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An optimization-based decision support tool for air cargo loading
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AI
DOI：10.1016/j.cie.2022.108816.png)
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摘要
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In this research, we study the air cargo loading problem that aims to assign cargo containers to appropriate loading positions within a freight carrier aircraft. Here, as we deal with an aircraft that has been specially reconfigured into a freight aircraft from originally a passenger aircraft, this leads to a novel air cargo loading problem that is subject to four types of constraints, namely: assignment constraints; maximum position weight limits and zero fuel weight limit considerations; center of gravity (CG) envelope limiting conditions, which are based on the aircraft weight and fluctuating CG during the fueling process; respecting panel weight limits (a legacy constraint from the passenger aircraft structure), which are related to the CG envelope; and finally, lateral imbalance limits for double-row cargo configurations. We minimize the deviation from an optimal CG value, which is determined based on fuel economy and safety restrictions. This problem is formulated as a 0-1 mixed-integer nonlinear programming model, which is subsequently linearized, and four different types of aircraft configurations are utilized to test our formulation. The results indicate that significant improvements can be achieved as compared to a more traditionally used method in the freight cargo industry. Finally, we highlight a user-oriented, functional, and graphically appealing decision support tool, based on the proposed optimization framework, that has been developed and deployed at a major air cargo operator in Singapore.
Keyword:
Air cargo loading
0-1 mixed-integer nonlinear programming
Convex and concave outer-envelopes
Freighter aircraft
Decision support tool
期刊
IF:
6.5
论文数:
1.0W
被引数:
3.8W
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