Intelligent warehousing optimization method based on game theory

Abstract

A method provided by the invention is an intelligent warehousing management and control optimization method based on the game theory in a complex scene. A satisfactory optimization degree can be obtained under the condition of centralized tasks in a complex scene. The method can be widely applied to the optimization of warehousing strategies in various scenes based on a robot transport vehicle, the inventory management based on commodity purchase and sale data, the realization of intelligent management, and the warehouse daily management business such as warehousing-in / out and material inventory. Multi-objective strategy combination optimization is carried out based on the inventory management data and the game theory, and operation instructions are provided by an information system. The labor intensity of workers is reduced. The efficiency of warehousing is improved. The execution equipment in the warehouse is reduced. The flexibility of warehouse operation is improved. It is hoped that intelligent management and optimal scheduling of warehousing can be realized and a scientific decision-making basis can be provided for intelligent logistics management.

Description

technical field [0001] The method of the present invention is an intelligent storage management and control optimization method based on game theory in a complex scene, and can obtain a satisfactory degree of optimization when the task amount is concentrated and the scene is complex; The optimal problem of warehousing strategies in various scenarios, inventory management based on commodity purchase and sale data, realizes intelligent management, daily warehouse management operations such as outbound, inbound, and material inventory; multi-objective based on inventory management data combined with game theory Strategy combination optimization, using the information system to provide operating instructions; reducing the labor intensity of operators; improving the efficiency of warehousing operations; reducing the workload of labor in the warehouse; improving the flexibility of warehouse operations; Logistics management provides scientific basis for decision-making. Background ...

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Problems solved by technology

Among them, the task scheduling algorithm is the core of the intelligent storage system. Some scholars have used GOOPN and OOCPN to study automatic transportation scheduling and other issues, but because there is no time constraint, the system cannot be dynamically analyzed; in addition, some scholars have studied multi-objective path optimization methods. , the path optimization technology is mainly to deal with minimizing the time for the moving target to reach the destination to achieve the most efficient planning; but with the increase of the warehouse scale, the increase of transportation equipment, and the increase of the structural complexity of the scene, this local optimal It is difficult to achieve the optimal goal of the global strategy by optimizing the global optimal method; therefore, formulating the optimal scheduling strategy according to the scene environment and the current state of the transport robot is the key point to improve the work efficiency of intelligent warehousing

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