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Learning-Aided Iterated Local Search Algorithm for Integrated Order Batching, Picker Assignment, Batch Sequencing, and Picker Routing Problem
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DOI：10.1109/TASE.2024.3477982.png)
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Abstract
En 中文
This work tackles an integrated order batching, picker assignment, batch sequencing, and picker routing problem in warehouse environments. A Learning-Aided Iterated Local Search (LILS) is proposed to efficiently find its high-quality solutions. The main optimizer is iterated local search. A novel bidirectional long short-term memory network-embedded autoencoder, built through end-to-end unsupervised learning with an encoder and decoder, guides the search direction. To capture the implicit relationships among strongly-coupled subproblems, long short-term memory layers are incorporated in the encoder. A network-aided mutation operator is introduced to enhance global search in a low-dimensional feature space. In the decoder, low-order and high-fit subsolutions are identified and reconstructed to generate mutated offspring solutions using long short-term memory layers and a masking mechanism. To balance the exploration and exploitation of LILS, an information exchange method is developed. Numerical experiments show that LILS outperforms several existing methods in generating high-quality schedules within a reasonable time. Note to Practitioners-In a warehouse environment, an integrated optimization problem is often addressed by using heuristics due to limited computational resources. However, expedient heuristic rules tend to produce subpar results. While meta-heuristics can generate relatively better schedules, they are time-consuming, particularly for population-based algorithms that must iteratively evaluate fitness functions for numerous candidate solutions. In order to strike a balance between computational demands and solution-qualities, our approach combines machine learning techniques with meta-heuristics. Specifically, we integrate a bidirectional long short-term memory-based autoencoder into iterated local search to enhance the latter's global optimization capability. The integration of machine learning and meta-heuristics enables efficient generation of superior schedules in a limited time. Various experimental results demonstrate that the proposed method significantly outperforms its recently-developed competitive peers, thus greatly facilitating the efficient operation of a smart warehouse.
Keywords:
Routing
Sequential analysis
Logistics
Schedules
Search problems
Metaheuristics
Machine learning algorithms
Heuristic algorithms
Automation
Robots
Integrated scheduling problem
iterated local search
autoencoder
long short term memory
machine learning
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