Automatic stereoscopic warehouse selection operation scheduling modeling and optimizing method based on Petri network and improved genetic algorithm

Abstract

The present invention relates to an automatic stereoscopic warehouse selection operation scheduling modeling and optimizing method based on a Petri network and an improved genetic algorithm, belonging to the technical field of automatic stereoscopic warehouse operation scheduling optimization analysis. The method comprises the steps of (1) establishing an automatic stereoscopic warehouse selection operation scheduling Petri network model, (2) carrying out operation scheduling optimization algorithm designing based on the combination of the Petri network and the improved genetic algorithm, (3) designing an automatic stereoscopic warehouse selection operation scheduling optimization system according to the designed operation scheduling optimization algorithm based on the combination of the Petri network and the improved genetic algorithm, comparing the designed algorithm and standard genetic algorithm solution process efficiency and optimization results, and verifying the advantages of the designed algorithm. According to the method, the automatic stereoscopic warehouse selection operation scheduling modeling and operation scheduling fast and excellent quality optimization can be realized, and the zero load invalid operation time in an automatic stereoscopic warehouse selection operation execution process is reduced.

Description

technical field [0001] The invention belongs to the technical field of automatic stereoscopic warehouse operation scheduling optimization analysis, and relates to an automatic stereoscopic warehouse picking operation scheduling modeling optimization method based on Petri net and improved genetic algorithm. Background technique [0002] In the early stage of the development of the manufacturing industry, the development of logistics did not receive due attention, resulting in larger production scales, more flexible and more automated manufacturing processes, and the backwardness of logistics leading to a more prominent contradiction between it and the manufacturing system. The important manifestation of modern logistics is automated three-dimensional warehouse, which is the trend of storage development. It can effectively improve space utilization and reduce logistics costs. It has incomparable advantages over traditional warehouses in terms of improving enterprise storage and...

AI Technical Summary

Problems solved by technology

In the early 1970s, with the use of roadway stackers, mobile shelves, rotating shelves and other handling equipment in automated warehousing, it entered the stage of automated warehousing, but the independence of the equipment caused it to become an island of automation, which could not effectively contact the system as a whole

my country's research on automated three-dimensional warehouses started late, but there is a big gap with western developed countries in terms of information processing and automation. The problems in the long-term operation of the system have gradually become prominent, and the imperfect and inefficient information processing methods will lead to automated three-dimensional warehouses. The unreasonable arrangement of warehouse operation scheduling greatly reduces the storage efficiency of the automated three-dimensional warehouse, and the cost remains high

[0005] The automated three-dimensional warehouse is a random and complex system. Especially when the system is faced with high-intensity and large-volume rapid picking operations, if the operations are only executed in order, ignoring the existing conflicts and shared resource competitions, due to the location point of the picking task target Due to the random distribution of the stacker, there is a large amount of idle running time when the stacker is executed between the previous job task and the subsequent job task, which will lead to the failure of the system to achieve the optimal running time and path of the effective job, which will greatly reduce the overall system Operational efficiency. Traditional accurate algorithms (algorithms that can find the optimal solution, such as dynamic programming algorithms, enumeration methods, etc.) or intelligent algorithms (algorithms that infinitely approach the optimal solution, Particle swarm algorithm, ant colony algorithm, simulated annealing algorithm, tabu search algorithm, genetic algorithm, etc.) to solve, the traditional accurate algorithm is difficult to deal with the exponential relationship between the solution efficiency and the problem size, it is difficult to find the optimal solution within an acceptable time Or sub-optimal solution, the intelligent algorithm infinitely approximates the optimal solution within an acceptable time, but for the discrete optimization problem such as automatic three-dimensional warehouse picking job scheduling, the effect is different, and there are problems in the early or late stage of optimization. It may lead to the deterioration of the optimization results. Therefore, in order to achieve high-quality and fast optimization within an acceptable time, the original intelligent algorithm can be designed and improved.

[0006] At present, the mathematical model of automated three-dimensional warehouse usually adopts polynomial combination, and uses standard intelligent algorithm to optimize it. There is no one on the scheduling model of automated three-dimensional warehouse based on Petri net, and the scheduling optimization based on the combination algorithm of Petri net and improved genetic algorithm. research

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