About this project❓❓❓
- Operational Challenges: PT JNE faces significant difficulties in ensuring timely and efficient deliveries within extensive shipping networks, particularly due to flight scheduling for international and inter-island shipments. Flight delays can lead to increased shipment delays, impacting customer satisfaction and raising operational costs.
- Complex Scheduling Factors: The complexity of flight scheduling is exacerbated by rising demand and unpredictable factors such as adverse weather conditions, limited flight slots, and the intricacies of managing cargo capacity.
- Need for Advanced Solutions: There is an urgent need for more sophisticated approaches to flight scheduling that optimize costs while minimizing delivery delays.
- Dynamic Programming (DP)*: DP is identified as a powerful method for solving optimization problems under multiple conditions, but traditional DP methods can be computationally complex, especially with unpredictable variables.
- Adaptive Dynamic Programming (ADP): ADP, which incorporates reinforcement learning, is proposed as an effective solution for PT JNE’s flight scheduling challenges, allowing for continuous learning and adaptation to changing circumstances. The goal is to enhance delivery timeliness, reduce costs, and improve customer satisfaction through the implementation of ADP.
This research can still be developed because in terms of weather, but somedays if we want to devolop can develop it again for the future. more related and can be connected to the weather forecast application. then with the traffic we can see that with the flight conditions we can find the average, approximately the right and effective time to see the conditions where the traffic conditions are like and others. 🚀✍️
Contributions are always welcome! Just raise an issue, we will discuss it.
If you have any feedback, please reach out to us at syedhapiri20@gmail.com