Order sequencing optimization method for logistics

Abstract

The present invention provides an order sequencing optimization method for logistics. The process of the method includes the following steps that: since the same type of commodities may be stored in a plurality of different containers, containers from which corresponding commodities in orders should be taken out are determined according to the expiration dates of the commodities in the orders, the number of remaining commodities in the containers where the commodities are located or the distances of the containers before order sequencing optimization is performed; and the orders of the commodities of which the containers are determined are optimally sequenced. According to the order sequencing optimization method for logistics of the invention, an evolutionary computation optimization method is adopted to optimally sequence the orders, so that the orders of the same commodities can be arranged at adjacent positions as much as possible, and therefore, commodities which have taken out can be repeatedly utilized, and the problem of low efficiency caused by frequent transport of the commodities can be avoided; and a constructed learning group concept is utilized to expand the search range of the algorithm, so that optimization calculation can be prevented from being trapped in local optimum, and convergence precision is greatly increased. With the order sequencing optimization method for logistics of the invention adopted, the optimal commodity picking efficiency of the commodities in the orders in a logistics process can be realized.

Description

technical field [0001] The invention relates to a logistics system for using robots to automatically access goods in a warehouse, in particular to an order sorting optimization method for logistics. Background technique [0002] At present, intelligent warehousing is based on the needs of orders. The warehouse background classifies and summarizes the orders, and then uses robots to pick out the containers where the goods are located from the storage area and transport them to the goods sorting area. Finally, the staff in the sorting area pick them up from the containers. The number of items required for the current order is sorted from the SKU of the current order, and the order is packed out of the warehouse after the order is completed. [0003] In the existing technology, a typical example of the above functions is Amazon’s kiva robot. However, since the robot takes the entire shelf at one time, and not all other items on the shelf are needed for this order, those unneces...

AI Technical Summary

Problems solved by technology

[0003] In the existing technology, a typical example of the above functions is Amazon’s kiva robot. However, since the robot takes the entire shelf at one time, an

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