Multi-target energy supply and operation flexible scheduling method

Abstract

The invention discloses a multi-objective energy supply and operation flexible scheduling method, which comprises the steps of (1) determining an energy supply optimization index and an energy supplyconstraint of flexible scheduling, and constructing a multi-objective energy supply optimization model, wherein target functions of the multi-target energy supply optimization model comprise a targetfunction with the minimum maximum completion time, a target function with the maximum energy consumption and the minimum load and a target function with the minimum total energy consumption and the minimum load; (2) adopting an improved genetic algorithm to solve the multi-objective energy supply optimization model, so that each objective function reaches the minimum value, and the optimal energysupply and operation flexible scheduling is obtained; the improved genetic algorithm adopts self-adaptive crossover and mutation probabilities, and adopts selection operation based on NSGA II. According to the method, energy conservation and efficiency improvement are taken as optimization objectives, energy optimization indexes are constructed, an improved genetic algorithm is applied, schedulingpersonnel are assisted to quickly generate a production scheduling plan,.

Description

technical field [0001] The invention relates to the technical field of workshop production scheduling, in particular to a multi-objective energy supply and operation flexible scheduling method. Background technique [0002] There have been more than 60 years of research history in workshop production scheduling, and some analytical optimization, dynamic programming, and heuristic algorithms have been proposed in the early days. With the development of computer science, software and hardware technology, some complex multidisciplinary algorithms such as genetic algorithm, artificial neural network, ant colony algorithm and other intelligent methods have gradually been applied, and have accumulated rich experience in job scheduling in complex situations. research results. [0003] By simulating the survival and competition process of organisms in nature, genetic algorithms can perform global optimization of complex engineering problems by using operators such as selection, cro...

AI Technical Summary

Problems solved by technology

[0006] Aiming at the problems that the current enterprises do not consider the consumption of power and energy in the production scheduling, the present invention provides a multi-objective energy supply and operation

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