Study On Optimization Algorithms Of Cigarette Logistics Automatic Sorting And Replenishment

The constant reform of China's tobacco industry makes tobacco companies dramatically develop tobacco logistics distribution.As a core link of logistics distribution,tobacco sorting operation is directly related to the work level and efficiency of tobacco logistics distribution.Therefore,combining with the practical conditions of domestic sorting system,an scientific and reasonable processing method of intelligence and informatization has to be implemented to optimize logistics sorting operation of tobacco,so as to improve the quality of sorting operation,reduce the cost of logistics distribution and increase work efficiency of distribution.This paper firstly researched the current situation of domestic existing sorting system,analyzing the difficulties of sorting operation and the strategies for sorting and major analytical methods for orders,and exploring the categories of common sorting systems in sorting center.Then,this paper proposed an idea of simulating automatic sorting operation as the queue of “virtual container”,establishing an algorithm model for tobacco automatic sorting control.Afterwards,the platform Labview2014 was utilized to compile model into a set of automatic sorting control system,conducing sorting operation for practical tobacco orders in the city.In the system,we could manually set up order interval and the speed of belt for tobacco,thus presenting the influence of different parameters on the efficiency of sorting operation.Through plenty of practice,it was proved that this system had high accuracy,with the capacity to conduct well-organized sorting operation.Then,this paper,combining with the problems in replenishment of practical tobacco distribution,optimized the permutation and combination of actual orders on the foundation of sorting control to release supply pressure in sorting center.To begin with,through analyzing the influence of sequence of different orders on the planning of replenishment signal,the paper designed three mathematical models of time signal for replenishment,that is,time optimization model,interval optimization model and peak optimization model.Then,taking three models as the objective function,orderoptimization problem was transformed into discrete TSP problem,solving the objective function by genetic algorithm and particle swarm optimization.Finally,actual customers' orders were invoked in Matlab simulation platform to conduct data analysis and comparison of simulating optimization results of two intelligent optimization algorithm.It was proved that the performance of particle swarm optimization was relatively excellent and fitness value of objective function was more low,which was good for optimizing replenishment ability,achieving the effect of global optimal.Besides,the most even order sequence was obtained depending on the demand signal in replenishment time.