Automatic air cargo selection and weight balancing: A mixed integer programming approach

•The paper describes cargo selection and weight balancing for freight carriers.•It presents a mixed integer programming model enabling decision support.•A wide range of stability, safety and operational constraints are considered.•The model is validated on a representative set of flights of a commer...

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Vydáno v:Transportation research. Part E, Logistics and transportation review Ročník 65; s. 70 - 83
Hlavní autoři: Vancroonenburg, Wim, Verstichel, Jannes, Tavernier, Karel, Vanden Berghe, Greet
Médium: Journal Article
Jazyk:angličtina
Vydáno: Exeter Elsevier India Pvt Ltd 01.05.2014
Elsevier Sequoia S.A
Témata:
Air freight service
Aircraft
Aircraft load planning
Decision making models
Energy consumption
Energy efficiency
Integer programming
Linear programming
Profitability
Studies
Transportation problem (Operations research)
Weight and balance
Integer programming
Aircraft load planning
Weight and balance
ISSN:1366-5545, 1878-5794
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Shrnutí:•The paper describes cargo selection and weight balancing for freight carriers.•It presents a mixed integer programming model enabling decision support.•A wide range of stability, safety and operational constraints are considered.•The model is validated on a representative set of flights of a commercial carrier.•Testing shows an improvement on both selected cargo value and aircraft balancing. The present contribution introduces a mixed integer linear programming model as a decision support tool for air cargo load planning. The main objective for the model is to find the most profitable selection from a set of cargo to be loaded on an aircraft. The secondary objective is to minimise the deviation between the aircraft’s centre of gravity, and a known target value so as to reduce fuel consumption and improve stability. The model is subject to a large number of constraints that ensure structural integrity and stability of the aircraft, as well as the safety of the cargo and crew. A set of additional constraints guarantees safe and efficient loading and unloading. Experimental results on real-life data show that the model outperforms human expert planners on both objectives, while remaining computationally fast enough for interactive use. This advocates the use of such a decision support model for all air cargo load planning.
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ISSN:1366-5545
1878-5794
DOI:10.1016/j.tre.2013.12.013