Shop Scheduling System and Its Working Method Based on Adaptive Non-Dominated Genetic Algorithm

Abstract

The invention discloses a workshop scheduling system based on an adaptive non-dominated genetic algorithm and a working method thereof. The system includes a senior administrator management module and an ordinary administrator module. The senior administrator management module is responsible for maintenance and decision. The ordinary administrator module is responsible for operating orders. Production management information is managed systematically through a resource allocation function and scheduling management, so that the system can be used both in static small-scale production workshop scheduling and in multi-batch dynamic production workshop scheduling, and the problem that the scheduling algorithm is of poor universality and is severely limited in application target in the prior art is solved. A senior administrator can set work pieces corresponding to products and processing procedures corresponding to work pieces in a production workshop before scheduling. The problem that the existing research algorithm converges slowly is solved. The invention provides a highly accurate and efficient scheduling system for users, which ensures the practicability of research and makes scheduling more practical.

Description

technical field [0001] The invention relates to a workshop production scheduling technology, in particular to an application system work platform and method for multi-objective workshop scheduling based on an improved self-adaptive non-dominated sorting genetic algorithm, which belongs to the technical field of genetic algorithms and workshop scheduling management. Background technique [0002] With the development of enterprise modernization, the production efficiency of workshop scheduling is a key factor for enterprises to compete globally in the market. Before the popularization of computer applications, enterprises adopted traditional manual work methods, which caused many problems in the production, operation and management of enterprises, and it was difficult to improve the market competitiveness and comprehensive economic benefits of enterprises. In view of this, the enterprise has introduced advanced information and scientific management technology, and through the ...

AI Technical Summary

Problems solved by technology

After investigating the current situation of the enterprise and reviewing a large number of documents, a number of problems in the production scheduling mode of the enterprise are summarized: unreasonable resource allocation, resulting in waste caused by unbalanced consumption of workshop equipment; the function allocation in the management system is not detailed enough The responsibilities of management personnel are not clear, resulting in system information not being updated in time and resulting in waste of production resources

[0004] 1. The scheduling algorithm has poor versatility

The current multi-objective workshop scheduling system is only suitable for a specific workshop environment, and its goals are only for the shortest processing time, the smallest machine load, etc.

[0005] 2. The current research algorithm for multi-objective workshop scheduling problem has a slow convergence speed and poor real-time performance for data processing

The scheduling model has not considered the actual situation of complete real production scheduling. There are certain errors between the simulated results and the actual situation, which cannot completely solve the actual scheduling problem of the workshop.

[0006] 3. In the research of optimization indicators, most of them are based on performance indicators, and seldom research on cost indicators, which cannot provide users with a highly accurate and efficient scheduling system

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