A. Baskar ¹, M. Anthony Xavior ², B. Dhanasakkaravarthi ¹

Affiliations
1 Panimalar Institute of Technology, Chennai - 600123, Tamil Nadu, IN
2 SMBS, VIT University, Vellore – 632014, Tamil Nadu, IN

Abstract

Objectives: This paper analyzes the impact of a few new initial partial sequences on the makespan in a permutation flow shop scheduling problem. Taillard benchmark problems are used for the purpose of validation. Methods/Statistical Analysis: The popular NEH heuristic considers the first two jobs as its initial partial sequence after arranging them in descending order of their total processing times. The algorithms using different partial sequences are coded in MATLAB and a total of 120 number problem instances are used for the analysis which fall under twelve sets of 10 instances each. One-way ANOVA has been conducted for validating the results. Findings: It has been found that the initial partial sequences other than the first two jobs considered by the original NEH can also yield better makespans. Also, initial ordering of jobs according to the decreasing order of the average processing time and standard deviation of the processing times proposed by in. perform relatively better. In all the cases, job insertion technique is proved to be more powerful. The random algorithm that uses the job insertion technique do perform well with a deviation of 3.4342% which is better than many other known simple algorithms. The ANOVA confirms that the variants are statically not different from the NEH algorithm. But, it shows that a few variants perform better than the NEH for the Taillard benchmark problems. Application/Improvements: The results can be used as a seed solution and could be improved using metaheuristics. Further, the authors are working on other benchmarks and using tie breaking rules to know the impact..

Keywords

Initial Partial Sequence, Makespan, NEH Heuristic, Permutation Flow Shop, Scheduling

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M. Vamsi Krishna ¹, M. Anthony Xavior ²

Affiliations
1 Department of Mechanical Engineering, M.I.T.S., Madanapalle - 517325, Andhra Pradesh, IN
2 School of Mechanical and Building Sciences, VIT University, Vellore - 632014, Tamil Nadu, IN

Abstract

The centre of research is to optimize the cutting Forces (F_R), surface Roughness (Ra) and Material Removal Rate (MRR) of end milling for Aluminium compositeusing Response Surface Methodology (RSM) and Genetic Algorithm (GA). The RSM L₃₁ empirical model is conducted with Al/SiC composites of various compositions. The cutting forces and the surface roughness are measured using 3-axis milling tool dynamometer and MarTalk Profilometer respectively. The second order mathematical models in terms of machining parameters are developed for predicting responses with adequacy above 85%. The optimal configuration of end milling are 5 wt. % of reinforcement, 0.3 mm depth of cut, feed rate of 49.3 mm/min and cutting speed of 474.3 rpm to acquire minimum FR, Ra with maximum MRR is done by Genetic Algorithm (GA). From the estimated model, the responses are with the experimental deviation of 11% MRR, 13% Ra and 17% FR for the desirability of 98.7%. The optimization of three machining parameters with a advance hybrid approach brought a new scope for the researchers and manufactures to improve the standard of automated machining.

Keywords

ANOVA, Aluminium, Composites, Milling, Optimization.

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