Learning-Aided Iterated Local Search Algorithm for Integrated Order Batching, Picker Assignment, Batch Sequencing, and Picker Routing Problem

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.