A hybrid genetic algorithm for optimization problems in flowshop scheduling

Numerous real-world problems relating to flow shop scheduling are complex. The main problem is that the solution space is very large and therefore the set of feasible solutions cannot be enumerated one by one. Current approaches to solve these problems are metaheuristics techniques, which fall in two categories: population-based search and trajectory-based search. Because of their complexity, recent research has turned to genetic algorithms to address such problems. This paper gives an overall view for the problems in production scheduling where considerable emphasis is put on genetic algorithms and the evaluation of trade-off solutions. Although genetic algorithms have been proven to facilitate the entire space search, they lack in fine-tuning capability for obtaining the global optimum. Therefore an integer programming model is developed by using a hybrid genetic algorithm for the problem which belongs to NP-hard class. Experimental results of a flow shop scheduling problem indicate that the hybrid genetic algorithm outperforms the other methods.