Hybrid genetic algorithms for minimizing makespan in dynamic job shop scheduling problem

• We developed efficient hybrid genetic algorithms for dynamic job shop scheduling.
• A new KK heuristic is proposed and it is combined with genetic algorithm.
• The problem includes new job arrival, machine breakdown and changes in processing time.
• In conclusion, proposed methodologies generate outstanding solutions.

Job shop scheduling has been the focus of a substantial amount of research over the last decade and most of these approaches are formulated and designed to address the static job shop scheduling problem. Dynamic events such as random job arrivals, machine breakdowns and changes in processing time, which are inevitable occurrences in production environment, are ignored in static job shop scheduling problem. As dynamic job shop scheduling problem is known NP-hard combinatorial optimization, this paper introduces efficient hybrid Genetic Algorithm (GA) methodologies for minimizing makespan in this kind of problem. Various benchmark problems including the number of jobs, the number of machines, and different dynamic events are generated and detailed numerical experiments are carried out to evaluate the performance of proposed methodologies. The numerical results indicate that the proposed methods produce superior solutions for well-known benchmark problems compared to those reported in the literature.