Loading And Unloading Process And Scheduling Optimization In AS/RS Based On Polychromatic Sets Theory

With the rapid development of economy,the automated warehouse has been widely used in China.Automated warehouse not only being regarded as an important link in the modern logistics system,but also it is the major storage form that is widely used in modern manufacturing and automation production line.The storage system as the central link in the whole logistics,it should ensure the scientific operation and processes in warehouse to meet the requirements of the stock goods turnover;at the same time,the storage system is designed to guarantee the resource distribution in warehouse to improve the efficiency of the logistics network within the warehouse;in additional,it is also expected to optimize the output of storage scheduling,aiming to achieve the benefits of the logistics system.According to the above background,this thesis aims to study the optimization and dispatch of the automated warehousing and storage process.Firstly,a multi-color set hierarchical structure modeling method is used to study the modeling and optimization of storage process,and the optimizing measures are put forward according to the analysis of performance index.A new outbound flow model is established,which is compared with the model's expected execution time: rationalit y and feasibility.Based on the optimization of the inbound and outbound process,the polychromatic set relation rule model is established by using the polychromatic set theory in order to achieve the resource allocation within each activity note,and it will also contribute to improve the efficiency and scientific of resource utilization in warehouse.Finally,a multicolor hybrid genetic algorithm is proposed to solve the joint storage scheduling problem.And the initial population quality is improved by using the enclosing matrix;the simulated annealing algorithm is used to jump out of the locality optimum in the iterative process to obtain the best solution.The effectiveness and superiority of the algorithm is verified by comparing the basic genetic algorithm and the multi-color genetic algorithm with the multicolor hybrid genetic algorithm.