Forecasting the work-in-process (WIP) level is very important for the control of factory. However, the uncertainty in the WIP level is not easy to deal with. To solve this problem, a fuzzy collaborative forecasting approach is proposed. And also we show the effectiveness of the proposed methodology with a case study.

Accurate time series forecasting is a key issue to support individual and organizational decision making. In this paper, we introduce novel methods for multi-step seasonal time series forecasting. All the presented methods stem from computational intelligence techniques: evolutionary artificial neural networks, support vector machines and genuine linguistic fuzzy rules. Performance of the sugge...

Time series forecasting in highly nonlinear and chaotic systems is a challenging research area with a variety of applications in economics, environmental sciences and various fields of engineering. This paper presents a novel Locally Quadratic Fuzzy Neural Model (LQFNM) to forecast the behavior of highly nonlinear and chaotic time series. It is based on the idea of approximating a nonlinear fun...

This paper presents an automatic method to design interval type-2 fuzzy systems for load forecasting applications using a memetic algorithm. This hybridisation of a variable-length genetic algorithm and a gradient descent method allows for concurrent learning of the system’s parameters and structure in a versatile fashion. Results are presented addressing chaotic system and market-level one-day...

Damage due to flooding has increase in many countries in the last years, and due to the global climate change, which is now recognized as a real threat, an increase in the occurrence of flooding events and especially of flash flooding events is likely to continue into the future. In those conditions and because building new flood defences structures for defending vulnerable areas has serious fi...

To reduce their stocks and to limit ruptures, textile companies must improve their supply chain management. This organization requires sales forecasting systems adapted to the uncertain environment of the textile field. The uncertainty is characterized by noisy data, short historic and numerous explanatory variables that influence the sales behavior. This paper deals with new forecasting models...

An organization has to make the right decisions in time depending on demand information to enhance the commercial competitive advantage in a constantly fluctuating business environment. Therefore, estimating the demand quantity for the next period most likely appears to be crucial. This work presents a comparative forecasting methodology regarding to uncertain customer demands in a multi-level ...

This paper explores a nearest neighbour pattern recognition method for time-series forecasting. A nearest neighbour method (FNNM) based on fuzzy membership values is developed. The main aim of the forecasting algorithm is to make single point forecasts into the future on the basis of past nearest neighbours. The nearest neighbours are selected using a membership threshold value. The results inc...

The Data mining technology is widely used in various fields. Temperature forecasting is also the domain area where data mining is used. Accuracy is always an issue in forecasting and it will remain a matter of the concern forever. This paper presents data mining model based on heuristic search, which leads to more accurate solution of forecasting problems. The suggested model is implemented on ...