Abstract
In practical situations, solving a given problem usually calls for the systematic and simultaneous analysis of more than one objective function. Hence, a worthwhile research question may be posed thus: In multiobjective optimization, what can facilitate the decision maker in choosing the best weighting? Thus, this study attempts to propose a method that can identify the optimal weights involved in a multiobjective formulation. Our method uses functions of Entropy and Global Percentage Error as selection criteria of optimal weights. To demonstrate its applicability, we employed this method to optimize the machining process for vertical turning martensitic gray cast iron piston rings, maximizing the productivity and the life of cutting tool and minimizing the cost, using feed rate and rotation of the cutting tool as the decision variables. The proposed optimization goals were achieved with feed rate = 0.35 mm rev-1 and rotation = 248 rpm. Thus, the main contributions of this study are the proposal of a structured method, differentiated in relation to the techniques found in the literature, of identifying optimal weights for multiobjective problems and the possibility of viewing the optimal result on the Pareto frontier of the problem. This viewing possibility is very relevant information for managing processes more efficiently.
Highlights
Optimization techniques, in recent years, have evolved greatly, finding wide application in various types of industries, mainly because making decision about complex problems involves process optimization and engineering design
This study attempts to propose a method that can identify the optimal weights involved in a multiobjective formulation
This paper proposes an optimization of cutting conditions, maximizing the productivity and the life of cutting tool and minimizing the cost, using feed rate and rotation of the cutting tool as the decision variables, according to proposed by Severino et al(2012)
Summary
Optimization techniques, in recent years, have evolved greatly, finding wide application in various types of industries, mainly because making decision about complex problems involves process optimization and engineering design They are capable of solving ever larger and more complex problems, thanks to a new generation of powerful computers. According to Rao (2009), optimization is the act, in any given circumstance, of obtaining the best result. In this context, the main purpose of decision making in industrial processes is to minimize the Maringá, v. The effort required or the benefit desired in any practical situation can be expressed as a function of certain decision variables. This function is known as the objective function
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