Economic and statistical design of andScontrol charts using an improved multi-objective particle swarm optimisation algorithm

Abstract

This research work embellishes a cost and loss model with Taguchi's loss function to incorporate the avoidable surplus quality losses due to failure to detect the out-of-control state for the economic and statistical optimisation design of and S control charts. The optimisation design considered as a multi-objective decision-making problem is carried out based on the adequate knowledge related to the process shifts, which is extracted from the field operation of conventional control charts. An improved crowding distance based fuzzy multi-objective particle swarm optimisation (CD-FMOPSO) is developed to solve such a multi-objective problem in optimisation design of and S control charts. The proposed CD-FMOPSO algorithm is first tested for several benchmark problems taken from the literature and evaluated with standard performance metrics. The test results show that the proposed algorithm does not have any difficulty in achieving well-spread Pareto optimal solutions with good convergence to true Pareto optimal front for multi-objective optimisation problems. Finally, to facilitate ease in decision making for quality control managers, a simple but effective decision making approach was introduced. The result obtained indicates that the proposed approach may be a promising tool for economic and statistical optimisation design of control charts.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 13 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.