Customer demand-oriented scheduling optimization method in cloud logistics mode

Abstract

The invention discloses a customer demand-oriented scheduling optimization method in a cloud logistics mode, which belongs to the technical field of cloud logistics and comprises the following steps:S1, acquiring demand information of a client; S2, collecting distribution vehicle information of the server; S3, calling real-time traffic big data information; S4, scanning customer demand information, distribution vehicle information, real-time traffic big data and historical operation record information, and setting constraint conditions; and S5, according to the actual situation, arranging thelogistics requirements of the client to the logistics vehicle for transportation, so that the total cost is the lowest after all the requirements are arranged. According to the invention, delivery time requirements, unloading requirements and unloading time of customers and whether scenes such as going upstairs are included are fully oriented; the real-time map big data and the business data accumulated by the platform are called, and the optimal path is searched through big data mining analysis based on customer requirements and big data records, so that the customer distribution requirements are met, the distribution timeliness is improved, and the distribution cost is reduced.

Description

technical field [0001] The invention relates to the technical field of cloud logistics, in particular to a scheduling optimization method oriented to customer needs under the cloud logistics mode. Background technique [0002] With the continuous development of new retail and fresh food e-commerce, urban distribution, as an emerging logistics service method, has developed rapidly this year. A reasonable and efficient urban distribution dispatch system has very important positive significance for improving transportation capacity, developing modern logistics, integrating urban transportation resources and protecting urban environment. Since urban distribution scheduling needs to consider many factors, such as the customer's related services and delivery time requirements, it is necessary to consider the cost and efficiency of transportation, and consider the reasonable loading and route planning of the transportation process from the pick-up location to the customer's deliver...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve the pain points of traditional manual scheduling, utilize the advantages of the cloud logistics service model, face customer needs, accumulate a large amount of delivery, in-transit, unloading and customer demand information, and fully consider urban distribution in the big data environment In order to plan loading information and distribution routes, provide intelligent decision-making for dispatching based on the needs of customers, vehicle length and model, nuclear load and volume information, traffic restrictions related to urban road traffic, traffic accidents, and weather conditions, etc.

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