Approach of hybrid GA for multi-objective job-shop scheduling

In recent years, scholars have made many research results on job-shop scheduling (JSP) problem, especially in single objective such as the maximum completion time. But most of the actual system scheduling problems are more than one object. Therefore, the research of multi-objective scheduling problem is very important and meaningful. In this paper, we proposed a multi-objective scheduling model which adopts weighted sum method to optimize two important indexes (makespan and total flow time). Genetic algorithm (GA) has diversified global search ability, while simulated annealing (SA) combined with tabu search (TS) have intensified capabilities in local neighborhood search. To overcome the drawback of the GA, we proposed a new hybrid GA (NewHGA) which produces initial solutions by GA firstly, and then take SA operator incorporate TS operator to search in the local space. By adding the novel local search strategy, the diversity of solutions will be improved greatly so that it can ensure the algorithm jump out of the local optimal value. We test this algorithm using the benchmark instances of different sizes taken from the OR-Library, and the results show that the algorithm is efficient than another hybrid algorithm.