Particle Swarm Optimization Algorithm for Distributed Flow Shop Scheduling Problem
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DOI:
https://doi.org/10.5281/zenodo.14957413Keywords:
Optimization Algorithms, PSO, DFSP, MakespanAbstract
The increasing prevalence of multi-factory models has led to the emergence of more complex
problems. To solve these large and complex issues, our study utilizes the Distributed Flow Shop Scheduling
Problem (DFSP) with Particle Swarm Optimization (PSO) for inter-factory transportation. DFSP aims to
optimize job sequencing and inter-factory transportation times to minimize the total completion time
(makespan). Solving this problem is particularly crucial in improving resource-sharing efficiency,
minimizing costs, and ensuring quick market response in complex and diverse production environments.
Experiments demonstrate that PSO optimizes job sequences and balances factory assignments, achieving a
minimum makespan of 10 units. A Gantt chart was used to visualize resource utilization and planning
efficiency for a better understanding of the solution. Compared to methods such as Genetic Algorithms and
Simulated Annealing, PSO offers advantages in convergence speed and computational efficiency.
This study shows that PSO is one of the best methods applicable to real-world planning problems and
logistical challenges. Future studies may combine PSO with other metaheuristic methods for faster
resolution of dynamic planning problems.
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