Global optimization preprocessing method for intelligent manufacturing production scheduling
A technology for production scheduling and global optimization, applied in manufacturing computing systems, data processing applications, forecasting, etc., to achieve the effect of strengthening slack sub-problems, reducing the difficulty of optimization, and simplifying problems
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Embodiment 1
[0038] Presolver for mixed integer linear programming problems
[0039] Including the following steps:
[0040](1) Variable selection based on the Fourier-Motzkin elimination method: estimate the number of non-zero elements of the model after the Fourier-Motzkin elimination method is executed, apply the Fourier-Motzkin elimination method to eliminate variables and reduce non-zero elements, and reduce the number of non-zero elements according to the , to sort the variables, and the higher the number of cuts, the higher the priority of the variable;
[0041] (2) Redundant constraint elimination method based on Fourier-Motzkin elimination method: After eliminating selected variables based on Fourier-Motzkin elimination method, the constraint redundancy is judged through boundary reasoning. If the upper and lower bounds of the reasoning conflict or violate If the original constraint boundary is used, it can be directly determined that the original linear programming problem is no...
Embodiment 2
[0047] Search Domain Reduction for Mixed Integer Nonlinear Programming Problems
[0048] Including the following steps:
[0049] (1) Deriving optimality conditions: deriving optimality conditions through the monotonic characteristics of the objective function and constraints;
[0050] (2) Search domain reduction: through the above optimality conditions, as a set of constraints that the optimal solution must satisfy, and filter the solution domain that satisfies these constraints, and use it as the reduced search domain; the search domain reduction includes outputting Single-constraint search domain reduction, multi-constraint search domain reduction;
[0051] The narrowing of the search domain includes the following steps:
[0052] 1) Select a variable, calculate the gradient boundary of the objective function and constraints for the variable in a forward direction, and if the gradient boundary obtained by the forward calculation meets the optimality condition, the algorithm...
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