Multi-load AGV task scheduling method in intelligent storage environment

Abstract

The invention discloses a multi-load AGV task scheduling method in an intelligent storage environment. The method comprises the following steps: obtaining warehouse-in and warehouse-out information; performing population initialization to obtain an initial population with N individuals; performing crossover and mutation operation on individuals in the initial population to generate offspring individuals, and combining the offspring individuals with parent individuals to form a second population with 2N individuals; according to a preset fitness function, dividing individuals in the second population into different levels of dominable layers; crowding degree calculation is carried out on all individuals in the Mth-level dominable layer of the second population, and a crowding distance is obtained; reserving individuals with large crowding distances in the current dominant layer, and forming a third population with N individuals together with the individuals in the previous (M-1) levels of dominant layers; and performing iteration for multiple times, and determining an optimal cargo scheduling scheme according to an iteration result. According to the method, task scheduling of the multi-load AGVs in the intelligent storage field is facilitated, the reasonable number of AGVs and the reasonable AGV goods taking sequence are obtained, and the intelligent storage performance is improved.

Description

technical field [0001] The invention belongs to the technical field of intelligent warehousing, and in particular relates to a multi-load AGV task scheduling method in an intelligent warehousing environment. Background technique [0002] Automated Storage and Retrieval System (AS / RS, Automated Storage and Retrieval System) based on AGV (Automated Guided Vehicle) has become an effective and competitive solution for suppliers and distributors, and AGV has been widely used to execute Store or retrieve tasks. Specifically, an AGV designated according to the system scheduling starts from the original position, arrives at the designated station according to the planned route, provides delivery or pick-up operations, and finally returns to the warehouse or predetermined location. In the highly competitive intelligent manufacturing industry, AGV can provide end-to-end warehousing services with better control and execution capabilities, and has the advantages of efficient storage an...

AI Technical Summary

Problems solved by technology

[0004] However, scheduling optimization for multi-load AGVs is far from being fully understood, and many interrelated elements are simplified or even ignored, such as

Route conflicts, the number of AGVs, and the load decision of each AGV, etc., and these factors will have a greater impact on system performance, energy consumption, economic costs, etc.

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