Workshop resource scheduling method based on heuristic optimization algorithm

Abstract

The invention relates to a workshop resource scheduling method based on a heuristic optimization algorithm, which comprises the following production scheduling steps: receiving input data, and inputting the data into the algorithm; setting constraint conditions for screening output results; the method also includes that the algorithm calculates the received data and outputs a result; the algorithm supports input of various constraint conditions, and the constraint conditions comprise minimum waiting time, minimum overdue tasks, priority priority and forced guarantee priority, wherein the constraint condition is a digital quantity, the value range is 1-5, and the default value is 3. According to the method, submitted data are processed by using a genetic algorithm, and a group of locally optimal solutions are obtained under certain constraint conditions; the delivery time accuracy is improved, the consumed resources are reduced, the time required by an enterprise to specify a production plan is reduced, and the production efficiency of the enterprise is improved.

Description

technical field [0001] The invention relates to the technical field of intelligent workshop resource scheduling and scheduling, in particular to a workshop resource scheduling method based on a heuristic optimization algorithm based on multi-objective constraint conditions. Background technique [0002] Job shop scheduling problem (JSP) is the most common scheduling type in job shop scheduling, and it is one of the most difficult combinatorial optimization problems. Processing lines, etc., so its research has great practical significance. Scientific and effective production scheduling can not only improve the efficient utilization of workers and equipment resources in the production process, but also shorten the production cycle and reduce production costs. [0003] The job shop scheduling problem is a resource allocation problem that satisfies the task configuration and sequence constraints. It is one of the most difficult combinatorial optimization problems, and it is als...

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Problems solved by technology

[0004] Because in the actual workshop production, job scheduling is one of the representative combinatorial optimization problems, which is an NP-hard problem. Since its solution space belongs to the massive level, and the feasible solutions of the massive level correspond to massive calculations, the general solution is unable to effectively obtain a better feasible solution

At present, the following problems need to be solved in manual production scheduling: there are only a handful of enterprise workers who are qualified for production scheduling positions; is a huge project

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