Modeling of Throughput in Production Lines Using Response Surface Methodology and Artificial Neural Networks

Abstract

The problem of assigning buffers in a production line to obtain an optimum production rate is a combinatorial problem of type NP-Hard and it is known as Buffer Allocation Problem. It is of great importance for designers of production systems due to the costs involved in terms of space requirements. In this work, the relationship among the number of buffer slots, the number of work stations, and the production rate is studied. Response surface methodology and artificial neural network were used to develop predictive models to find optimal throughput values. 360 production rate values for different number of buffer slots and workstations were used to obtain a fourth-order mathematical model and four hidden layers' artificial neural network. Both models have a good performance in predicting the throughput, although the artificial neural network model shows a better fit ( = 1.0000) against the response surface methodology ( = 0.9996). Moreover, the artificial neural network produces better predictions for data not utilized in the models construction. Finally, this study can be used as a guide to forecast the maximum or near maximum throughput of production lines taking into account the buffer size and the number of machines in the line.

Order-Driven Dynamic Resource Configuration Based on a Metamodel for an Unbalanced Assembly Line

“…Azadeh et al [33] optimized the performance of a steelmaking plant based on a second-order RSM metamodel, where the effect of four factors (e.g., the number of converters and mixing machines) on plant capacity was studied. For the problem of assigning buffers in a production line, Nuñez-Piña et al [34] found the optimal output values through the construction of a predictive four-order RSM metamodel. In their work, the relationship among the number of buffer slots, the number of workstations and the production rate is studied.…”

“…The Root Mean Squared Error (RSME) and the Correlation Coefficient (R) are two popular indicators to measure the performance of the metamodel [34].…”

Buffer allocation problem in a shoe manufacturing line: A metamodeling approach

“…In a comparison of regression analysis and artificial neural networks for modeling the production rate, neural networks showed a better fit of the data, although only the value of R 2 is used as a performance criterion [22].…”

“…Both for the cycle time and for the production of the line, it is only viable with the current design to obtain the x 1 x 2 , x 1 x 3 , x 1 x 4 , x 2 x 3 , and x 2 x 4 interactions while the remaining 19 are confusing or masked. In earlier papers, interactions between the buffers have been found to have a significant effect on the performance measurements as well as on other properties such as the blocking probability for a station [4,22,24]. Although the correlation coefficient is high, we consider performing new experiments that will enable us to detect the significant interactions between buffers, so the second phase of runs is performed to characterize the region of interest.…”

Surface Mounted Production Line Capacity Prediction Based on Data Driven

“…Vainio predicted the average production time in the production line as well as the line capacity based on the ANN method [10]. Federico et al predicted the relationship between buffer tanks, the number of workstations, and productivity based on the response surface method and ANN to predict production line capacity [11]. The literature used a support vector regression (SVR) optimization model for predicting SMT production lines [5].…”