A parallel genetic algorithm for multi-objective flexible flowshop scheduling in pasta manufacturing

Among the potential road maps to sustainable production, efficient manufacturing scheduling is a promising direction. This paper addresses lack of knowledge in theory by introducing generalized flexible flow shop model with unrelated parallel machines each stage. A mixed-integer programming formulation proposed for such model, solved two-phase genetic algorithm (GA), tackling job sequencing and machine allocation phase. The parallelized specialized island where evaluated chromosomes all generations are preserved provide final Pareto-Optimal solutions. feasibility our method demonstrated small example from literature, followed investigation premature convergence issue. Afterwards, applied real-sized instance Belgium pasta manufacturer. We illustrate how converges over iterations trade-off near-optimal solutions (with 8.50% shorter makespan, 5.24% cheaper energy cost 6.02% lower labor cost), candidates distribute objective space. comparison NSGA-II implementation further performed using hypothesis testing, having 5.43%, 0.95% 2.07% improvement three sub-objectives mentioned above. Although this focuses on issues, GA can serve as an other multi-objective optimization problems.