A Learning-based Iterated Local Search Algorithm for Order Batching and Sequencing Problems

Abstract

An order batching and sequencing problem in a warehouse is studied in this work. The problem is proved to be an NP-hard problem. A mathematical programming model is formulated to describe it clearly. To minimize tardiness, an improved iterated local search algorithm based on reinforcement learning is proposed. An operator selecting scheme, which aims to automatically select local search operator combinations instead of simply performing all the operators in each iteration, is designed to reduce the computational cost greatly. Besides, an adaptive perturbation mechanism is designed to improve its global search ability. Extensive simulation experimental results and comparisons with the state of the art demonstrate the high effectiveness and efficiency of the proposed approach.