DEMAND FORECASTING OF 3. ISTANBUL GRAND AIRPORT VIA ARTIFICIAL NEURAL NETWORKS AND ADAPTIVE NEURO FUZZY INFERENCE SYSTEMS FOR OPTIMIZATION OF DOMESTIC AIRCRAFT FLEET OF TURKISH AIRLINES

Journal Title: Endüstri Mühendisliği - Year 2019, Vol 30, Issue 2

Abstract

The aim of this study is to estimate the passenger and freight demand of the 3rd Istanbul Airport, which was built as a substitute for the Istanbul Ataturk Airport with Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) methods and to plan the possible aircraft fleet using financial and physical constraints by considering scenarios in order to be able to carry out the anticipated operation volume using the past period data of Istanbul Ataturk Airport. The data of the study were compiled by the Turkish Statistical Institute (TUIK) and subjected to the normalization process. The Root Mean Square Error (RMSE) and the Sum of Square Error (SSE) were used as the error measurement method and their performances were evaluated. The findings of the study include important information about the airport's ability to respond to possible demand and the airport's performance characteristics, estimated passenger and cargo values for the coming years.

Authors and Affiliations

Metehan ATAY, Yunus EROĞLU, Serap Ulusam SEÇKİNER

Keywords

Related Articles

MAXIMIZATION OF WAREHOUSE STORAGE

The objective of this study is to ensure effective warehouse storage in face of ever changing customer demands, through providing maximum storage space and volume by calculating the space to be allocated in the warehouse...

THE EFFECTS OF ENVIRONMENTAL FACTORS ON JOB PERFORMANCE IN METAL INDUSTRY

Environmental factors such as noise, temperature, humidity and illumination have direct or indirect effects on worker’s job performance, productivity, occupational health and safety. Inappropriate conditions may decrease...

Explosive Compaction of Metal and Ceramic Powders: A Review of the Application of the Technique

Explosive compaction of metal and ceramic powders is based on the fact of using high shock wave presssure formed in a few microseconds after the explosion of the detonator in the compaction of powders. Explosive compacti...

State Feedback H∞ Semi Active Control of Fighter Jet Seat

This paper deals with the design of a Linear Matrix Inequalities (LMI) based state feedback H∞ controller for a semi active vibration mitigation problem of fighter jet seats. Ten degrees of freedom detailed full aircraft...

2-OPT ALGORITHM AND EFFECTS OF INITIAL SOLUTION ON RESULTS

In this study the 2-opt heuristic algorithm which was proposed by Croes (1958) for the travelling salesman problem is presented and the effect of the initial solutions produced by constructive heuristics on the performan...

Download PDF file
  • EP ID EP673290
  • DOI -
  • Views 82
  • Downloads 0

How To Cite

Metehan ATAY, Yunus EROĞLU, Serap Ulusam SEÇKİNER (2019). DEMAND FORECASTING OF 3. ISTANBUL GRAND AIRPORT VIA ARTIFICIAL NEURAL NETWORKS AND ADAPTIVE NEURO FUZZY INFERENCE SYSTEMS FOR OPTIMIZATION OF DOMESTIC AIRCRAFT FLEET OF TURKISH AIRLINES. Endüstri Mühendisliği, 30(2), 141-156. https://europub.co.uk/articles/-A-673290