Minimizing total earliness and tardiness through unrelated parallel machine scheduling using distributed release time control |
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| Affiliation: | 1. Department of Industrial Engineering and Management, Taipei University of Technology, Taipei, 106, Taiwan, ROC;2. Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung City, 407, Taiwan, ROC;1. Department of Mathematics, East China University of Science and Technology, Shanghai 200237, China;2. Department of Statistics, Feng Chia University, Taichung 40724, Taiwan;3. Department of Computer Science and Information Management, Hungkuang University, Shalu 43302, Taiwan;1. Department of Information Management, Chang Gung University, Taoyan, Taiwan;2. Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei, Taiwan;1. Integration and Collaboration Laboratory, Department of Industrial Engineering and Engineering Management, National Tsing Hua University, P.O. Box 24-60, Hsinchu 300, Taiwan, ROC;2. Department of Mathematics, National Kaohsiung Normal University, Kaohsiung 824, Taiwan, ROC;3. Department of Statistics, Feng Chia University, Taichung, Taiwan;1. Tecnológico de Monterrey, Escuela de Ingeniería y Ciencias, Mexico;2. Universidad Autónoma de Nuevo León, Facultad de Ingeniería Mecánica y Eléctrica, Mexico;3. Universidad de La Laguna, Departamento de Ingeniería Informática y de Sistemas, Spain;1. University of Valencia, Department of Statistics and Operations Research, Burjassot, Valencia, Spain;2. Polytechnic University of Valencia, Department of Applied Statistics and Operations Research, and Quality, Valencia, Spain |
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| Abstract: | Using unrelated parallel machine scheduling to minimize the total earliness and tardiness of jobs with distinct due dates is a nondeterministic polynomial-hard problem. Delayed customer orders may result in penalties and reduce customer satisfaction. On the other hand, early completion creates inventory storage costs, which increase the total cost. Although parallel machines can increase productivity, machine assignments also increase the complexity of production. Therefore, the challenge in parallel machine scheduling is to dynamically adjust the machine assignment to complete the job within the shortest possible time. In this paper, we address an unrelated parallel machine scheduling problem for jobs with distinct due dates and dedicated machines. The objective is to dynamically allocate jobs to unrelated parallel machines in order to minimize the total earliness and tardiness time. We formulate the problem as a mixed integer linear programming (MILP) model and develop a modified genetic algorithm (GA) with a distributed release time control (GARTC) mechanism to obtain the near-optimal solution. A preliminary computational study indicates that the developed GARTC not only provides good quality solutions within a reasonable amount of time, but also outperforms the MILP model, a classic GA and heuristic approaches described in the literature. |
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| Keywords: | Unrelated parallel-machine (PM) Total earliness and tardiness Release time control Genetic algorithm (GA) |
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