We propose a method to obtain a set of Pareto solutions using genetic algorithms and hybrid firefly algorithms for the multi-objective integrated process planning and scheduling problem (MOIPPS) and demonstrate its effectiveness through computational experiments on several benchmark problems.
In particular, the multi-objective integrated process planning and scheduling (MOIPPS) problem has huge search space and complex technical constraints and considerable difficulty in obtaining efficient solutions, hence, metaheuristic-based solution algorithms are actively being introduced.
While genetic algorithm (GA) has a chromosome representation that is highly suitable for scheduling problems and has the ability to efficiently search solutions in complex solution space, the firefly algorithm (FA) has a fast convergence speed and high multimodal search ability, and therefore has a very good global search ability.
We have developed a hybrid algorithm (GAFA) that combines the advantages of GA and FA to solve the MOIPPS problem.
In detail, GA's gene coding and genetic operation methods are combined into FA's search framework, FA's attraction-based migration scheme is redefined using GA's crossover and mutation operators, and experiments on three MOIPPS problems are carried out to verify the effectiveness of the proposed hybrid algorithm.
The results were published in "Journal of Industrial and Management Optimization"under the title of "Firefly algorithm hybridized with genetic algorithm for multi-objective process planning and scheduling"(https://doi.org/10.3934/jimo.2024003).
