Dynamic decision support framework for production scheduling using a combined genetic algorithm and multiagent model. Issue 1 (10th February 2020)

• Record Type: Journal Article
• Authors: Du, Juan; Dong, Peng; Sugumaran, Vijayan; Castro‐Lacouture, Daniel
• Other Names: Gupta Deepak guestEditor.; Rodrigues Joel J. P. C. guestEditor.; Castillo Oscar guestEditor.; Herrero Álvaro guestEditor.; Jiménez Alfredo guestEditor.; Bayraktar Secil guestEditor.; Arroyo Angel guestEditor.
• Is Part Of: Expert systems. Volume 38:Issue 1(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-02-10
• Subjects: dynamic decision support -- genetic algorithm -- multiagent system -- prefabricated concrete components -- production scheduling; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12533
• Languages: English
• ISSNs: 0266-4720

Abstract

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 real‐world 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.