Design of generalized fuzzy multiple deferred state (GFMDS) sampling plan for attributes

TL;DR

This paper introduces a generalized fuzzy multiple deferred state sampling plan for attributes that effectively handles uncertainty in defect percentage estimation, optimizing sample size and improving inspection reliability.

Contribution

It proposes a novel GFMDS sampling plan considering ambiguity in defect rates, with derived performance measures and comparison to existing fuzzy plans.

Findings

GFMDS plan reduces average sample size compared to traditional plans.

Inspection errors decrease acceptance criteria, affecting plan reliability.

Numerical examples validate the efficiency of the proposed scheme.

Abstract

. A sampling plan is a pilot tool for a supply and demand chain quality check strategy. These plans proved to be economically viable for the quality inspection processes but the uncertainty in the plan parameters challenged the reliability of the application of traditional acceptance sampling plans. This study proposes a generalized fuzzy multiple deferred state (GFMDS) sampling plan for attributes that consider the ambiguity in determining the exact value of the percentage of defectives in a batch. The performance measures have been derived and the plan is designed in terms of a minimum average sample number. A comparison study is done over the existing fuzzy acceptance sampling plans for attributes and a pertinent observation is made regarding the efficiency of the GFMDS scheme. The effect of inspection errors on the sampling procedure is analyzed and the drop in the acceptance criteria of the plan is observed corresponding to the intensified inspection errors. Several numerical examples are presented to validate the results