Title: Advances in solution methods for optimisation of multiple quality characteristics in manufacturing processes

Authors: Sasadhar Bera; Indrajit Mukherjee

Addresses: Indian Institute of Management (IIM) Ranchi, Jharkhand 834008, India ' Shailesh J. Mehta School of Management, Indian Institute of Technology, Bombay 400076, India

Abstract: A typical problem generally encountered in the quality control and improvement operations of manufacturing processes involves simultaneously optimising multiple critical quality characteristics (or 'multiple responses'). These type of problems are so-called 'multiple response optimisation (MRO) problems'. Owing to correlation between multiple responses, trade-off solution(s) are inevitable. The term 'trade-off' is an explicit compromised solution, considering the bias from the targets and variability in the responses. The global best solution for such a problem is usually unknown. Over the years, various solution methods and their theoretical advancement are proposed. However, only a handful of critical reviews are evident in open literature. Available review articles on MRO seem insufficient and address only specific aspects of the solution methods (e.g., response-surface modelling, problem formulation, or optimisation techniques). In this study, five different types of solution methods suggested for MRO problems are categorised and critically reviewed, including response-surface-based contour plot, response surface-free data mining methods, etc. The theoretical relationship between the MRO and the multi-objective-optimisation solution methods is analysed along with identifying potential research direction in this domain.

Keywords: multiple-response optimisation; MRO; response surface; quality control and improvement; manufacturing-process optimisation; multi-objective optimisation; MOO.

DOI: 10.1504/IJPQM.2018.093448

International Journal of Productivity and Quality Management, 2018 Vol.24 No.4, pp.475 - 494

Received: 25 Aug 2016
Accepted: 05 Apr 2017

Published online: 26 Jul 2018 *

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