Multi-target scheduling method and system for minimizing delivery time and delay of high-end equipment

Abstract

The invention provides a multi-target scheduling method for minimizing delivery time and delay of high-end equipment, and relates to the technical field of task scheduling. After the parent population is generated, the parent population is divided into a plurality of small populations, different quick sorting and crowding degree calculation rules are set for the plurality of small populations, the plurality of small populations can intersect in the evolution process, excellent genes in individuals can be spread among the populations. The whole population is effectively prevented from falling into a local optimal solution, the search range of the optimal solution is expanded, and the effect of the optimal scheduling scheme is effectively improved. Through the method provided by the invention, the development project collaborative scheduling problem of minimizing the project delivery time and delivery delay is effectively solved, the delivery time and delay time of multiple projects are shortened, and meanwhile, a good decision basis can be provided when an enterprise makes a plan.

Description

technical field [0001] The invention relates to the technical field of task scheduling, in particular to a multi-objective scheduling method and system for minimum delivery time and delay of high-end equipment. Background technique [0002] The collaborative scheduling problem of multi-objective R&D projects is a typical combinatorial optimization problem, which generally exists in the R&D and manufacturing of high-end equipment. The development of high-end equipment such as launch vehicles not only needs to consider the constraints of resources and places, but also needs to consider multiple project goals such as minimizing project delivery time and minimizing project delivery delays. In the research and development process of high-end equipment, minimizing the project delivery time is of great significance to the cost saving of the enterprise, and minimizing the project delivery delay is of great significance to the punctual delivery of the enterprise. These two goals are ...

AI Technical Summary

Problems solved by technology

However, due to the existence of multiple Pareto optimal solutions for multi-objective problems and the high time complexity of the algorithm, the Non-Dominated Sorting Genetic Algorithm (NSGA-II) has the problem that it is easy to fall into local optimum, that is, the existing multi-objective Algorithms for Collaborative Scheduling Problems of Development Projects Are Easy to Fall into Local Optimum

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