ADAPTIVE FUZZY PARTICLE SWARM OPTIMIZATION FOR FLOW-SHOP SCHEDULING PROBLEM

This paper describes the application of a hybrid of fuzzy logic and swarm intelligence in order to achieve sub-optimal solutions for flow-shop scheduling problem. Anovel adaptive approach with fuzzy particle swarm optimization is proposed. The developed model is tested with the standardized test functions and compared with selected stochastic algorithms (first with one objective functions and later with multi objective functions) to determine its applicability to general problems. Benchmark examples were utilized to evaluate the approach and determine the optimal number of the algorithm evaluations. Finally, the proposed model is applied on two practical problems of flow production problems (assembly lines and packaging lines). The results achieved were compared with the conventional priority rules and the effectiveness of the application of hybrid fuzzy logic and adaptive particle swarm optimization algorithm was demonstrated.Original Abstract: Ovaj rad razmatra novi pristup problemu rasporedivanja u protocnoj proizvodnji koristenjem kombinacije neizrazite logike i optimizacije rojevima cestica u cilju postizanja sub-optimalnog rjesenja. Predlaze se upotreba Tip-1 i Tip-2 modela neizrazite logike u kombinaciji s adaptivnim modelom rojeva cestica. Razvijeni model je usporeden na standardiziranim testnim funkcijama za stohasticke algoritme (prvo jednokriterijske, a zatim visekriterijske postavljene funkcije cilj a) kako bi se utvrdila njegova upotrebljivost na opce postavljenim problemima. Zatim je testiran na standardiziranim testnim zadacima za probleme protocne proizvodnje te konacno na dva prakticna problema protocne proizvodnje (linije montaze i linije pakiranja). Rezultati ostvareni novim modelom su usporedeni s konvencionalnim pravilima prioriteta te je pokazan kvantitativan i kvalitativan napredak primjenom hibrida neizrazite logike i rojeva cestica.