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4240. A Simulation based Decision Support System for Logistics
Invited abstract in session WC-55: Big data analysis and AI in transportation, stream Transportation.
Wednesday, 12:30-14:00Room: S02 (building: 101)
Authors (first author is the speaker)
| 1. | Emre Çimen
|
| Industrial Engineering, Eskisehir Technical Universty | |
| 2. | Gurkan Ozturk
|
| Industrial Engineering, Eskisehir Technical University | |
| 3. | Onur Kaya
|
| Industrial Engineering, Eskisehir Technical University |
Abstract
The study is related to raw material procurement and product shipments of a steel mill. These materials arrive at the factory's port and are transported from the port to the factory by trucks. The aim is to utilize cranes efficiently, reduce costs, and minimize queues. The system reads parameters related to ports, logistics, and factories from Excel and uses them for the simulation model for different scenarios. This includes ship details such as arrival dates, loading/unloading information, coil/sheet numbers and weights, and assigned port numbers. For facilities, it involves crane-ship assignments, operational details for various processes, and compatibility between coils and trucks. The system allows users to input data for different scenarios. The model is transformed into a functional system using Python with the SimPy library. Key performance indicators are analyzed through graphs and tables by comparing various parameters. This includes calculating queue lengths, waiting times, ship completion durations, demurrage rates, and costs, and exporting results to Excel. Additionally, some artificial intelligence methods such as SVM, Random Forest, Artificial Neural Networks, or PCFs will be added to the system to make predictions related to these key indicators.
Keywords
- Simulation
- Decision Support Systems
- Artificial Intelligence
Status: accepted
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