### Optimization of Warehouse Picking Based on TSP

#### Abstract

*Modern warehouse management has entered the era of information intelligence, which requires the establishment of a set of accurate, reasonable and efficient warehouse management methods, to improve the efficiency of workers. *

*In this paper, a series of TSP (Traveling salesman problem) algorithms are used to further optimize the process efficiency of goods removal from the warehouse. Based on Freudian algorithm, the distance problem between cargo grid and **re**check **station** was solved in the first problem. Founded on the genetic algorithm in TSP, specific application problems were solved in two, three and four questions. *

*For problem 1, by observing the relation between the coordinates given and the actual route, all the coordinates of cargo grid were divided into two kinds: odd and even columns, and the two kinds of coordinates were processed, respectively. After coordinate processing, the distance matrix between each lattice can be obtained by using Freudian algorithm. At the same time, due to the small number of **re**check stations and their regular distribution, the distance matrix between each **re**check station and each cargo grid can be obtained by manually classifying the coordinates of the **re**check stations and applying Freud algorithm again. Output all the matrices in the same EXCEL table, the distance matrix between the 3013 elements can be obtained. (See Appendix 1 for the distance matrix and Appendix 1 for the algorithm). *

*For problem 2, this problem is a unidirectional TSP problem by macro analysis**.** Firstly, the distance matrix of the required point was called from the solution of problem 1, which was imported into LINGO program to establish 0-1 decision variables, and the objective function is established to solve according to the principles of single-direction connection and loop-breaking, to obtain the connection sequence and loop distance. Select the cargo lattice back to the initial **re**check **station** and connect it with the nearest **re**check **station**, to complete the task of breaking the ring, solving the connection sequence and the total distance length. The outbound time can be divided into three parts: (1) journey time; **(2)** Pick up time; **(3)** Packing time, group calculation, sum can get the result. The total distance is 382.5m (about 1254.92 ft) and the total minimum time is 462 seconds (about 7 and a half minutes). *

*For problem 3, **d**istance matrix of the required point was called from the solution of problem 1, and imported into LINGO program, afterwards, 0-1 decision variable and objective function were established according to the principles of single-direction connection and loop-breaking to solve, and then the connection sequence and loop distance was obtained. Select the cargo lattice back to the initial **re**check **station** and connect it with the nearest **re**check **station**, to complete the task of breaking the ring, solving the connection sequence and the total distance length.** **Compared with Question 2, Question 3 specifies the available recheck **station** and the total time needed to complete the shipment. During the calculation of each task order, it was classified and discussed (double-starting point operation), and the overall optimization was carried out according to the starting point and end point of each task order, and the total shortest time was 2288.6 seconds (about 38 minutes) (See the attachment for the results of this question). *

*For question 4, queuing theory is needed. To reduce the total outbound time, it is necessary to give priority to orders with abbreviated time. Firstly, the shortest outbound time of 49 task orders should be calculated, and the time should be arranged in order from short to long. After that, according to the order of the task list, nine pickers pick goods in a certain order. Hence, a variable should be introduced to preserve the status of the recheck table and the remaining time currently occupied.*

#### Full Text:

PDFDOI: https://doi.org/10.22158/asir.v6n2p85

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Copyright (c) 2022 Yuhan Wang, Yanshan Liao, Junhong Kang, Yu Dang, Zixin Jiang

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