Review of Multiresponse Process Optimisation Methods

Abstract

The review of methods used for multiresponse process parameter design and similar multiobjective optimisation problems is discussed in this chapter, implying the following classification: (1) conventional methods based on statistical or mathematical techniques: (i) experimental design techniques (response surface methodology, Taguchi's robust parameter design and related approaches), and (ii) iterative mathematical search techniques; (2) non-conventional methods based on artificial intelligence techniques: (i) fuzzy logic, (ii) artificial neural networks, (iii) metaheuristic search techniques (genetic algorithm, simulated annealing, particle swarm optimisation, ant colony optimisation, tabu search, and recent evolutionary algorithms such as artificial bee colony algorithm, biogeography-based optimisation, and teaching-learning-based optimisation), and (iv) expert systems.

A novel TOPSIS linear programming model based on response surface methodology for determining optimal mixture proportions of lightweight concrete blocks containing sugarcane bagasse ash

“…Additionally, response surface methodology (RSM) can be used to fit mathematical models to data, enabling the optimization of multiple responses simultaneously. There are many methods for solving MRO problems, including [ [8] , [9] , [10] , [11] , [12] ]: (1) Pareto optimization: This technique is based on the pareto dominance principle, in which a solution is deemed superior if none of the other solutions in the search space dominates it. This method generates a set of pareto-optimal solutions that cannot be improved in one direction without deteriorating in another; (2) Weighted-sum method: This method involves converting a multi-objective optimization issue into a single-objective optimization problem by giving weights to each objective.…”