A fuzzy nonlinear programming approach for planning energy-efficient wafer fabrication factories

et al. 2021

Mathematics

Analyzing energy consumption is an important task for a factory. In order to accomplish this task, most studies fit the relationship between energy consumption and product design features, process characteristics, or equipment types. However, the energy-saving effects of product yield learning are rarely considered. To bridge this gap, this study proposes a two-stage fuzzy approach to estimate the energy savings brought about by yield improvement. In the two-stage fuzzy approach, a fuzzy polynomial programming approach is first utilized to fit the yield-learning process of a product. Then, the relationship between monthly electricity consumption and increase in yield was fit to estimate the energy savings brought about by the improvement in yield. The actual case of a dynamic random-access memory factory was used to illustrate the applicability of the two-stage fuzzy approach. According to the experiment results, product yield learning can greatly reduce electricity consumption.

Section: Literature Reviewmentioning

confidence: 99%

Energy-Efficient Production Planning Using a Two-Stage Fuzzy Approach

Tsai

et al. 2021

Mathematics

“…To facilitate the optimization of NLP model I, it must be converted into a more tractable model (Tsai and Chen 2013 ; Lin et al 2018 ; Chen and Wang 2019 ; Hübner et al 2020 ; Wang et al 2020 ). The objective function involving square roots should first be replaced with the following linear and quadratic equations (Tsai and Chen 2014 ): …”

Section: Diversified Ahp-tree Approachmentioning

confidence: 99%

A diversified AHP-tree approach for multiple-criteria supplier selection

2021

Comput Manag Sci

Self Cite

A decision maker usually holds various viewpoints regarding the priorities of criteria, which complicates the decision making process. To overcome this concern, in this study, a diversified AHP-tree approach was proposed. In the proposed diversified AHP-tree approach, the judgement matrix of a decision maker is decomposed into several subjudgement matrices, which are more consistent than the original judgement matrix and represent diverse viewpoints on the relative priorities of criteria. Thus, a nonlinear programming model was established and optimized, for which a genetic algorithm is designed. To assess the effectiveness of the proposed diversified AHP-tree approach, it was applied to a supplier selection problem. The experimental results showed that the application of the diversified AHP-tree approach enabled the selection of multiple diversified suppliers from a single judgement matrix. Furthermore, all suppliers selected using the diversified AHP-tree approach were somewhat ideal.

“…Similarly, in solving the two NLP problems, the successive linear programming (SLP) directions algorithm [18] stopped after a number of iterations have been repeated. Only the local optimality of the solutions was guaranteed [35].…”

Section: Application To a Real Casementioning

confidence: 99%

Enhancing the accuracy and precision of forecasting the productivity of a factory: a fuzzified feedforward neural network approach

Lin

2021

Complex Intell. Syst.

Self Cite

Most existing methods for forecasting the productivity of a factory cannot estimate the range of productivity reliably, especially when future conditions are distinct from those in the past. To address this issue, a fuzzified feedforward neural network (FFNN) approach is proposed in this study. The FFNN approach improves the forecasting precision after generating accurate fuzzy productivity forecasts. In addition, the acceptable range of a fuzzy productivity forecast is specified, based on which the sum of the memberships of actual values is maximized. In this way, the range of productivity can be precisely estimated. After applying the FFNN approach to a real case, the experimental results revealed the superiority of the FFNN approach by improving the forecasting precision, in terms of the hit rate, by 25%. Such an improvement also contributed to a better forecasting accuracy. The superiority of the FFNN approach is in the context that the accuracy of forecasting productivity is optimized only after the range of productivity has been precisely estimated. In contrast, most state-of-the-art methods focus on optimizing the forecasting accuracy, but may be ineffective without information about the range of productivity when future conditions are distinct from the past.