A Two-Level Metaheuristic Algorithm for the Job-Shop Scheduling Problem

This paper proposes a novel two-level metaheuristic algorithm, consisting of an upper-level algorithm and a lower-level algorithm, for the job-shop scheduling problem (JSP). The upper-level algorithm is a novel population-based algorithm developed to be a parameter controller for the lower-level algorithm, while the lower-level algorithm is a local search algorithm searching for an optimal schedule in the solution space of parameterized-active schedules. The lower-level algorithm's parameters controlled by the upper-level algorithm consist of the maximum allowed length of idle time, the scheduling direction, the perturbation method to generate an initial solution, and the neighborhood structure. The proposed two-level metaheuristic algorithm, as the combination of the upper-level algorithm and the lower-level algorithm, thus can adapt itself for every single JSP instance.