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CHAPTER 6 CONCLUSION AND FUTURE WORK

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CHAPTER 6

CONCLUSION AND FUTURE WORK

 

6.1 CONCLUSION

The general conclusion is drawn from this thesis, and some suggested new directions are presented in this paper. The objective of this thesis is to formulate and explained the purpose of hybrid dragonfly and fruit fly algorithm. The computational examination that can be performed with the benchmark problem sets taken from the existing algorithm. Many metaheuristic techniques are used to develop cellular manufacturing systems. The cell formation is done using genetic algorithm GA as a metaheuristic technique. The genetic algorithm is developed for concurrent formation of part families and machine cells for cellular manufacturing system. The genetic algorithm is designed to handle the objective of minimization of exceptional elements in the sheet metal industry, where unique features are the machines and parts that are excluded from the suggested two or three cells. This work gives an overview of the Back Propagation Network BPN based approaches to form the machine cells and component grouping for minimizing the exceptional elements and bottleneck machines. To presents the cell formation problem that would maximize the grouping efficacy and reduce the computational time.

 

6.2 FUTURE WORK

The performances of the proposed heuristic as far as hybrid modelling are either superior or aggressive with the outstanding existing algorithms. The computational times are enormously diminished contrasting with the state-of-art approaches. They exhibited that our proposal is a capable model and demonstrate the effectiveness of our execution. The machine parts incidence matrix is given as input for the genetic algorithm to minimize the total number of exceptional elements to evaluate the effectiveness of the cell formation. The proposed genetic algorithm is coded in C++ language on a personal computer with core Duo, using various frequency processors. They also applied to the know equal as well as better. They can be compared to in the term of minimizing the number of exceptional elements.

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