Heuristic-tabu-genetic algorithm based method for flowshop scheduling to minimize flowtime

In order to avoid premature convergence, a method based on heuristic-tabu-genetic algorithm was developed to solve the NP-complete problem of flowshop scheduling to minimize flowtime of jobs. Constructive heuristic and random methods were used to generate initial solutions, tabu search was carried out before PMX crossover and swapping mutation operations to obtain local optimal solutions for each chromosome in the population, and a population management strategy was designed to generate new population. The results of extensive computational experiments indicate that the heuristic-tabugenetic algorithm is feasible, efficient and superior to the tabu search and constructive heuristic, and suggests that this proposed algorithm can also provide seed solutions for the problem of flowshop scheduling with flexible resources.