A discrete manufacturing SCOS framework based on functional interval parameters and fuzzy QoS attributes using moving window FPA

Abstract

Manufacturing service composition and optimal selection (SCOS) is a key technology that improves resource utilization and reduces the cost in discrete manufacturing. However, the lack of evaluation of the service composition function and the unconformity of the actual composition vague characteristics, resulting in the incomplete evaluation of the service composition. Additionally, various optimization and selection algorithms have defects of premature convergence and low efficiency. At the same time, the fitness value distribution of the service composition has a non-linear characteristic. In this article, a framework called discrete manufacturing SCOS (DMSCOS) is proposed to overcome these issues. DMSCOS uses the functional interval parameter and fuzzy QoS attribute aware evaluation model (FIPFQA) to achieve composition evaluation and introduces a moving window flower pollination algorithm (MWFPA) to achieve optimization and selection for the non-linear characteristic population. Experiments show that DMSCOS has good performance for optimization and selection. The FIPFQA has a good effect on service composition evaluation. Furthermore, compared with two other extended algorithms, the proposed MWFPA performs better when addressing the optimal and selection problem.