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.
Automated Storage and Retrieval System storage allocation rusting multi-objective optimization Pareto optimality Genetic Algorithm (GA)
Shu-an Liu Qing Wang Ling Jin
with Faculty of Information Science and Engineering,Northeastern University,Shenyang,China,110004
国际会议
2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)
四川绵阳
英文
3912-3917
2011-05-23(万方平台首次上网日期,不代表论文的发表时间)