Automobile part color spraying production schedule design method, system and equipment based on 0-1 planning

Abstract

The invention discloses an automobile part color spraying production scheduling method based on 0-1 planning, and the method comprises the steps: obtaining color spraying equipment and color spraying color data, and carrying out the preprocessing of the obtained data; establishing a decision variable and an intermediate variable; establishing constraint conditions; establishing a target function and a single-target optimization model; and solving the single-target optimization model. According to the method, the most basic condition of event occurrence is selected as a decision variable, and a proper intermediate variable is established for meeting each condition. The decision variable and the intermediate variable serve as the basis of the model, an objective function is established according to actual requirements, a constraint equation is established according to condition limitation, a single-objective optimization model based on 0-1 planning is established, the model can obtain an optimal solution, and the model precision is high.

Description

technical field [0001] The invention relates to the technical field of intelligent manufacturing, in particular to a method, system and equipment for designing a color matching production schedule for auto parts based on 0-1 planning. Background technique [0002] There are many auto parts manufacturers in China, and it is extremely critical to study the production schedule design in the auto parts manufacturing industry. Spare parts manufacturers need to work out a better production plan based on their own production needs, combined with the actual production situation, and fully consider various constraints to achieve the goal of minimizing waste of resources. To achieve this goal, various models and algorithms have been developed. [0003] At present, the research of auto parts production scheduling problem mainly focuses on two aspects: intelligent algorithm and mathematical model. For example, based on the intelligent algorithm to solve the production scheduling probl...

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Problems solved by technology

[0004] (1) The optimal solution is not easy to find

[0005] With the occurrence of intelligent algorithms, the global search algorithm can theoretically find an optimal solution, however, due to the limitations of practical constraints, based on the established objective function, the optimal solution is often difficult to find

[0006] (2) Low model accuracy

The accuracy of the model thus established is low, and although the obtained results can meet some conditions and the results are better, they are not the optimal solution.

[0008] (3) The solution speed is slow

[0009] At present, some algorithms try to search all possible situations in order to get the best results, which leads to slow solution speed and low efficiency of the model

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