A Production Scheduling Method Based on Distributed Set Robust Parallel Machine Scheduling Model

Abstract

The invention, which belongs to the field of production scheduling and operations research, provides a production scheduling method based on a distributed set robust parallel machine scheduling model.According to the method, a distributed set robust optimization model DR-PMSP-RA having a risk aversion characteristic is constructed; on the basis of an objective function and a constraint conditionof the model, an expression of an initial model DR-PMSP-RA1 is obtained; the objective function of the DR-PMSP-RA model is transformed to obtain an estimation upper bound of the objective function, the initial model is transformed into an estimation model DR-PMSP-RA2, wherein the estimation model can be decomposed into two independent sub models, the two sub models are solved and the optimal sub model solution is an optimal solution of the model, and thus an optimal production scheduling plan is obtained. The model established based on the method conforms to the actual production situation well; and with information in a production environment, the decision-making risk is reduced under the circumstance that the system performance is guaranteed.

Description

Technical field [0001] The invention belongs to the field of production scheduling and operations research, and in particular relates to a production scheduling method based on a distributed set robust parallel machine scheduling model. Considering that the workpiece processing time has random uncertainty, the robust scheduling scheme with the least risk is sought . Background technique [0002] The manufacturing industry occupies a very important position in the development of my country's national economy, and its development status directly affects my country's comprehensive strength. Production scheduling is an important key technology of the manufacturing system, which aims to ensure the efficient and orderly progress of the manufacturing process. Designing reasonable planning and scheduling strategies for the production process can effectively shorten the production cycle of products, increase the rate of on-time delivery, improve equipment utilization and reduce inventory...

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Problems solved by technology

3) The solution of this kind of random scheduling model is usually NP-difficult, and generally can only be solved by heuristic algorithm or dynamic programming algorithm. As the scale of the problem gradually expands, the difficulty of solving the random scheduling model will increase exponentially

However, because only the boundary information of the variable range of uncertain parameters is used, and the system performance in the worst case is mainly considered, the decision obtained by this robust scheduling model based on the uncertainty set may be too conservative, sacrificing the parameter System performance under normal conditions

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