper

Authors

1 Assistant Professor of industrial engineering, Tarbiat Modares University

2 School of Engineering, Imam Hossein University

3 School of Industrial and Systems Engineering, Tarbiat Modares University

Abstract

The cost of design and building industrial systems is greatly affected by determining the exact number of machineries and human resources, consequently allowing to achieve a higher level of efficiency and productivity. Different methods have been presented to estimate the number of required resources for operational processes. The reliability of the results from these methods is highly dependent on the estimation of the input data which, under uncertain conditions, might have a vague nature and convey incorrect information. Therefore, this study aimed to propose a novel framework based on the fuzzy logic to determine the optimal number of machineries and human resources. The fuzzy set theory was used to determine the percentage of wastes and the time required to complete operational processes. Moreover, to prove the practicality of the proposed model and given the significance of improving the productivity of Textile industry in Iran, the proposed model was employed in a case study of the textile industry and the results were compared with the standard method. The results suggest a significant difference between the number of machineries and human resources estimated by the proposed method and that by the standard method. These differences may negatively affect the performance and optimal usage of the available capacity. The results obtained from the proposed model offer more accurate and comprehensive information under uncertain conditions, allowing us to make appropriate decisions to revise the unused capacity, reduce the cost of idle resources, and increase the efficiency and productivity of the industry.

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