Dynamic decision support framework for production scheduling using a combined genetic algorithm and multiagent model

Abstract

Due to the dynamic nature, complexity, and interactivity of production scheduling in an actual business environment, suitable combined and hybrid methods are necessary. This paper takes prefabricated concrete components as an example and develops the dynamic decision support framework based on a genetic algorithm and multiagent system (MAS) to optimize and simulate the production scheduling. First, a multiobjective genetic algorithm is integrated into the MAS for preliminary optimization and a series of near-optimal solutions are obtained. Subsequently, considering the resource constraints and uncertainties, the MAS is used to simulate complex realworld production environments. Considering the different types of uncertainty factors, the paper proposes the corresponding dynamic scheduling method and uses MAS to generate the optimal production schedule. Finally, a practical prefabricated construction case is used to validate the proposed model. The results show that the model can effectively address the occurrence of uncertain events and can provide dynamic decision support for production scheduling. Highlights • A dynamic decision support framework based on a genetic algorithm and multiagent system (MAS) for production scheduling is proposed. • A variety of agents are set up in the MAS to increase the likeness of the experimental environment to the real production environment. • Dynamic scheduling methods are proposed for different uncertain factors, and the effectiveness of the proposed method is verified by experiments.

Mixed Production Line Optimization of Industrialized Building Based on Ant Colony Optimization Algorithm

“…(2) the parallel production of hybrid production line is equivalent to the parallel production method of multiple machines; (3) unreasonable scheduling strategy; (4) most of the research on hybrid production line scheduling problem is single objective scheduling. erefore, the key to solve the multiobjective hybrid production line scheduling problem is to find a reasonable batch strategy and propose a more effective new algorithm for the multiobjective hybrid production line scheduling optimization problem [24]. For solving this problem, this article proposes a model of mixed production line optimization of industrialized building based on the ant colony optimization algorithm.…”

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