Product Layout Optimization for Autonomous Warehouses with Grouped Products

Sammanfattning: To utilize space better, warehouses stack their products on top of each other. This increases the risk of injury for workers when storing and retrieving the products. Some warehouses counteract this by using robots to retrieve products to a picking area where a human worker picks the products needed to fulfill an order. This means that it is important for the robots to be effective when retrieving products to reduce the time the worker spends waiting in the picking area. This thesis focuses on the grouping of products in the containers when they are stored in the warehouse. The robots will then retrieve one container at a time and if the grouping of products is done correctly this should decrease the number of retrievals required to fulfill an order. In order to make the decision on which products to group together, an application was developed that data mined previous orders that the warehouse had received in an attempt to extract information about the products. With the help of this information the application then suggests different product layouts that focus on different goals when they are created. The different layouts are then compared against each other in order to determine which layout technique produces the best results. This algorithm has been named the PLO-algorithm. The results showed that when a product is placed with the PLO-algorithm, the most important aspect to consider is the relations it has with the other products it is grouped with. The results also showed that data mining orders that are too old can have a negative impact on the result if not handled correctly. The results also showed that when constructing the warehouse you should try to avoid restrictions that affect which products can be placed together as much as possible since these restrictions can impact the effectiveness of the warehouse in a negative way. The thesis draws the conclusion that there is a clear gain in effectiveness for warehouses to have a planed layout for their products. It is recommended to data mine previous orders to extract relations between the products if possible since this piece of information showed the best results in this thesis. It is also in the warehouse best interest to avoid as many restrictions as possible that affect which products can be placed together since this can impact the results in a negative way. It is also beneficial to not include data that is too old in the data mining since this can impact the results in a negative way if not handled correctly.