Research On Truck Sequnce In A Cross Docking System Based On Post Distribution Mode

The modern logistics industry belongs to the productive service industry.It is a national key encouragement development industry.As a national economic industry,it integrates many industries such as road transportation,warehousing and information industry.It involves a wide range of fields and attracts a large number of employed people.For enterprises,in order to improve their competitiveness,they are looking for a supply chain management strategy suitable for their own development.Among them,cross-border distribution is an efficient and fast scheduling strategy.In the cross-docking system,the pre-allocation mode and the postallocation mode are classified according to whether the goods are interchangeable.The postallocation mode can shorten the waiting time of the goods in the distribution center and the waiting time of the trucks at the warehouse door,so it can greatly reduce the time cost and management cost for the enterprise and improve the system operation efficiency.Therefore,the article studies how to perform door allocation,truck sorting,and cargo distribution in the post-distribution mode to achieve the goal of minimizing completion time and minimizing cargo residence time in the cache.In the post-allocation mode,the article divides the problem model studied into the vehicle full arrival mode and the vehicle non-full arrival mode according to the situation that the vehicle arrives at the yard: 1)The cross-vehicle vehicle dispatch in the vehicle full arrival mode.In this mode,the inbound trucks and the outbound trucks have been docked at the parking lot of the cross-border distribution center,and are in a state of being scheduled at any time.The unloading and loading process of the warehousing trucks and the warehousing trucks is uninterruptible,and the cargo has loading and unloading sequence requirements,but the cargo is interchangeable;2)the over-the-ship vehicle dispatching in the vehicle non-full arrival mode.In this mode,each warehousing truck has a time to arrive at the parking lot of the crossdocking distribution center.The time for the departure of the warehouse is not required to be completed when all vehicles have been parked at the yard.The unloading and loading process of the warehousing trucks and the warehousing trucks can not be interrupted.The loading and unloading of the goods does not consider the order requirements,but the goods are interchangeable.For the first model,the mathematical model of the problem is first established according to the problem constraints,and then the genetic algorithm is designed to solve the problem.Then the test data is designed,and the parameters of the algorithm are numerically simulated at different levels,and the obtained results are obtained.Analyze.Finally,the experimental results show that under the test data designed in this paper,when the population size is 200,300 and 400,the crossover rate is 0.75,0.8 and 0.85,and the mutation rate is 0.07,0.08 and 0.09.A total of 27 combinations,the best among the results obtained was a combination of population size 300,crossover rate of 0.85,and variability of 0.08.The worst performing result was a population size of 200,crossover.The combination of a ratio of 0.75 and a variability of 0.07,but the evolutionary curves all indicate that the genetic algorithm is caught in the local optimum earlier.For the second mode,firstly,the mathematical model of the problem is established according to the problem constraints,then the improved genetic algorithm and the wolves group algorithm are designed to solve the problem,then the test data is designed,and the test data is compared with the wolf group algorithm and the improved genetic algorithm.Solve the difference between accuracy and solution time.Finally,the experimental results show that when the scale of the problem is small,the improved genetic algorithm and the wolf group algorithm are almost the same in terms of solution accuracy and solution time.When faced with large-scale problems,the accuracy of the wolf group algorithm is higher than that of the improved genetic algorithm.5%,the solution time is 11% lower.Therefore,in the face of large-scale problems,the wolf group algorithm performs better than the improved genetic algorithm.