Business-to-consumer (B2C) e-commerce sales are booming. To meet customer expectations at low cost, excellent logistics performance is indispensable. In this respect, efficiently organizing order picking operations (i.e. retrieval of items in the warehouse to fulfil customer orders) is essential. Unfortunately, current academic literature is lacking models and algorithms to adequately support order picking decisions in complex, real-life settings. Therefore, this project will design efficient order picking planning algorithms that take into account the real-life characteristics of modern B2C e-commerce warehouse environments, such as scattered storage, the availability of products, and picker blocking, all features that are hardly explored academically. First, new algorithms for integrated order batching and picker routing under a scattered storage policy are developed, taking into account product availability. Second, these algorithms are evaluated using a simulation framework which accounts for picker blocking effects. The gained insights will be used to enhance the developed algorithms. Third, the impact of different warehouse configurations and scattered storage assignments is analysed. To conclude, we will provide innovative algorithms for complex optimization problems faced by many companies.
