Improved genetic programming algorithm optimization method for resource-constrained multi-project scheduling

Abstract

The invention discloses an improved genetic programming algorithm optimization method for resource-constrained multi-project scheduling. The method comprises the following steps: step 1, initializingparameters, a function set and an attribute set; step 2, collecting project set data under different working conditions, and decomposing the project set data into a training set and a test set; step 3, extracting project information in each working condition project set in the training set as training input, extracting a function set and an attribute set as coding bases, and training populations in the improved genetic programming algorithm; step 4, judging whether the maximum working condition number of the training set is reached or not, if so, outputting an optimal solution set in the population, and if not, returning to the step 3 after converting the project set; step 5, testing the optimal solution set output in the step 4 by adopting a test set and a training set. According to the method, the resource-constrained multi-project scheduling problem under the single / multiple targets can be solved, the defect that traditional genetic programming is prone to falling into local optimumis overcome, and the searching and training capacity of genetic programming is improved.

Description

technical field [0001] The invention relates to the technical field of intelligent optimization algorithms, in particular to an improved genetic programming algorithm optimization method for resource-constrained multi-item scheduling. Background technique [0002] Resource Constrained Project Scheduling Problem (RCPSP) is the most classic and core NP-hard problem in project management. However, the resource-constrained project scheduling problem is not fully applicable to many complex practical application scenarios, and it needs to be extended from different aspects, such as the optimization of resource-constrained project scheduling problems under multi-objectives, the optimization of multi-mode resource-constrained project scheduling problems, etc. Among them, the resource constrained multi-project scheduling problem (Resource Constrained Multi-project Scheduling Problem, RCMPSP) is one of the most widely extended models. In recent years, for resource-constrained multi-i...

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Problems solved by technology

[0003] Although the meta-heuristic intelligent algorithm has good optimization and search capabilities, it has the following disadvantages: (1) With the expansion of the problem scale, the stability of the solution accuracy becomes worse, the scheduling results fluctuate greatly and it takes a lot of iteration time, Scenarios that are not conducive to frequent scheduling and dynamic scheduling

(2) With the expansion of problem constraints, the meta-heuristic algorithm involves relevant strategies to avoid or repair illegal solutions, reducing its applicability

[0004] Compared with the flexible job shop scheduling problem and the resource-constrained project scheduling problem, the resource-constrained multi-project scheduling problem has a different and complex problem space, such as resource conflicts and preemption among different projects, and has its unique application priority. Level rules, while multi-objective optimization will increase the complexity of the solution

And due to the characteristics of super-heuristic coding, traditional genetic programming is easy to generate the same functional coding and thus easy to fall into local optimum

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