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.