Cost optimization of acceptance sampling plan in a fuzzy supply chain environment
International Journal of Quality & Reliability Management
ISSN: 0265-671X
Article publication date: 14 September 2023
Issue publication date: 15 February 2024
Abstract
Purpose
The purpose of the paper is set to minimize the total cost of a manufacturing system when an acceptance sampling plan (ASP) is carried out in a fuzzy environment.
Design/methodology/approach
A fuzzy acceptance sampling plan (FASP) is employed for the inspection of the batch of products and a fuzzy cost optimization problem is formulated.
Findings
The extent of uncertainty determines an interval for the total cost function with upper and lower bounds. The effect of variation in the ambiguity of the proportion of defectives in the probability of acceptance is determined.
Practical implications
The proposed model is specifically designed for production and supply units with ASP for attributes. Still, the proportion of defectives in the inspection process is fuzzy.
Originality/value
Fuzzy probability distribution is used to model an optimal inspection plan for a general supply chain. Economic design of supply chain under fuzzy proportion of defectives is discussed for the first time.
Keywords
Acknowledgements
The authors would like to thank the editor and referees for their helpful comments which improved the quality of the presentation of the paper. The first author likes to thank CSIR, Government of India for extending financial support (No: 09/874 (0039)/2019-EMR-I). The authors also wish to thank DST, Government of India, for extending the computational laboratory support under the DST-FIST project (No: SR/FST/MS-1/2019/40) of the Department of Mathematics, NIT Calicut, India.
Citation
Thomas, J.T. and Kumar, M. (2024), "Cost optimization of acceptance sampling plan in a fuzzy supply chain environment", International Journal of Quality & Reliability Management, Vol. 41 No. 3, pp. 901-914. https://doi.org/10.1108/IJQRM-03-2023-0076
Publisher
:Emerald Publishing Limited
Copyright © 2023, Emerald Publishing Limited