Integrated trucks assignment and scheduling problem with mixed service mode docks: A Q-learning based adaptive large neighborhood search algorithm

TL;DR

This paper introduces a novel Q-learning-based adaptive large neighborhood search algorithm for integrated truck assignment and scheduling with mixed service mode docks, improving efficiency and adaptability in warehouse operations.

Contribution

It presents a new integrated model and an adaptive algorithm that dynamically decides dock modes, truck assignment, and scheduling, outperforming existing fixed-mode approaches.

Findings

Q-ALNS outperforms benchmark algorithms in optimality gap.

Adaptive dock mode selection reduces tardiness and makespan.

Algorithm effectively balances operator performance for better solutions.

Abstract

Mixed service mode docks enhance efficiency by flexibly handling both loading and unloading trucks in warehouses. However, existing research often predetermines the number and location of these docks prior to planning truck assignment and sequencing. This paper proposes a new model integrating dock mode decision, truck assignment, and scheduling, thus enabling adaptive dock mode arrangements. Specifically, we introduce a Q-learning-based adaptive large neighborhood search (Q-ALNS) algorithm to address the integrated problem. The algorithm adjusts dock modes via perturbation operators, while truck assignment and scheduling are solved using destroy and repair local search operators. Q-learning adaptively selects these operators based on their performance history and future gains, employing the epsilon-greedy strategy. Extensive experimental results and statistical analysis indicate that the Q-ALNS benefits from efficient operator combinations and its adaptive mechanism, consistently outperforming benchmark algorithms in terms of optimality gap and Pareto front discovery. In comparison to the predetermined service mode, our adaptive strategy results in lower average tardiness and makespan, highlighting its superior adaptability to varying demands.