An Improved Genetic Programming Algorithm Optimization Method for Resource Constrained Multi-item Scheduling

Abstract

The invention discloses an improved genetic programming algorithm optimization method for resource-constrained multi-item scheduling, which includes the following steps: step 1: initializing parameters, function sets and attribute sets; step 2: collecting item set data under different working conditions, and Decompose it into a training set and a test set; step 3: extract the item information of each working condition item set in the training set as the training input, extract the function set and attribute set as the coding basis, and train the population in the improved genetic programming algorithm; step 4: Judging whether the maximum number of working conditions in the training set is reached, if so, output the optimal solution set in the population, if not, return to step 3 after changing the item set; step 5: use the test set and training set to output the optimal solution set in step 4 The solution set is tested; the invention can solve resource-constrained multi-item scheduling problems under single / multiple objectives, improve the defect that traditional genetic programming is easy to fall into local optimum, and improve the search and training capabilities of genetic programming.

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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