A Neural Network Approach to Air Cargo Fleet Assignment
* 본 문서는 배포용으로 복사 및 편집이 불가합니다.
서지정보
ㆍ발행기관 : 대한교통학회
ㆍ수록지정보 : 대한교통학회지 / 39권
ㆍ저자명 : Choongyeol Ye
ㆍ저자명 : Choongyeol Ye
목차
1. Intoduction2. Airline Schedule Design and Fleet Assignment Model
3. Optimisation Solution Methods and Neural Network
4. Application of Neural Network to Air Cargo Fleet Assignmant
5. Conclusion
한국어 초록
As the activities of many companies become increasingly global and just-in-time concepts are becoming more universal, the amount of goods carried by air is increasing continuously. Statistics show that air cargo traffic has been growing faster than passenger traffic in most regions of the world for several decades. Though the amount of freight carried by express carriers is growing rapidly, the significant amount of air cargo is still carried by combination air carriers, and this trend is expected to continue for several decades.Shaw (1993) states the importance of air cargo thus: "No airline can be successful unless it gives the fullest possible attention to the freight side of its activities". Some long haul combination carriers now earn more than 50% of their revenue from cargo operations. However, while the market may grow, the yield from this activity is under constant pressure, reflecting productivity efficiencies and intense competition, reducing by about 2.5% per year. Air cargo carriers therefore seek constantly to streamline their operations, look for and develop new products and become more cost effective (Lobo and Zairi, 1999).