Abstract
Simultaneous optimisation of multiple quality characteristics or responses is so-called multiple response optimisation (MRO) problem. As the responses are often correlated, trade-off solutions are inevitable for multiple response optimisation problem. Thus, multiple efficient or nondominated solutions are sought for a specific multiple response optimisation problem. In addition, solution quality of a multiple response optimisation problem also depends on the accuracy of empirical models or response surface (RS) models. Higher the response surface model accuracy, better is the solution quality. However, point estimate prediction from an empirical response surface model will always have inherent uncertainties. Two such prominent uncertainties are attributed to model parameters and response uncertainties. Thus, obtaining an implementable best process setting condition is always a challenging task for researchers. Researchers suggested various solution approaches to derive improved and efficient solutions in the context of multiple response optimisation. To further contribute to this field of study, this chapter proposes a multi-objective particle swarm optimisation-based solution approach for mean-responses optimisation, considering the above-mentioned uncertainties. To assist the decision-maker (DM), a ranking strategy is suggested based on two multi-criteria decision-making (MCDM) techniques. Step-wise implementation of the proposed approach is illustrated using varied mean-response multiple response optimisation cases. Implementation results are also contrasted with existing best-reported solutions (derived using various approaches). Comparative results indicate that the proposed approach can derive the best efficient solutions with higher nondominated frequency and mean responses closer to target values.
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Appendices
Appendix 1
Summary of relevant multiple response optimisation research in different solution categories (SCOO and MOO).
A summary of different multiple response optimisation solution categories (SCOO and MOO), proposed by researchers, and lacunas is provided in Table 14.
Appendix 2: Pseudo-code of MOPSO
See Fig. 6.
Pseudo-code of MOPSO
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Sharma, A.K., Mukherjee, I. (2022). A Multi-objective Particle Swarm Optimisation-Based Solution Approach for Multiple Mean-Responses Optimisation Considering Empirical Model Uncertainties. In: Ghosh, D., Khanra, A., Vanamalla, S.V., Hamid, F., Sengupta, R.N. (eds) Studies in Quantitative Decision Making. Asset Analytics. Springer, Singapore. https://doi.org/10.1007/978-981-16-5820-4_6
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