|Title:||Agent-based airfreight planning system for IATA-compliant containers with bin packing heuristics and a genetic algorithm|
|Subject:||Hong Kong Polytechnic University -- Dissertations.|
Aeronautics, Commercial -- Freight -- Data processing.
Aeronautics, Commercial -- Freight -- Planning.
Cargo handling -- Data processing.
|Department:||Department of Computing|
|Pages:||vi, 55 leaves : ill. ; 30 cm.|
|Abstract:||This dissertation aims to design an intelligent logistics system that supports the operations of a regional airfreight forwarding business with IATA-compliant containers. It utilizes the existing methods, such as the First-Fit and the Best-Fit heuristics, and a genetic algorithm to resolve the bin packing problem. Existing solutions on this type of problem are generally designed for two-dimensional rectangular items with relatively few restrictions regarding the physical placement; however, these approaches are insufficient for the airfreight forwarding industry. IATA-compIiant containers are restricted by the physical structure and form of an airplane; hence they have shapes that are relatively irregular to increase usable spaces. This leads to inefficient resource utilization when allocated with the generic approach. In our work, we explore the three-dimensional bin packing problem with irregular bins (IATA-compliant containers) and regular items (packages to be shipped). We aim to improve on the profits by always selecting a configuration that generates more profits by minimizing costs. In addition to the bin packing algorithms, the paper proposes using multi-agent systems and web services to extend this local problem onto a regional level. Through the communication module between the shipping centers, it provides a channel for information exchange and aims to optimize bin packing results in the global perspective. While the system achieves similar results in terms of costs for the three algorithms when experimented with a small sample data size, First-fit heuristic requires a significantly less amount of time for a large sample data size. Conversely, the genetic algorithm uses processing time in the unit of days and results with optimal configuration. Lies between the two algorithms are the best-fit heuristic. Although it consumes more time than the first-fit heuristic, its configuration optimization is comparable to that of the genetic algorithm at a much faster pace.|
|Rights:||All rights reserved|
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