OPTIMIZING PRODUCTION SCHEDULING THROUGH HYBRID DYNAMIC GENETIC-ADAPTIVE IMPROVED GRAVITATIONAL OPTIMIZATION ALGORITHM

Abstract

Mass customization is becoming the more and more of emphasis on the production optimization. In many manufacturing and service organizations, production planning and scheduling are characterized as the daily decision-making procedures. The significance of the choices made is therefore to shown in the areas of work orders, manufacturing, transportation, and distribution of the finished goods. Production scheduling is the process of regulating, determining, and maximizing the restricted resources of the production system. In this study, a novel Hybrid Dynamic Genetic-Adaptive Improved Gravitational Optimization Algorithm (HDG-AIGOA) approach is introduced to optimize the production schedule. In this case, the AIGOA classification effectiveness is increased by using the HDG method. The small and benchmark iMOPSE dataset has been used to assess the success of suggested approach. The noisy data from raw data samples are removed using the Adaptive Median Filter (AMF) filter. To extract the properties from the segmented data, a Kernel Principal Component Analysis (KPCA) is performed. The results of the research show that the recommended methodology beats earlier approaches in terms of the accuracy, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Square Error (MSE). Our proposed method might consider to improve the production scheduling in an dynamic environment.

Keywords

• production scheduling
• adaptive median filter (amf)
• kernel principal component analysis (kpca)
• genetic adaptive algorithm(gaa)
• gravitational optimization algorithm (goa)
• hybrid dynamic genetic-adaptive improved gravitational optimization algorithm (hdg-aigoa)