Genetic crossover method and project scheduling method based on active building block crossover operator
A technology of crossover operator and scheduling method, which is applied in the field of genetic crossover method and project scheduling, can solve problems such as poor performance, poor operator controllability and flexibility, and low solution efficiency, so as to improve controllability and flexibility performance, improved efficiency and excellent performance of the solution
Inactive Publication Date: 2022-08-09
BEIJING UNIV OF POSTS & TELECOMM
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[0004] The embodiment of the present application provides a genetic crossover method and project scheduling method based on an active building block crossover operator to solve the technical problems of poor controllability and flexibility of the operator, which lead to low solution efficiency and poor solution quality of the project scheduling problem
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[0048] In order to make the purpose, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the present application. examples, but not all examples. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.
[0049] figure 1 This is one of the schematic flowcharts of the genetic crossover method based on the active building block crossover operator provided in the embodiment of the present application. refer to figure 1 , an embodiment of the present application provides a genetic crossover method based on an active building block crossover operator, which may include:
[0050] S1...
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The invention relates to the field of project scheduling, and provides a genetic crossover method based on an active building block crossover operator and a project scheduling method. The genetic crossover method based on the active building block crossover operator comprises the following steps: determining a parent selection block in an active list of a parent by utilizing a plurality of crossover points; the positions of the intersection points are determined according to a project scheduling problem; determining a parent selection block in the active list of the parent using the plurality of intersections; replacing the parent selection block with building blocks to generate a part of filial generations; and inserting the to-be-distributed activity into the activity list of a part of the filial generations to generate a filial generation scheduling scheme. According to the genetic crossover method based on the active building block crossover operator provided by the embodiment of the invention, the controllability and the flexibility of the crossover operator can be improved, so that the obtained filial generation scheduling scheme can converge to the global optimal solution more and more quickly in the filial generation genetic process; and thus, the solving efficiency and the solving excellence of the resource-constrained project scheduling problem are improved.
Description
technical field [0001] The present application relates to the technical field of project scheduling, in particular to a genetic crossover method and a project scheduling method based on an active building block crossover operator. Background technique [0002] Resource-constrained project scheduling problem (RCPSP, Resource-constrained project scheduling problem) is a complex combinatorial optimization problem in which non-preemptive activities of a project should be scheduled according to priority constraints and resource constraints to minimize completion time. In recent years, it has attracted more and more attention from scholars and practitioners due to its wide range of practical applications in transportation, construction, manufacturing, management, and logistics. [0003] At present, in the meta-heuristic method of solving RCPSP, genetic algorithm is widely used because of its random search ability, population-based evolution mechanism, simple process and strong sca...
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IPC IPC(8): G06Q10/04G06Q10/06G06N3/12
CPCG06Q10/04G06Q10/06312G06N3/126
Inventor 翁迅张经天范宏强张静曹忠辉
Owner BEIJING UNIV OF POSTS & TELECOMM