Multi-criteria Decision Making Through Soft Computing and Evolutionary Techniques
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 reaso
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Multi-criteria Decision Making Through Soft Computing and Evolutionary Techniques Senol Bayraktar and Kapil Gupta
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. Keywords Decision making · Optimization · ANN · Fuzzy logic · PSO · GRA · TOPSIS · AHP · ELECTRE · PROMETHEE
6.1 Introduction Multi-Criteria Decision Making (MCDM) is one of the rapidly developing areas in recent years due to changes in the business sector. Decision-makers (DMr) need decision support to determine among choices and often erase less preferred options. Decision making (DM) using computers is widely preferred in all areas of DM. Since S. Bayraktar (B) Faculty of Engineering, Department of Mechanical Engineering, Recep Tayyip Erdogan University, Rize, Turkey e-mail: [email protected] K. Gupta Department of Mechanical and Industrial Engineering Technology, University of Johannesburg, Johannesburg, Republic of South Africa © Springer Nature Switzerland AG 2021 S. Pathak (ed.), Intelligent Manufacturing, Materials Forming, Machining and Tribology, https://doi.org/10.1007/978-3-030-50312-3_6
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S. Bayraktar and K. Gupta
MCDM is preferred in many fields, it has contributed to the emergence of different methods. The MCDM process has become easier for DMr, even in complex mathematical solutions, with the increasing use of computers in recent years [1]. DM is the main factor for success in many points where information and knowledge must be addressed in any working discipline. The processes and procedures consist of tasks and requirements that cover many factors and aspects to consider. It is difficult to decide and difficult to deal with in this case. Therefore, a demand arises for a mechanism that should assist in solving complex scenarios. MCDM has been developed to facilitate solving problems under different conditions and application areas [2–4]. As an example, the decisions taken for the water resources method are u
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