An optimization model for storage location problem in the automated storage and retrieval system

As metal products, bearings are prone to rust provided in storage for a long time. To improve products quality on bearing rusting during storage, this paper proposes a concept of satisfactory level on bearing rusting and designs the expressing function. With regard to the stock-in operations, a multi-objective optimization model is proposed for allocating storage at the time of stock-in, so as to maximize satisfaction on rusting and minimize the energy consumption during a storage period of bearings. In terms of the stock-out operations, another multi-objective optimization model is designed for retrieving bearings at the time of stock-out, so that satisfaction on rusting is maximized and the time consumed is minimized. Considering the complication of solving 0-1 multi-objective integer programming models, Genetic Algorithm is applied. According to the problem features, two approaches of special chromosome representations, one-point mapping-based crossover operator and displacement mutation operator are designed for stock-in and stock-out models, respectively. The fitness function is designed with adaptively moving line technique. Furthermore, the designed algorithm is embedded with the process of obtaining Pareto optimality. The simulation experiments show that the rusting might impact on the storage allocation in stock-in and even more significantly in stock-out. The experimental results testify the models effectiveness and the algorithm practicability.