Staff scheduling method and device in warehouse

A personnel and warehouse technology, applied in the field of personnel scheduling and scheduling in the warehouse, can solve problems such as not being able to meet the real-time requirements of warehouse scheduling tasks, and achieve the effect of improving the convergence rate and robustness of the algorithm

Pending Publication Date: 2021-12-07
BEIJING JINGDONG ZHENSHI INFORMATION TECH CO LTD
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  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In view of this, the embodiment of the present invention provides a method and device for personnel scheduling in a warehouse to solve the technical problem that cannot meet the real-time requirements of warehouse scheduling tasks

Method used

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  • Staff scheduling method and device in warehouse
  • Staff scheduling method and device in warehouse
  • Staff scheduling method and device in warehouse

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Embodiment Construction

[0078] Exemplary embodiments of the present invention are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present invention to facilitate understanding, and they should be regarded as exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

[0079] Although the warehouse operation model is complex, it is essentially a combinatorial optimization problem. Specifically, it is a multi-objective optimization problem of scheduling-cost minimization under certain constraints. The embodiment of the present invention introduces a reinforcement learning method, and inputs the initial personnel scheduling sequence into ...

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Abstract

The invention discloses a staff scheduling method and device in a warehouse, and relates to the technical field of computers. A specific embodiment of the method comprises the following steps: acquiring an initial personnel scheduling sequence, wherein each element in the personnel scheduling sequence represents the number of personnel corresponding to each time node; based on a deep reinforcement learning neural network, simulation calculation and a simulation evaluation function, optimizing the initial personnel scheduling sequence to obtain a trained deep reinforcement learning neural network, and outputting an optimal personnel scheduling sequence; and scheduling personnel scheduling in a warehouse according to the optimal personnel scheduling sequence. According to the embodiment, the technical problem that the real-time requirement of a storage scheduling task cannot be met can be solved.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a method and device for staff scheduling in a warehouse. Background technique [0002] With the continuous increase of labor costs and logistics costs in modern society, for warehousing business, it is difficult to accurately evaluate the efficiency of warehouse operations, which makes it difficult to control the cost of production according to plan, and it is difficult to guarantee timeliness. It can only be achieved by investing a lot of manpower. Solve, but this will lead to further increase in labor costs. [0003] In the course of realizing the present invention, the inventor finds that there are at least the following problems in the prior art: [0004] Without the support of a simulation system, on-site personnel scheduling management completely depends on the experience of warehouse managers, and it is difficult to obtain the optimal solution by means of traditional li...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06N3/04G06N3/08
CPCG06Q10/06311G06N3/08G06N3/045
Inventor 白悦辉张峰王尊梁
Owner BEIJING JINGDONG ZHENSHI INFORMATION TECH CO LTD
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