Abstract
Order release is a key production planning and control function, specifically in high variety contexts. A large literature on release methods that balance the workload consequently emerged. These Workload Control methods can be rule based, using a simple greedy heuristic, optimization based or optimization based with lead times that are exogenous. Although all three types of methods have the same objective, their performance has never been compared. Using simulation, this study shows that a better on time delivery performance of jobs can be achieved by the two optimization based release methods. Most importantly, optimization based methods that assume lead times to be exogenous significantly outperform alternative methods in terms of tardiness performance. Rule based and optimization based Workload Control without exogenous lead times overemphasize average lateness reduction, which leads to sequence deviations that offset performance improvements through balancing. In contrast, Workload Control methods that assume lead times to be exogenous limit sequence deviations, which leads to a significant reduction in dispersion of lateness. This has important implication for the future design of order release methods, and managerial practice.
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The datasets generated during and/or analysed during the current study are not publicly available but are available from the corresponding author on reasonable request.
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Acknowledgments
This work is financially supported by National Key Research and Development Program of China (2021YFB3301701), 2019 Guangdong Special Support Talent Program Innovation and Entrepreneurship Leading Team (China) (2019BT02S593), 2018 Guangzhou Leading Innovation Team Program (China) (201909010006), and the Science and Technology Development Fund (Macau SAR) (0078/2021/A).
We also appreciate the sponsorships from the industry, including but not limited to Carpoly Chemical Group Co., Ltd. GBA and B&R International Joint Research Center for Smart Logistics is a provincial research lab sponsored by the Department of Science and Technology of Guangdong Province, thanks to which the international collaboration has been effectively conducted.
Finally, we gratefully thank the editor and all reviewers for their time spend making their constructive remark and useful suggestions, which has significantly raised the quality of the paper. Each suggested revision and comment, brought forward by the reviewers was accurately incorporated and considered.
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Mingze Yuan is a master student in management science and engineering at the School of Management, Jinan University (Guangzhou, China). Her main research interests include workload control, order release mechanisms for high variety make-to-order shops and Industry 4.0.
Ting Qu is a full professor at the School of Intelligent Systems Science and Engineering, Jinan University (Zhuhai, PR China). He received his Ph.D. degree from the Department of Industrial and Manufacturing Systems Engineering of The University of Hong Kong. His research interests include IoT-based smart manufacturing systems, logistics and supply chain management, and industrial product/production service systems. He has undertaken over twenty research projects funded by government and industry and has published nearly 200 technical papers in these areas, half of which have appeared in reputable journals. He serves as director or board member for several academic associations in industrial engineering and smart manufacturing.
Matthias Thürer is professor at Chemnitz University of Technology, Germany. He contributed to the improvement, simplification and integration of material flow control systems, and their integration with Industry 4.0. Apart from operations management, Matthias is also interested in social and philosophical issues including system theory, cybernetics causality and philosophy of science.
Lin Ma is a postdoctoral fellow in the Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong (PR China). He holds a Master from Xian University of Science and Technology, Xian (PR China) and a PhD from Jinan University, Guangzhou (PR China). Production Bottleneck Management for high variety make-to-order shops is one of his main research interests, he is also interested in intelligent manufacturing, including digital twins and production-logistics synchronization. He has published seven papers in journals such as the International Journal of Production Research, Production Planning & Control.
Lei Liu is currently a Ph.D. student in Management Science and Engineering at the School of Management, Jinan University (Guangzhou, PR China). He received a Master degree in industrial engineering from Chongqing University of Post and Telecommunications (Chongqing, PR China) in 2019 and a bachelor degree in manufacturing and automation from Northwest A&F University (Yangling, PR China) in 2014. Lei Liu worked as a big data R&D engineer from 2019 to 2020 at GREE Electric Appliances, a domestic appliance manufacturer and the worlds largest air conditioner producer. His research interests include data and knowledge-driven operation decision making and optimisation for complex industrial systems, intelligent algorithms, and intelligent manufacturing for production-logistics synchronisation, production workload control, modular manufacturing systems and digital twins.
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Yuan, M., Qu, T., Thürer, M. et al. Rule based vs Optimization based Workload Control with and without Exogenous Lead Times: An Assessment by Simulation. J. Syst. Sci. Syst. Eng. 32, 553–570 (2023). https://doi.org/10.1007/s11518-023-5574-8
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DOI: https://doi.org/10.1007/s11518-023-5574-8