Abstract
A modified genetic algorithm (MGA) is developed for solving the flow shop sequencing problem with the objective of minimizing mean flow time. To improve the general genetic algorithm (GA) procedure, two additional operations are introduced into the algorithm. One replaces the worst solutions in each generation with the best solutions found in previous generations. The other improves the most promising solution, through local search, whenever the best solution has not been updated for a certain number of generations. Computational experiments on randomly generated problems are carried out to compare the MGA with the general GA and special-purpose heuristics. The results show that the MGA is superior to general GA in solution quality with similar computation times. The MGA solutions are also better than those given by special-purpose heuristics though MGA takes longer computation time.
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