Backward Scheduling to Minimize the Actual Mean Flow Time with Dependent and Independent Setup Times

The present paper deals with a new perfomance measure, the actual mean flow time, defined as a mean of the elapsed time of each job counted from the start time on a schedule to the corresponding due date. For the one machine backward scheduling model with a common due date and independent setup times, LPT schedule is shown as the optimal solution for the proposed measure. An optimal algorithm is presented for the case with dependent setup times on the basis of the algorithm by Arcelus and Chandra for a n / 1 / F forward scheduling problem. The proposed algorithm is coded in C-language and a computational experience is reported through a 16-bit computer

Multivariate Analysis for Fault Diagnosis System

Many multivariate techniques have been applied to diagnose faults such as Principal Component Analysis (PCA), Fisher’s Discriminant Analysis (FDA), and Discriminant Partial Least Squares (DPLS). However, it has been shown that FDA and DPLS are more proficient than PCA for diagnosing faults. And recently applying kernel on FDA which is called KFDA (Kernel FDA) has showed outperformance than linear FDA based method. We propose in this research work an advanced KFDA for faults classification with Building knowledge base for faults structure using FSN. A case study is done on a chemical G-Plant process, constructed and experimental runs are done in Okayama University, Japan. The results are showing improving performance of fault detection rate for the new model over FDA

A Forecasting Decision Support System

Nowadays forecasting is needed in many fields such as weather forecasting, population estimation, industry demand forecasting, and many others. As complexity and factors increase, it becomes impossible for a human being to do the prediction operation without support of computer system. A Decision support system is needed to model all demand factors and combine with expert opinions to enhance forecasting accuracy. In this research work, we present a decision support system using winters’, simple exponential smoothing, and regression statistical analysis with a new proposed genetic algorithm to generate operational forecast. A case study is presented using real industrial demand data from different products types to show the improved demand forecasting accuracy for the proposed system over individual statistical techniques for all time series types