Predicting unexpected incidents and energy consumption decline is one of the current problems in the industry. The extant study addressed parallel machine scheduling by consideration of failures and energy consumption decline. Moreover, the present paper aimed at minimizing early and late delivery penalties, and enhancing tasks. This research designed a mathematical model for this problem that considered processing times, delivery time, rotation speed and torque, failure time, and machine availability after repair and maintenance. Failure times have been predicated on using machine learning algorithms. The results indicated that the proposed model can be suitably solved for the size of 10 jobs or tasks and five machines. This research addresses the problem in two parts: the first part predicts failures, and the second part includes the sequence of parallel machine scheduling operations. After the previous data were received in the first step, machine failure was predicted by using machine learning algorithms, and a set of rules were obtained to correct the process. The obtained rules were used in the model to improve the machining process. In the second step, scheduling mode was used to determine operations sequence by consideration of these failures and machinery unavailability to achieve the optimal sequence. Moreover, it is supposed to reduce energy consumption and failures. This study used the Light GBM algorithm and achieved 85% precision in failure prediction. The rules obtained from this algorithm contributed to cost reduction. پرونده مقاله

In real quality control applications, the performance of a process or the quality of a product is described by the relationship between a non-metric response variable and one or more control variables. Furthermore, the quality characteristic of a product or process is vague, unreliable, and linguistic and cannot be accurately expressed in most practical applications. This study was carried out aimed to provide a method for monitoring the fuzzy logistic regression profile in Phase II. In these circumstances, there is a need for special diagrams to monitor the performance of this fuzzy data. To this aim, some powerful control charts including Fuzzy exponentially weighted moving average (FEWMA), fuzzy T2 (FT2) control chart have been developed. In addition, to show the performance of the proposed control charts, the fuzzy hypothesis test along with average Run Length (ARL) criterion is used in Phase II. In addition, to show the efficiency of the proposed control chart in real applications, a real case study in health-care has been applied. پرونده مقاله

Many real-world issues are based on multivariate processes with descriptive characteristics that are represented by contingency tables. A contingency table is a tool for showing the simultaneous relationship of two or more descriptive variables that is modeled by the log-linear communication function and monitored over time. In some statistical process monitoring (SPM) applications, we are faced with the multiplicity of variables and, of course, the number of nominal classifications of the response variable. To model them, a log-linear model based on large-scale contingency tables is used that are called nominal large-scale descriptive multivariate processes. In monitoring this type of process, we face the negative impact of large dimensions of contingency tables on the performance of control charts. For this purpose, a new approach based on the clustering approach in correspondence analysis have been developed to reduce the effect of large dimensions and improvement performance of the control charts in diagnosing out of control status. The performance of control charts has been evaluated using simulated studies and the results indicate the appropriate efficiency of the proposed approach in reducing the impact of the contingency table dimensions on the performance of the control charts. In addition, to demonstrate the performance efficiency of the proposed methods, a real case study in the field of renewable energy has been used, the results of which indicate the proper performance of the proposed control charts in diagnosing out of control status. پرونده مقاله