Dynamic decision support framework for production scheduling using a combined genetic algorithm and multiagent model
Corresponding Author
Juan Du
SILC Business School, Shanghai University, Shanghai, China
Faculty of Engineering and IT, University of Technology Sydney, Sydney, Australia
School of Building Construction, Georgia Institute of Technology, Atlanta, Georgia, United States
Correspondence
Juan Du, SILC Business School, Shanghai University, Shanghai 201800, China.
Email: [email protected]
Search for more papers by this authorPeng Dong
SILC Business School, Shanghai University, Shanghai, China
SHU-SUCG Research Center for Building Industrialization, Shanghai University, Shanghai, China
Search for more papers by this authorVijayan Sugumaran
School of Business Administration, Oakland University, Rochester, Michigan, United States
Search for more papers by this authorDaniel Castro-Lacouture
School of Building Construction, Georgia Institute of Technology, Atlanta, Georgia, United States
Search for more papers by this authorCorresponding Author
Juan Du
SILC Business School, Shanghai University, Shanghai, China
Faculty of Engineering and IT, University of Technology Sydney, Sydney, Australia
School of Building Construction, Georgia Institute of Technology, Atlanta, Georgia, United States
Correspondence
Juan Du, SILC Business School, Shanghai University, Shanghai 201800, China.
Email: [email protected]
Search for more papers by this authorPeng Dong
SILC Business School, Shanghai University, Shanghai, China
SHU-SUCG Research Center for Building Industrialization, Shanghai University, Shanghai, China
Search for more papers by this authorVijayan Sugumaran
School of Business Administration, Oakland University, Rochester, Michigan, United States
Search for more papers by this authorDaniel Castro-Lacouture
School of Building Construction, Georgia Institute of Technology, Atlanta, Georgia, United States
Search for more papers by this authorFunding information: Ontology and Multi Agent-based Prefabricated Component Supply Chain Information Interoperability Mechanism and Decision Simulation, Grant/Award Number: 71701121; Prefabricated Component Supply Chain Cloud Platform Oriented Information Integration and Demand Forecasting Research, Grant/Award Number: 17YJC630021
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.
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