Flexible workshop robustness scheduling method based on decomposition multi-target evolution algorithm

Abstract

The invention discloses a flexible workshop robustness scheduling method based on a decomposition multi-target evolution algorithm. The method comprises the following steps: 1, reading such input information as operation, machine attributes and the like of a flexible operation workshop, defining an optimization object, and setting constraint conditions; 2, initializing parameters of the algorithm; 3, determining an adjacent domain of each subproblem, generating an initial parent group, and determining all Pereto non-dominant solutions from the initial group so as to form an external memory; 4, generating a child group, carrying out mating selection, breeding child individuals by use of an adaptive variation operator and a restoration-based intersection operator, and updating the external memory; 5, by use of the generated child group, updating a current optimal individual of each subproblem, and forming a new parent group; and 6, when it is determined that the individual object evaluation frequency reaches the maximum, outputting the external memory, i.e., a group of Pareto non-dominant flexible operation workshop scheduling solutions, and if the frequency does not reach the maximum, skipping to the fourth step. According to the invention, scheduling tasks in a flexible operation workshop can be rapidly and efficiency realized.

Description

technical field [0001] The invention relates to the technical field of flexible job shop scheduling control, in particular to a flexible job shop robust scheduling method based on decomposition multi-objective evolutionary algorithm. Background technique [0002] The flexible job shop scheduling problem is a generalization of the classic job shop scheduling problem, and it is a kind of NP-hard problem. In the flexible job shop scheduling problem, one process is allowed to be processed by multiple machines. Therefore, it is necessary to allocate appropriate machines for each process of each job, and determine the processing sequence of processes on each machine to meet various constraints. Under the premise of achieving the shortest completion time of the job, the load balancing of each machine and other optimization goals. [0003] There are many uncertain factors in the production environment of the actual flexible job shop, such as changes in the processing time of some p...

AI Technical Summary

Problems solved by technology

They assume that all the information in the flexible job shop is known in advance and fixed. Obviously, when there are uncertain factors in the actual production environment, the scheduling scheme generated by the static method is no longer applicable.

[0007] (2) The processing method for multiple optimization objectives is relatively simple

[0008] (3) The local search ability is weak, it is easy to fall into local optimum, and the scheduling efficiency is low

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