Abstract

Reverse Logistics (RL) has become increasingly popular in different industries especially aerospace industry over the past decade due to the fact that RL can be a profitable and sustainable business strategy for many organizations. However, executing and fulfilling an efficient recovery network needs constructing appropriate logistics system for flows of new, used, and recovered products.
On the other hand, successful RL network requires a reliable monitoring and control system. A key factor for the success and effectiveness of RL system is to conduct real-time monitoring system such as radio frequency identification (RFID) technology. The RFID system can evaluate and analyze RL performance timely so that in the case of deviation in any areas of RL, the appropriate corrective actions can be taken in a quick manner. An automated data capturing system like RFID and computer simulation techniques such as agent-based (AB), system dynamic (SD) and discrete event (DE) provide a reliable platform for effective RL tracking and control, as they can respectively decrease the time needed to obtain data and simulate various scenarios for suitable best corrective actions. The functionality of the RL system can be noticeably elevated by integrating these two systems and techniques. Besides, each computer simulation approach has its own benefits for understanding the RL network from different aspects. Therefore, in this study, after designing and constructing the RL system through the real case study from Bell Helicopter Company with the aid of unified modeling language (UML), three simulation techniques were proposed for the model. Afterwards the results of all three simulation approaches (AB, SD and DE) were compared with considering two scenarios of RL RFID-enabled and RL without RFID. The computer simulation models were developed using “AnyLogic 7.1” software.
The results of the research present that with exploiting RFID technology, the total disassembly time of a single helicopter was decreased. The comparison of all three simulation methods was performed as well.

Keywords: Reverse logistics (RL), RFID, aerospace industry, agent-based simulation, system dynamic simulation, discrete event simulation, AnyLogic