A hybrid scheduling decision support model for minimizing job tardiness in a make-to-order based mould manufacturing environment |
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| Authors: | K.L. Choy Y.K. Leung H.K.H. Chow T.C. Poon C.K. Kwong G.T.S. Ho S.K. Kwok |
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| Affiliation: | 1. CNR-ISTEC, National Research Council – Institute of Science and Technology for Ceramics, Via Granarolo 64, 48018 Faenza, Italy;2. DICAM, Alma Mater Studiorum Università di Bologna, Bologna, Italy;1. Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, United States;2. High Performance Information Systems Lab, School of Computer Engineering and Informatics, University of Patras, Rio 26500, Greece;3. Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, United States;1. School of Management, Hefei University of Technology, Hefei 230009, PR China;2. Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei 230009, PR China;3. Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07012, USA;1. State Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China;2. Information Technology Center, Tsinghua University, Beijing 100083, China;3. Department of Computer Science and Technology, Tsinghua University, Beijing 100083, China;4. The 61th Institute of Electronic System Engineering, Beijing 100039, China |
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| Abstract: | In the make-to-order (MTO) mode of manufacturing, the specification of each product is unique such that production processes vary from one product to another making the production schedule complex. In order to achieve high level productivity, the production flow is not arranged in sequence; instead, the job schedule of different production jobs is adjusted to fit in with the multiple-job shop environment. A poor scheduling of jobs leads to high production cost, long production time and tardiness in job performance. The existing of tardiness in the production schedule significantly affects the harmony among the multiple jobs on the shops floor. In order to provide a complete solution for solving MTO scheduling problems with job shifting and minimizing job tardiness, a hybrid scheduling decision support model (SDSM) is introduced. The model is combined by a Genetic Algorithm (GA) and an optimisation module. GA is adopted to solve the complex scheduling problem taking into consideration of the wide variety of processes while the optimisation module is suggested for tackling tardiness in doing the jobs in a cost effective way. The simulation results reveal that the model shortens the generation time of production schedules and reduces the production cost in MTO-based production projects. |
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