Artificial Neural Networks (ANNs) have the ability of learning and to adapt to new situations by recognizing patterns in previous data. Time Series (TS) (observations ordered in time) often present a high degree of noise which difficults forecasting. Using ANNs for Time Series Forecasting (TSF) may be appealing. However, the main problem with this approach is on the search for the best ANN arch...

To forecast a complex and non-linear system, such as a stock market, advanced artificial intelligence algorithms, like neural networks (NNs) and genetic algorithms (GAs) have been proposed as new approaches. However, for the average stock investor, two major disadvantages are argued against these advanced algorithms: (1) the rules generated by NNs and GAs are difficult to apply in investment de...

Recent maritime legislation demands the transformation of transportation sector to greener and more energy efficient. Liquified natural gas (LNG) seems a promising alternative fuel solution that could replace conventional sources. Various studies have focused on prediction LNG price; however, no previous work has been carried out forecast spot charter rate carrier ships, an important factor for...

This paper presents a new multi-objective mathematical model to address a Healthcare Inventory Routing Problem (HIRP) for medicinal drug distribution to healthcare facilities. The first part of objective function minimizes total inventory and transportation costs, while satisfaction is maximized by minimizing forecast error which caused by product shortage and the amount of expired drugs; Green...

Abstract: The increase of energy consumption in the world is reflected in the consumption of natural gas. However, this increment requires additional investment. This effect leads imbalances in terms of demand forecasting, such as applying penalties in the case of error rates occurring beyond the acceptable limits. As the forecasting errors increase, penalties increase exponentially. Therefore,...