Dynamic Job Shop Scheduling Problem With New Job Arrivals Using Hybrid Genetic Algorithm

Abstract

The paper tackles the dynamic job shop scheduling problem (DJSSP), aiming to schedule a new set of jobs while minimizing the completion time of all operations. This paper proposes an optimal scheduling method based on a meta-heuristic particle swarm optimization algorithm. The difficulty of this problem is to comprehensively find the best direction of a candidate solution while maintaining the minimum global makespan. To adapt the system to changes and perform the scheduling of a new job, a local search could be an appropriate solution to fix and repair the problem by guiding the search directions following the job's arrival. The problem is classified as NP-hard complex combinatorial optimization problem. Experiments based statistical analysis show that the proposed model has better convergence and accuracy than state of the art algorithms.