Same-type parallel machine production scheduling method considering moment information indeterminacy

Abstract

The invention provides a same-type parallel machine production scheduling method considering moment information indeterminacy, and belongs to the field of production scheduling and operational research. The method comprises the steps of firstly building a distribution set robust optimization model DR-PMSP-MU considering the moment information indeterminacy, and obtaining an expression of an initial model DR-PMSP-MU1 comprising an inner layer problem and an outer layer problem; converting a decision variable of the model DR-PMSP-MU, solving the inner layer problem, and equivalently converting the model DR-PMSP-MU1 into a solvable model DR-PMSP-MU2; and taking an optimal solution of the model DR-PMSP-MU2 as an optimal solution of the whole model, wherein the optimal solution corresponds to multiple optimal production scheduling schemes, and a decider can perform selection as needed. The built model better meets an actual production condition; and by utilizing more information in a production environment, the decision-making risk can be lowered under the condition of ensuring system performance.

Description

technical field [0001] The invention belongs to the fields of production scheduling and operations research, and in particular relates to a production scheduling method for parallel machines of the same type considering the uncertainty of moment information, which can be obtained by sampling and estimating historical data when the workpiece processing time has random uncertainty. A robust scheduling scheme with strong fault tolerance. Background technique [0002] The same-type parallel machine scheduling problem considers that a batch of workpieces are processed on a series of identical machines, each machine can only process one workpiece at the same time, and each workpiece can only be processed by one machine at the same time. Each workpiece has its specific processing time, and the scheduling problem is to find a scheduling scheme to assign all workpieces to the corresponding machines for processing in a certain processing order, so that the performance index of a certa...

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3) This kind of stochastic model is usually NP-hard, and can only be solved by heuristic algorithm or dynamic programming algorithm. As the scale of the problem gradually expands, the difficulty of solving the stochastic scheduling model will increase exponentially

However, because only the boundary information of the workpiece processing time variation range 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 parameters System performance under normal conditions

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