Abstract
Reverse logistics, which is the management or return flow due to product recovery, goods return, or overstock, form a closed-loop supply chain. The success of the closed-loop supply chain depends on actions of both manufacturers and customers. Now, manufacturers require producing products which are easy for disassembly, reuse and remanufacturing owing to the law of environmental protection. On the other hand, the number of customers supporting environmental protection by delivering their used products to collection points is increasing. According to the findings, the total cost spent in reverse logistics is huge. In order to minimize the total reverse logistics cost and high utilization rate of collection points, selecting appropriate locations for collection points is critical in reverse logistics. This paper proposes a genetic algorithm to determine such locations in order to maximize the coverage of customers. Also, the use of RFID is suggested to count the quantities of collected items in collection points and send the signal to the central return center. This can facilitate the vehicle scheduling for transferring the items from collection points to the return center. The significance of this research is the proposal of RFID-based reverse logistics framework and optimization of locations of collection points which allow economically and ecologically reasonable recycling. Simulation results indicated that the genetic algorithm is able to produce good-quality solutions in terms of coverage of collection points by choosing suitable locations for collection points and RFID can help detect the quantity of returned products so as to increase efficiency of logistics operations.
Original language | English |
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Pages (from-to) | 9299-9307 |
Number of pages | 9 |
Journal | Expert Systems with Applications |
Volume | 36 |
Issue number | 5 |
DOIs | |
Publication status | Published - 1 Jul 2009 |
Externally published | Yes |
Keywords
- Genetic algorithm
- Reverse logistics
- RFID
- Supply chain management
ASJC Scopus subject areas
- General Engineering
- Computer Science Applications
- Artificial Intelligence