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Efficiency improvement of Job scheduling by using genetic algorithm: A case study in electronic Industry



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    In this paper, we present the implementation of genetic algorithms (GA) which are modified to deal with the Job scheduling in the electronic assembly Industry. The Performance comparison showed that the proposed GA gives perform significantly better in decreasing Makespan and Idle time. Furthermore, we accelerated the proposed GA by using the solution from the conventional Heuristic methods as the Initial population. It showed that the solution converges to the optimum faster than the former. However, due to the nature of Stochastic search conducted by GA, we also focus on GA parameters which through Experiment design and Fine tuning of parameters. © 2011 IEEE.

    Performance comparison (65 items found) | Genetic algorithms (740 items found) | Initial population (2 items found) | genetic algorithm (452 items found) | Heuristic methods (179 items found) | Experiment design (3 items found) | Stochastic search (2 items found) | Job scheduling (8 items found) | Fine tuning (11 items found) | Idle time (11 items found) | Industry (649 items found) | Makespan (28 items found) | makespanEfficiency improvement | Job scheduling problem | Industrial engineering | Electronic assemblies | Scheduling algorithms | Electronic industries | Design of experiments | experimental design | Project management |

    ต้นฉบับข้อมูล : scopus