Optimization of Cargo Handling Equipment at the Airport

Delaying an aircraft during ground handling costs the airline a lot of money. In critical situations, it is even possible that some flights have to be canceled due to delays. It is therefore the endeavor of all personnel involved in ground operations that they proceed without delay. This paper shows what methods can be used to optimize the required number of handling equipment and what can affect its number

Design of Warehouse for Material, Which Is Non-uniform and Difficult to Handle

Main goal of this paper is warehouse layout optimization with emphasis on maximum space utilization. It is about rack system design, which leads to capacity maximization of warehouse for non-uniform material with heavy weight. The warehouse system has to enable at least partial automation (the possibility to use AS/RS system), minimization of service time and minimization of man effort. Initial constraining conditions for warehouse design in our case are warehouse area, maximum load of racks or shelves, maximum allowed floor load and existing location of arrival road for material loading and unloading

Optimization of Skus\u27 Locations in Warehouse

Many companies, which deal with warehousing, optimize their warehouses. The main business of these companies is to offer to customers the warehousing services. It is possible to note that these companies are specialized in warehousing. On the other hand, we can find companies, which have as the main business the selling of goods to customers. These companies use the warehouses too but in a little bit different way. The arrangement of their warehouses can be very unsuitable and convenient for optimization. Our paper is focused on optimization of stored goods’ locations based on market basket analysis. The solution of this task is known as well but not so many authors deal with it. A common problem for many companies is to find sets of products that are sold together. As the source of these information the history of sales transactions is used. The process of the data preparation is mentioned in this paper. The steps described in this paper are applied on real retail store

Stacked structure learning for lifted relational neural networks

Lifted Relational Neural Networks (LRNNs) describe relational domains using weighted first-order rules which act as templates for constructing feed-forward neural networks. While previous work has shown that using LRNNs can lead to state-of-the-art results in various ILP tasks, these results depended on hand-crafted rules. In this paper, we extend the framework of LRNNs with structure learning, thus enabling a fully automated learning process. Similarly to many ILP methods, our structure learning algorithm proceeds in an iterative fashion by top-down searching through the hypothesis space of all possible Horn clauses, considering the predicates that occur in the training examples as well as invented soft concepts entailed by the best weighted rules found so far. In the experiments, we demonstrate the ability to automatically induce useful hierarchical soft concepts leading to deep LRNNs with a competitive predictive power

Learning predictive categories using lifted relational neural networks

Lifted relational neural networks (LRNNs) are a flexible neural-symbolic framework based on the idea of lifted modelling. In this paper we show how LRNNs can be easily used to specify declaratively and solve learning problems in which latent categories of entities, properties and relations need to be jointly induced