Multi-criteria Decision Making Through Soft Computing and Evolutionary Techniques

Abstract

Time, cost and quality factors should be taken into consideration to increase productivity in production. Innovative approaches and solutions in manufacturing can be obtained by controlling the independent variables affecting these factors. For this reason, the use of optimization techniques based on different algorithm structures is increasing. Multi-criteria decision-making (MCDM) tools such as ANN (Artificial neural network), FL (Fuzzy logic), GA (Genetic algorithm), PSO (Particle swarm optimization), GRA (Grey relational analyses), TOPSIS (Technique for order of preference by similarity to ideal solution), PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation), AHP (Analytic Hierarchy Process), ELECTRE (Elimination Et Choix Traduisant la REaite) and hybrid are commonly used. Particularly, it is preferred in comparative analysis in the literature for optimum parameter determination and prediction of results in machinability studies. Throughout this chapter, research based on the studies on multi-criteria decision-making tools is discussed. Moreover, various characteristics and difference among these tools are also reported.