Off-line service task scheduling method for remote health monitoring with hard time windows

Abstract

The invention provides an off-line service task scheduling method for remote health monitoring with hard time windows. According to the invention, firstly a mathematical model for off-line service task scheduling of remote health monitoring is built, and then a meme algorithm is proposed for solving. The off-line service task scheduling method specifically comprises the steps of (1) building a mathematical model for off-line service task scheduling of remote health monitoring; (2) performing parameter initialization; (3) randomly initializing the location of firefly individuals, and determining the brightness corresponding to each firefly individual through calculating an objective function value of each firefly individual; (4) calculating the relative attractiveness and brightness of eachfirefly individual; (5) updating the location of the fireflies; (6) updating the brightness of each firefly individual; (7) performing local search on a chaotic neighborhood solution of an optical solution by using a simulated annealing algorithm; and (8) judging whether the number of iterations satisfies the maximum value or not, and obtaining an optimal solution. The off-line service task scheduling problem of remote health monitoring can be effectively solved according to the invention.

Description

technical field [0001] The invention belongs to the field of smart health, and relates to a remote health monitoring offline service task scheduling method with a hard time window. Background technique [0002] Remote health monitoring offline service is the core function of remote health monitoring, and the quality of its task scheduling directly affects the quality, efficiency and cost of remote health monitoring offline service. The offline service task scheduling problem of remote health monitoring can be simply described as a system with demand relations, there are several service objects, there are several service dispatch centers and service points, and it is required to reasonably design service travel routes and arrange travel time, To achieve a certain objective function value is optimal. It is a kind of academic research subject with quite extensive use value, and it belongs to complex combinatorial optimization problem in theory. [0003] The genetic algorithm ...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the problems of slow running speed, poor convergence ability, and low optimization efficiency of the existing remote health monitoring offline service task scheduling technology, the present invention provides a fast running speed, strong convergence ability, and high optimization efficiency. Window's remote health monitoring offline service task scheduling method, which is based on the memetic algorithm of improved firefly and improved simulated annealing

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