Machine learning techniques for scheduling jobs with incompatible families and unequal ready times on parallel batch machines |
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| Affiliation: | 1. Institute of Information Systems, Technical University of Ilmenau, 98684 Ilmenau, Germany;2. Department of System Analysis, Technical University of Ilmenau, 98684 Ilmenau, Germany;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 07102, USA;4. Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong;1. Department of Fundamental Education, Ningbo Institute of Technology, Zhejiang University, Ningbo 315100, PR China;2. Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Kowloon, Hong Kong;3. School of Computer Science and Information Engineering, Contemporary Business and Trade Research Center, Zhejiang Gongshang University, Hangzhou 310018, PR China;1. Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, Anhui University, Hefei, Anhui 230039, PR China;2. School of Management, Hefei University of Technology, Hefei, Anhui 230009, PR China;3. Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA |
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| Abstract: | This research is motivated by a scheduling problem found in the diffusion and oxidation areas of semiconductor wafer fabrication facilities, where the machines can be modeled as parallel batch processors. Total weighted tardiness on parallel batch machines with incompatible job families and unequal ready times of the jobs is attempt to minimize. Given that the problem is NP hard, a simple heuristic based on the Apparent Tardiness Cost (ATC) Dispatching Rule is suggested. Using this rule, a look-ahead parameter has to be chosen. Because of the appearance of unequal ready times and batch machines it is hard to develop a closed formula to estimate this parameter. The use of inductive decision trees and neural networks from machine learning is suggested to tackle the problem of parameter estimation. The results of computational experiments based on stochastically generated test data are presented. The results indicate that a successful choice of the look-ahead parameter is possible by using the machine learning techniques. |
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