Flowshop scheduling with a general exponential learning effect |
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| Affiliation: | 1. School of Science, Shenyang Aerospace University, Shenyang 110136, People''s Republic of China;2. Faculty of Management and Economics, Dalian University of Technology, Dalian 116024, People''s Republic of China;1. College of Mechanical Science and Engineering, Jilin University, Changchun, China;2. Department of Industrial Management, National Taiwan University of Science and Technology, Taipei, Taiwan;1. David Geffen School of Medicine at University of California, Los Angeles, Los Angeles, California;2. Department of Pediatrics, University of California, Los Angeles, Los Angeles, California;1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China;2. Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, China;1. Department of Industrial Engineering, Faculty of Engineering, University of Qom, Qom, Iran;2. Department of Industrial Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran;1. Industrial Engineering Department, Sharif University of Technology, Tehran, Iran;2. Grupo de Sistemas de Optimización Aplicada, Instituto Tecnológico de Informática, Ciudad Politécnica de la Innovación, Edifico 8G, Acc. B, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain;1. School of Electronics and Information Engineering, Liaoning University of Technology, China;2. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China;3. School of Software, Dalian University of Technology, China;4. Institute of Computing Science, Poznań University of Technology, Poland |
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| Abstract: | This paper investigates flowshop scheduling problems with a general exponential learning effect, i.e., the actual processing time of a job is defined by an exponent function of the total weighted normal processing time of the already processed jobs and its position in a sequence, where the weight is a position-dependent weight. The objective is to minimize the makespan, the total (weighted) completion time, the total weighted discounted completion time, and the sum of the quadratic job completion times, respectively. Several simple heuristic algorithms are proposed in this paper by using the optimal schedules for the corresponding single machine problems. The tight worst-case bound of these heuristic algorithms is also given. Two well-known heuristics are also proposed for the flowshop scheduling with a general exponential learning effect. |
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| Keywords: | Scheduling Flowshop Learning effect Heuristic algorithm Worst-case analysis |
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