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This paper considers the permutation flowshop scheduling problem with the objective of minimizing makespan. Genetic algorithm (GA) is one of the search heuristics used to solve global optimization problems in complex search spaces. It is observed that, the efficiency of GA in solving a flowshop problem can be improved significantly by tailoring the various GA operators to suit the structure of the problem. In this paper, an effective Improved Genetic Algorithm (IGA) for flowshop scheduling, incorporating multi-crossover operators, multi-mutation operators and hypermutation is proposed. Computation results based on some permutation flowshop scheduling benchmark problems (OR-Library) show that the IGA gives a better solution when compared with the earlier reported results. Nomenclature n number of jobs m number of machines p ij processing time of job i on machine j C max , C* makespan, optimal makespan value or lower bound value k current generation number M k best makespan at k th generation p s population size p c probability of crossover p m probability of mutation p ro probability for roulette wheel selection p to probability for tournament selection p t probability for two-point crossover P p probability for PMX crossover P sj probability for SJOX crossover p l probability for LOX crossover p 3j probability for arbitrary three-job change mutation p 2j probability for arbitrary two-job change mutation

The objective of this work is to investigate the mechanical properties of Glass fiber reinforced Epoxy composites with different weight proportions. Using hand lay-up technique followed by compression moulding, five composites were prepared by varying the Glass fiber from 15 wt.% to 35 wt.%. The necessary mechanical tests were conducted as per ASTM standards and surface morphology of the fractured surfaces was examined by Scanning Electron Microscope (SEM). As per the obtained results, it is concluded that the 25 wt.% fiber loaded composite has superior mechanical properties. The results are mathematically evaluated by adopting Mixture Design optimization technique. The predicted values establish that 25 wt.% fiber loading shows good mechanical properties compared to other combinations.

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