Resource allocation global optimization method of intelligent scheduling system

Abstract

The invention relates to a resource allocation global optimization method of an intelligent scheduling system. The resource allocation global optimization method is implemented by adopting a new genetic algorithm. The resource allocation global optimization method considers the difference between a full-load driving path and a no-load driving path of an AGV, adds a conversion coefficient alpha before the no-load driving path, the value of the conversion coefficient alpha ranges from 0 to 1, and establishes a new mathematical model for AGV global optimization scheduling f=min max{d1,d2,...,dn}.Based on the improvement of a crossover operator, a best-worst crossover mode is proposed on the basis of a hybrid crossover mode combining two-point crossover and BCBRC, thus the problem that a non-feasible solution is prone to generate due to changes of chromosome coding rules is avoided; and a variation pattern of gene segment random exchange is adopted, thereby avoiding the problem that the conventional variation mode is prone to generate a non-feasible solution containing the same nodes when a real number system coding method is adopted.

Description

technical field [0001] The invention belongs to the technical field of AGV operation scheduling, and in particular relates to a global optimization method for resource allocation of an intelligent scheduling system. Background technique [0002] The resource allocation problem is involved in the intelligent logistics scheduling system, and the resource mainly refers to the automatic guided vehicle (Automated Guided Vehicle, AGV), that is, the optimal scheduling problem of the AGV trolley in the sorting warehouse. As a flexible and efficient conveying equipment, AGV trolley has been widely promoted and applied in manufacturing systems, storage systems and other fields. The optimal scheduling problem of AGV cars is generally defined as organizing an appropriate driving sequence for a series of loading points or unloading points, so that the AGV cars can pass through in an orderly manner. Time, vehicle capacity limit, mileage limit, time limit, power level, etc.), to achieve a...

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Problems solved by technology

Wang Guoxin et al. proposed a method combining discrete simulation and Branch and Bound Algorithm (BBA) for the optimization of single AGV task scheduling in the manufacturing system, but this method has many iterations; Luo Jian et al. problem, establish a single AGV scheduling mathematical model, and use an improved quantum particle swarm optimization algorithm (QuantumParticle Swarm Optimization, QPSO) to solve the model, but does not consider the multi-task scheduling problem; Nishi et al. AGV scheduling model, a decomposition algorithm is proposed to solve

However, most of them focus on the scheduling problem of single AGV and single task. The present invention studies the scheduling problem of multiple AGV cars, and proposes a new genetic algorithm to realize the global optimal scheduling of multiple AGV cars.

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