HPGA: An Improved Hybrid Genetic Algorithm with Parallel Acceleration for Dynamic Flexible Job-shop Scheduling Problem

Flexible job-shop scheduling problem (FJSP) is a well-known and complex variant of the classical Job-shop scheduling Problem (JSP), which finds significant applications in manufacturing industries where flexibility in the use of machines is crucial for enhancing production efficiency and reducing operational costs. Currently, many popular hybrid meta-heuristic algorithms which involve combining two or more metaheuristic strategies such as genetic algorithm (GA) and tabu search (TS) procedures, often leads to improved performance in terms of solution quality and computational efficiency. In this paper, we design an improved hybrid genetic algorithm with parallel acceleration (HFGA) for dynamic FJSP with new job insertion, which can further speed up the execution of each operator including selection, crossover, mutation and local search. Experimental results show that our proposed hybrid algorithm with parallel acceleration demonstrates quite superior performance.