Summary of Integrated Trucks Assignment and Scheduling Problem with Mixed Service Mode Docks: a Q-learning Based Adaptive Large Neighborhood Search Algorithm, by Yueyi Li et al.
Integrated trucks assignment and scheduling problem with mixed service mode docks: A Q-learning based adaptive large neighborhood search algorithm
by Yueyi Li, Mehrdad Mohammadi, Xiaodong Zhang, Yunxing Lan, Willem van Jaarsveld
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed model integrates dock mode decision, truck assignment, and scheduling to enable adaptive dock mode arrangements in warehouses. The Q-learning-based adaptive large neighborhood search (Q-ALNS) algorithm adjusts dock modes via perturbation operators while solving truck assignment and scheduling using destroy and repair local search operators. The algorithm adapts operator selection based on performance history and future gains using the epsilon-greedy strategy. Experimental results demonstrate the Q-ALNS’s efficiency in optimality gap and Pareto front discovery, outperforming benchmark algorithms. The adaptive strategy reduces average tardiness and makespan compared to predetermined service modes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers developed a new way to optimize warehouse operations by adjusting how trucks are loaded and unloaded from warehouses. They created an algorithm that can change its approach based on past performance and future goals. This helped the algorithm find better solutions more quickly than other methods. The results showed that this adaptive strategy was effective in reducing delays and improving overall efficiency. |
