Power Material Guarantee and Allocation Platform Based on Multi-objective Particle Swarm Optimization Algorithm

Abstract

The invention discloses a power material guarantee and deployment platform based on a multi-objective particle swarm optimization algorithm, including: a reserve optimization result database, a material demand information database, a multi-objective particle swarm optimization algorithm module, an algorithm evaluation and verification module, and optimization scheme generation . The invention uses the multi-objective particle swarm optimization (MPSO) algorithm to collect the historical reserve data of power materials, the list of material requirements for business expansion and new installations, emergency repairs, and daily repairs, as well as related data such as emergency disaster categories and emergency repairs, etc., to generate Multi-objective optimization scheme for power material guarantee and deployment. Through this platform, users can quickly optimize the demand for various power materials after a certain disaster or event occurs, guide relevant departments to reserve or purchase materials on demand, ensure the smooth progress of power safety production, and avoid the waste of funds caused by excessive reserves .

Description

Technical field: [0001] The invention relates to a power material guarantee and deployment platform based on a multi-objective particle swarm optimization algorithm. Background technique: [0002] The material storage capacity of power companies is quite large, and the production of power companies has a strong social supply, which is easily affected by external factors. The storage period is relatively long, ranging from several months to dozens of months, and the storage capacity is large. , the procurement cycle is longer. Because of the particularity of the power industry, the technology of power production is updated frequently, and the storage and management of power materials is particularly arduous, which also poses a great test to managers. For power companies, improving the efficiency of power material storage management and establishing a coordination and linkage allocation mechanism for power materials can reduce the operating costs of enterprises, allocate rese...

AI Technical Summary

Problems solved by technology

[0005] The traditional multi-objective algorithm is to convert the multi-objective problem into a single-objective problem and then use a mature single-objective optimization algorithm to solve it. The disadvantage is that only one solution can be obtained for one optimization solution.

With the deepening of optimization research, more and more complex optimization problems have emerged. These problems usually involve many factors. The model of the optimization problem is difficult to describe, and the number of independent variables has many dimensions, which greatly increases the amount of calculation. Such problems There is less information available, so it is difficult to design a general optimization algorithm

Images