Modeling of collection strategies for end-of-life products using coloured Petri Net

Download files
Access & Terms of Use
open access
Copyright: Hanafi, Jessica
Altmetric
Abstract
Currently various collection strategies are implemented in different parts of the world to collect End-of-Life (EOL) products. These strategies are specific to certain conditions in a particular region, involving different parties such as Original Equipment Manufacturers, Government or Councils, Recyclers, and the community. Since different collectors in the reverse logistics network will influence its performance, a suitable collection program is required. The objective of this research is to find an optimum collection strategy to suit various environments by considering the costs and the environmental aspects of collection. To design optimum collection strategies, information on the rate of EOL product returns is essential. Therefore, a methodology is proposed to forecast the return of EOL products by considering product life, consumer behavior and historical sales. This forecast model is then integrated into the collection strategy model. The integrated model is dynamically formulated to present the behavior of different sets of strategies. Colored Petri Net (CPN) approach is utilized in the forecasting and modeling of collection strategies. The distinct characteristic of CPN provides the ability to model uncertainties in a system. Some case studies were conducted in Australia to verify and to validate the models. The results indicate that the integrated model will help practitioners in making decisions on implementing a suitable collection strategy for Reverse Logistics.
Persistent link to this record
Link to Publisher Version
Link to Open Access Version
Additional Link
Author(s)
Hanafi, Jessica
Supervisor(s)
Kara, Sami
Kaebernick, Hartmut
Creator(s)
Editor(s)
Translator(s)
Curator(s)
Designer(s)
Arranger(s)
Composer(s)
Recordist(s)
Conference Proceedings Editor(s)
Other Contributor(s)
Corporate/Industry Contributor(s)
Publication Year
2008
Resource Type
Thesis
Degree Type
PhD Doctorate
UNSW Faculty
Files
download whole.pdf 4.11 MB Adobe Portable Document Format
Related dataset(s)