Multi criteria decision making through TOPSIS and COPRAS on drilling parameters of magnesium AZ91

Magnesium(Mg)alloys are extensively used in the automotive and aircraft industries due to their prominent properties.The selection of appropriate process parameters is an important decision to be made because of the cost reduction and quality improvement.This decision entails the selection of suitable process parameters concerning various conflicting factors,so it has to be addressed with the Multiple Criteria Decision Making(MCDM)method.Therefore,this work addresses the MCDM problem through the TOPSIS(Technique for Order Preference by Similarity to Ideal Solution)and COPRAS(COmplex PRoportional ASsessment)methods.The assessment carried out in the material Mg AZ91 with the Solid Carbide(SC)drill bit.The dependent parameters like drilling time,burr height,burr thickness,and roughness are considered with the independent parameters like spindle speed and feed rate.Drilling alternatives are ranked using the above said two methods and the results are evaluated.The optimum combination was found on the basis of TOPSIS and COPRAS for simultaneous minimization of all the responses which is found with a spindle speed of 4540 rpm and a feed rate of 0.076 mm/rev.The identical sequencing order was observed in TOPSIS and COPRAS method.The empirical model was developed through Box-Behnken design for each response.Superior empirical model developed for drilling time which is 3.959 times accurate than the conventional equation.The trends of various dependents based on the heterogeneity of various independents are not identical,these complex mechanisms are identified and reported.The optimized results of the Desirability Function Approach are greater accordance with the TOPSIS and COPRAS top rank.The confirmation results are observed with lesser deviation suggesting the selection of the above independent parameters.

A HYBRID APPROACH FOR MINIMIZING MAKESPAN IN PERMUTATION FLOWSHOP SCHEDULING

This work proposes a hybrid approach for solving traditional flowshop scheduling problems to reduce the makespan （total completion time）. To solve scheduling problems, a combination of Decision Tree （DT） and Scatter Search （SS） algorithms are used. Initially, the DT is used to generate a seed solution which is then given input to the SS to obtain optimal / near optimal solutions of makespan. The DT used the entropy function to convert the given problem into a tree structured format / set of rules. The SS provides an extensive investigation of the search space through diversification. The advantages of both DT and SS are used to form a hybrid approach. The proposed algorithm is tested with various benchmark datasets available for flowshop scheduling. The statistical results prove that the proposed method is competent and efficient for solving flowshop problems.