Allocation of items considering unit loads balancing and joint retrieving

In the last years, the diffusion of lean thinking had a big impact, not only in manufacturing, but in logistics too. Because of one-piece-flow production and the point of view on inventory that considers it as inefficiency, purchasing and shipping batches have become smaller and more varied, requiring to the suppliers more shipments per day, a shorter throughput time, and, in general, higher performances. To improve retrieving performance in automated warehouses, many routing and scheduling procedures are presented in literature, although retrieving can be speeded up starting from the input phase using a correct allocation policy. In this paper, we present a procedure inspired by Genetic Algorithm (GA) for allocation of items inside unit loads. The procedure considers two aspects that are hardly studied in literature, such as unit load weight balancing and market basket analysis aimed at closed allocation of items that are usually jointly retrieved. The first one is a physical necessity, especially required in the steel sector, where objects stocked are heavy. The second one improves the retrieving performance and it increases the possibility to satisfy more order lines with fewer travels. The algorithm proposed was tested using the digital twin of an existing warehouse and comparing the results with the current performances of the real system