Forecasting, production planning, and control
Samrad Jafarian-Namin; Davood Shishebori; Alireza Goli
Abstract
The temperature has been a highly discussed issue in climate change. Predicting it plays an essential role in human affairs and lives. It is a challenging task to provide an accurate prediction of air temperature because of its complex and chaotic nature. This issue has drawn attention to utilizing the ...
Read More
The temperature has been a highly discussed issue in climate change. Predicting it plays an essential role in human affairs and lives. It is a challenging task to provide an accurate prediction of air temperature because of its complex and chaotic nature. This issue has drawn attention to utilizing the advances in modelling capabilities. ARIMA is a popular model for describing the underlying stochastic structure of available data. Artificial Neural Networks (ANNs) can also be appropriate alternatives. In the literature, forecasting the temperature of Tehran using both techniques has not been presented so far. Therefore, this article focuses on modelling air temperatures in the Tehran metropolis and then forecasting for twelve months by comparing ANN with ARIMA. Particle Swarm Optimization (PSO) can help deal with complex problems. However, its potential for improving the performance of forecasting methods has been neglected in the literature. Thus, improving the accuracy of ANN using PSO is investigated as well. After evaluations, applying the seasonal ARIMA model is recommended. Moreover, the improved ANN by PSO outperforms the pure ANN in predicting air temperature.
Forecasting, production planning, and control
Adeniran Adetayo Olaniyi; Kanyio Olufunto Adedotun; Owoeye Adelanke Samuel
Abstract
Two years single moving average and simple exponential smoothing with smoothing constant of 0.9 were applied to forecast the 2018 demand for domestic air passenger in Nigeria. Also, the two methods of forecasting were evaluated and compared with Mean Squared Deviations (MSD) to determine which method ...
Read More
Two years single moving average and simple exponential smoothing with smoothing constant of 0.9 were applied to forecast the 2018 demand for domestic air passenger in Nigeria. Also, the two methods of forecasting were evaluated and compared with Mean Squared Deviations (MSD) to determine which method gives the lowest deviation as it will produce best forecast for the year 2018 domestic air passenger demand in Nigeria. The study relied on data of domestic air passenger demand between the periods of the year 2010 to the year 2017. It was revealed that the MSD of two yearly single moving average gave the best year 2018 forecast as it has a lower MSD when compared to the MSD of simple exponential smoothing with the smoothing constant of 0.9. This study is useful in the planning process of an airport, airline, and other stakeholders involved in Nigeria’s air transportation. It will help to prevent problems of having excess air transport demand over air transport supply or having excess air transport supply over demand.
