Abstract
This research deals with a three-stage flowshop, processing two types of products. In the first stage, two heterogeneous machines assemble electronic components, and in the second stage, two dedicated parallel machines assemble the assembled electronic components with other components to produce respective types of products, where both the stages constitute job processor machines. The third stage is a common machine of a batch processor to conduct a functional test. The objective is to minimize total actual flowtime, where the actual flowtime of a part is defined as the time interval between its starting time of processing and a common due date. The problem is formulated as a mathematical model, and a heuristic procedure consists of two algorithms to solve this problem is proposed. The first algorithm is used to determine production batch sizes and the batch production sequence, whereas the second algorithm is used to select the machine in the first stage. The proposed models and algorithms are built by integrating all processes that occur at each stage. An illustrative example is intended to show that the proposed algorithms could perform well.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen, H., Zhou, S., Li, X., & Xu, R. (2014). A hybrid differential evolution algorithm for a two-stage flow shop on batch processing machines. International Journal of Production Research, 52, 1–10.
Dobson, G., Karmarkar, U. S., & Rummel, J. L. (1989). Batching to minimize flow times on parallel heterogeneous machines. Management science 35:607-613.
Gerstl, E., & dan Mosheiov, G. (2013). A two-stage flow shop batch-scheduling problem with the option of using not-all-machines. International Journal of Production Economics, 146(1), 161–166.
Halim, A. H., & Jenny. (2003). Penjadwalan Pada Flowshop Dengan Kelompok Mesin Heterogen untuk Meminimasi Makespan. Tek dan Manaj Ind, 23, 1–11.
Halim, A. H., Miyazaki, S., & Ohta, H. (1994). Batch-scheduling problems to minimize actual flow times of parts through the shop under JIT environment. European Journal of Operational Research, 72, 529–544.
Halim, A. H., & Ohta, H. (1994). Batch-scheduling problems to minimize inventory cost in the shop with both receiving and delivery just-in-times. International Journal of Production Economics, 33, 185–194.
Halim, A. H., Suryadhini, P. P., & Toha, I. S. (2006). Batch scheduling to minimize Total actual flow time in a two-stage Flowshop with dedicated machines in the second stage. In Proceedings of the 7th Asia Pacific industrial engineering and management systems conference. Bangkok (pp. 1343–1349).
Hidayat, N. P. A., Cakravastia, A., Samadhi, T. M. A. A., & Halim, A. H. (2013). A single item batch scheduling model on a batch processor to minimize total actual flowtime of parts through the shop. In Proceeding of asia pacific industrial engineering and management system. Cebu Islan, Philippine.
Hidayat, N. P. A., Cakravastia, A., Samadhi, T. M. A. A., & Halim, A. H. (2015). A batch scheduling model for m heterogeneous batch processor. International Journal of Production Research, 7543, 1–16.
Huang, T., & Lin, B. M. T. (2013). Batch scheduling in differentiation flow shops for makespan minimisation. International Journal of Production Research, 51, 37–41.
Kagermann, H., Helbig, J., Hellinger, A., & Wahlster, W. (2013). Umsetzungsempfehlungen für das Zukunftsprojekt Industrie 4.0: Deutschlands Zukunft als Produktionsstandort sichern; Abschlussbericht des Arbeitskreises Industrie 4.0. Forschungsunion; Geschäftsstelle der Plattform Industrie 4.0.
Morton, T. E., & Pentico, D. W. (1993). Heuristic scheduling systems. John Wiley & Sons, Inc.
Suhaimi, N., Nguyen, C., & Damodaran, P. (2016). Lagrangian approach to minimize makespan of non-identical parallel batch processing machines. Computers and Industrial Engineering, 101, 295–302.
Sukoyo, Samadhi, T. M. A. A., Iskandar, B. P., & Halim, A. H. (2008). Batch scheduling for a single machine processing parts of a single item with increasing processing time to minimize total actual flow time. In Proceedings of the 9th Asia pacific industrial engineering & management systems conference. Bali (pp. 572–579).
Thoben, K., Wiesner, S., & Wuest, T. (2017). “ Industrie 4.0 ” and smart manufacturing—A review of research issues and application examples. International Journal of Automation Technology, 11, 4–16.
Witt, C., Wheating, S., & Leser, U. (2018). Level order sampling for task graph scheduling on heterogeneous resources. In IEEE/ACM workflows in support of large-scale science (pp. 20–30).
Yusriski, R., Astuti, B., Samadhi, T. M. A. A., & Halim, A. H. (2015). Integer batch scheduling problems for a single-machine to minimize Total actual flow time. Procedia Manufacturing, 2, 118–123.
Acknowledgments
This research is funded by the Indonesia Endowment Fund for Education (LPDP)—Ministry of Finance through doctoral program scholarship.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Suryadhini, P.P., Sukoyo, Suprayogi, Halim, A.H. (2022). A Batch Scheduling Model for a Three-Stage Flowshop with Batch Processor and Heterogeneous Job Processor to Minimize Total Actual Flowtime. In: Kuo, YH., Fu, Y., Chen, PC., Or, C.Kl., Huang, G.G., Wang, J. (eds) Intelligent Engineering and Management for Industry 4.0. Springer, Cham. https://doi.org/10.1007/978-3-030-94683-8_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-94683-8_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-94682-1
Online ISBN: 978-3-030-94683-8
eBook Packages: Business and ManagementBusiness and Management (R0)