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Seru Scheduling Problems with Multiple Due-Windows Assignment and Learning Effect

Abstract

This paper deals with seru scheduling problems with multiple due windows assignment and DeJong’s learning effect. Specific time intervals are assigned to jobs with multiple due windows and learning effect is introduced to characterize the decrease of processing times with the accumulation of the working experience. We assume that the set of jobs assigned to each due window is independent, and no inclusion exists between due windows. The objective is to determine the optimal due window positions and sizes, the set of jobs assigned to each due window, and the optimal schedule in each seru to minimize a multidimensional function, which consists of the earliness and tardiness punishment cost, as well as the due window related starting time and size cost. We find that when the number of jobs and the due windows assigned to each seru are pre-specified in advance, the problem can be solved in polynomial time. Meanwhile, the impacts of the due-window allocation strategy and learning effect on the total cost are respectively discussed based on numerical examples and special cases. The results show that if each seru is assigned with the same number of due windows, the total cost can be reduced with the increasing ratio of the due-window number to the to-be-processed job number. Furthermore, with an increasing learning effect, the total cost will be decreased.

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Data Availability Statement

The authors confirm that the data supporting the findings of this study are available within the article.

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Acknowledgments

This work has been supported in part by the National Natural Science Foundation of China (NSFC), under grant Nos.71401075 and 71801129; System Science and Enterprise Development Research Center, under grant No.Xq22B06. We would like to give our great appreciation to all the reviewers and editors who contributed this research.

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Correspondence to Zhe Zhang.

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Yujing Jiang is a master candidate of Nanjing University of Science and Technology, in Nanjing, P. R. China. She is mainly engaged in the research of optimal scheduling algorithms in seru production system.

Zhe Zhang is associate professor of School of Economics and Management at Nanjing University of Science and Technology. Her current research interests are in the areas of seru scheduling, advanced manufacturing, production and operations management.

Xiaoling Song received her Ph.D. degree in management science and engineering, in 2016, and the B.S. degree in management science, in 2012, from Sichuan University, Chengdu, China. She is currently associate professor with the Department of Management Science and Engineering, School of Economics and Management in Nanjing University of Science and Technology, Nanjing China. Her research focuses on decision making, multi-objective bilevel optimization.

Yong Yin obtained his PhD from Tohoku University in 2002. He is currently professor of Doshisha University. He was the President of the Asian Association of Management Science and Applications (2013–2015), Executive Editor of the Asian Journal of Management Science and Applications, Senior Editor of the Operations Management Education Review, and the Associate Editor of the Journal of Japanese Operations Management and Strategy. His research focuses on seru production and advanced manufacturing.

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Jiang, Y., Zhang, Z., Song, X. et al. Seru Scheduling Problems with Multiple Due-Windows Assignment and Learning Effect. J. Syst. Sci. Syst. Eng. 31, 480–511 (2022). https://doi.org/10.1007/s11518-022-5534-8

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Keywords

  • Scheduling
  • seru production system
  • due windows
  • learning effect