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Most of the earlier studies in the inventory control and management make assumption that the manufacturing system is reliable and does not fail. However, in the real industrial applications, there is no completely reliable manufacturing system; Machine failure occur and the production does not resume before repair. In this paper, we will study and analyze the optimal lot size in a real production system which is not completely reliable. To obtain the optimal production quantity. Fuzzy Inference System (FIS) and Adaptive Neuro-Fuzzy Inference System (ANFIS) have been used for modeling and simulation. This approach combines the advantages of rule-base fuzzy system and the learning capability benefit of neural networks. In the case study of cement industry, ANFIS prediction has shown very good agreement with the real production quantity. This model can be extended for any inventory production quantity problems if the industrial data are available.

Most of the earlier studies in the inventory control and management make assumption that the manufacturing system is reliable and does not fail. However, in the real industrial applications, there is no completely reliable manufacturing system; Machine failure occur and the production does not resume before repair. In this paper, we will study and analyze the optimal lot size in a real production system which is not completely reliable. To obtain the optimal production quantity. Fuzzy Inference System (FIS) and Adaptive Neuro-Fuzzy Inference System (ANFIS) have been used for modeling and simulation. This approach combines the advantages of rule-base fuzzy system and the learning capability benefit of neural networks. In the case study of cement industry, ANFIS prediction has shown very good agreement with the real production quantity. This model can be extended for any inventory production quantity problems if the industrial data are available.

Most of the earlier studies in the inventory control and management make assumption that the manufacturing system is reliable and does not fail. However, in the real industrial applications, there is no completely reliable manufacturing system; Machine failure occur and the production does not resume before repair. In this paper, we will study and analyze the optimal lot size in a real production system which is not completely reliable. To obtain the optimal production quantity. Fuzzy Inference System (FIS) and Adaptive Neuro-Fuzzy Inference System (ANFIS) have been used for modeling and simulation. This approach combines the advantages of rule-base fuzzy system and the learning capability benefit of neural networks. In the case study of cement industry, ANFIS prediction has shown very good agreement with the real production quantity. This model can be extended for any inventory production quantity problems if the industrial data are available.

Most of the earlier studies in the inventory control and management make assumption that the manufacturing system is reliable and does not fail. However, in the real industrial applications, there is no completely reliable manufacturing system; Machine failure occur and the production does not resume before repair. In this paper, we will study and analyze the optimal lot size in a real production system which is not completely reliable. To obtain the optimal production quantity. Fuzzy Inference System (FIS) and Adaptive Neuro-Fuzzy Inference System (ANFIS) have been used for modeling and simulation. This approach combines the advantages of rule-base fuzzy system and the learning capability benefit of neural networks. In the case study of cement industry, ANFIS prediction has shown very good agreement with the real production quantity. This model can be extended for any inventory production quantity problems if the industrial data are available.

Most research studies on the economic production quantity (EPQ) model considered that produced items are of perfect quality. On the other hand, real production systems have some product defects. Considering the imperfect items makes the inventory model more complex, and more difficult to solve analytically rather than it is time consuming. Therefore, an efficient approach like D-optimal response surface methodology (RSM) is required since heterogeneous combination of data can be modeled to generate response surfaces and obtain optimum decision parameters values. This paper solves the EPQ model with sales return, rework, shortage and scrap by RSM optimization technique in order to optimize the long run average cost function. ANOVA analysis of data obtained from the total cost RSM quadratic model has shown that the Model is significant according to F, “Prob > F” and p-values.

Most research studies on the economic production quantity (EPQ) model considered that produced items are of perfect quality. On the other hand, real production systems have some product defects. Considering the imperfect items makes the inventory model more complex, and more difficult to solve analytically rather than it is time consuming. Therefore, an efficient approach like D-optimal response surface methodology (RSM) is required since heterogeneous combination of data can be modeled to generate response surfaces and obtain optimum decision parameters values. This paper solves the EPQ model with sales return, rework, shortage and scrap by RSM optimization technique in order to optimize the long run average cost function. ANOVA analysis of data obtained from the total cost RSM quadratic model has shown that the Model is significant according to F, “Prob > F” and p-values.

Most research studies on the economic production quantity (EPQ) model considered that produced items are of perfect quality. On the other hand, real production systems have some product defects. Considering the imperfect items makes the inventory model more complex, and more difficult to solve analytically rather than it is time consuming. Therefore, an efficient approach like D-optimal response surface methodology (RSM) is required since heterogeneous combination of data can be modeled to generate response surfaces and obtain optimum decision parameters values. This paper solves the EPQ model with sales return, rework, shortage and scrap by RSM optimization technique in order to optimize the long run average cost function. ANOVA analysis of data obtained from the total cost RSM quadratic model has shown that the Model is significant according to F, “Prob > F” and p-values.

Most research studies on the economic production quantity (EPQ) model considered that produced items are of perfect quality. On the other hand, real production systems have some product defects. Considering the imperfect items makes the inventory model more complex, and more difficult to solve analytically rather than it is time consuming. Therefore, an efficient approach like D-optimal response surface methodology (RSM) is required since heterogeneous combination of data can be modeled to generate response surfaces and obtain optimum decision parameters values. This paper solves the EPQ model with sales return, rework, shortage and scrap by RSM optimization technique in order to optimize the long run average cost function. ANOVA analysis of data obtained from the total cost RSM quadratic model has shown that the Model is significant according to F, “Prob > F” and p-values.

This paper is aimed at investigating thoroughly the corona performance in the wirecylinder ESP with and without loading by suspended particles in the exhaust of a diesel engine. The onset voltage of negative corona on the discharge wire is calculated based on the criterion of self-sustained discharge. The ionized space between the discharge wire and the collecting cylinder of the ESP is mathematically modeled for calculating the spatial distribution of the space-charge density due to both the ions and the charged particles as well as the components of the electric field including the applied field and the field due to the space charge. This is in addition to the calculation of the currentvoltage characteristics of the ESP with and without particle loading.