Research On Job Scheduling Strategy In Intelligent Warehouse System

Intelligent warehousing system is an important part of modern warehousing logistics.In recent years,with the rapid development of e-commerce and logistics industry,higher and higher requirements are put forward for the automation level and operation ability of the warehousing system.Traditional warehousing systems that rely on manual picking have gradually been unable to meet the requirements of modern warehousing demand.The "goods-to-man" system represented by the Kiva system is a new type of intelligent warehousing system.It uses robots to replace manual picking tasks in the warehouse,freeing warehouse personnel from the heavy work of finding goods,which greatly improve the picking efficiency of the warehouse.In this thesis,intelligent warehousing system is taken as the research object,and two key problems,task scheduling and storage allocation,which affect the efficiency of warehousing,are studied.The main contents are as follows:The thesis first summarizes the research status of task scheduling and storage allocation in the warehousing system,then gives a brief overview of the work scene and work flow of intelligent warehousing,analyzes the characteristics of job in the intelligent warehousing system,and then introduces the general theory and common methods of task scheduling and storage allocation,introduces various algorithms used in the follow-up research,which lays a foundation for the subsequent research work.Task scheduling is an important guarantee to give full play to individual ability and improve system efficiency.Aiming at the problems of uneven task allocation and unreasonable task sequence in intelligent warehousing system,this thesis designs the dual optimization objectives of minimizing the total distance and time-consuming of the system from the actual scene.A task allocation method was designed based on auction method,and the distance and time weighted bid calculation method was proposed,and then combined with the improved genetic algorithm to optimize the task sequence,realizing a task scheduling algorithm combining the auction method with the improved genetic algorithm.Finally,the simulation results show that the task scheduling algorithm can effectively improve the efficiency of the warehousing system.Storage allocation is an important means to save system resources and improve system efficiency.At present,fixed storage allocation is usually used in the "goods to man" system,which limits the improvement of system picking efficiency.This thesis proposes a dynamic storage allocation method suitable for intelligent warehousing system.First,this thesis analyzes the general flow of storage allocation in the intelligent warehousing system,and establishes the optimization mathematical model and objective function of storage allocation based on the grid model.then combined with constraints,a storage allocation algorithm based on tabu search is designed.Finally,through experimental simulations,it is verified that the storage allocation algorithm can reduce the shelf transportation distance and improve picking efficiency.