A Two-Level Metaheuristic for the Job-Shop Scheduling Problem with Multipurpose Machines

Abstract

This paper proposes a two-level metaheuristic consisting of lower- and upper-level algorithms for the job-shop scheduling problem with multipurpose machines. The lower-level algorithm is a local search algorithm used for finding an optimal solution. The upper-level algorithm is a population-based metaheuristic used to control the lower-level algorithm’s input parameters. With the upper-level algorithm, the lower-level algorithm can reach its best performance on every problem instance. Most changes of the proposed two-level metaheuristic from its original variants are in the lower-level algorithm. A main purpose of these changes is to increase diversity into solution neighborhood structures. One of the changes is that the neighbor operators of the proposed lower-level algorithm are developed to be more adjustable. Another change is that the roulette-wheel technique is applied for selecting a neighbor operator and for generating a perturbation operator. In addition, the proposed lower-level algorithm uses an adjustable delay-time limit to select an optional machine for each operation. The performance of the proposed two-level metaheuristic was evaluated on well-known benchmark instances. The evaluation’s results indicated that the proposed two-level metaheuristic performs well on most benchmark instances.