Journal of Industrial Engineering Research in Production Systems

Journal of Industrial Engineering Research in Production Systems

A Mathematical Model for an Order Management and Production Planning Problem Considering Installation in Hybrid (MTS-MTO) Systems

Document Type : Research Paper

Authors
Seyed Mohamad Javad Sadeghi 1
Ahmad Makui 2
1 M.Sc. in Industrial Engineering, Systems Optimization Division, Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
2 Professor, Systems Optimization Division, Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
10.22084/ier.2025.30418.2193
Abstract
With the increasing diversity of customer demands and the dynamic changes in supply chains, hybrid production systems (MTS-MTO) have emerged as a strategic solution for balancing mass production and customization. This study presents a bi-objective mathematical model aimed at maximizing system profit and minimizing customer dissatisfaction in a hybrid production environment. The proposed model integrates the simultaneous production of MTS and MTO products while considering production capacity constraints and shared resources. A key feature of the model is the inclusion of the installation and commissioning process for products, providing a comprehensive approach to production planning and process execution. The model was implemented in a case study of an industrial hoist manufacturing company. Results indicated that with increasing demand, system profit improved; however, customer dissatisfaction also grew significantly due to production and installation capacity limitations, leading to rejected orders and lost sales. Additionally, under high demand conditions, a greater focus on MTS products enhanced productivity in this segment but reduced the system's ability to fulfill MTO orders. The findings underscore the importance of precise production planning, improved capacity management, and increased flexibility in addressing demand fluctuations. The proposed model serves as an effective tool for decision-makers to enhance productivity and customer satisfaction in hybrid production environments and can be utilized as a framework for improving production systems across various industries.
Keywords
Order management
Hybrid Production System (MTS-MTO)
Production Planning
Installation and Commissioning. 
Subjects
  • Tarhan, İ., & Oğuz, C. (2022). A matheuristic for the generalized order acceptance and scheduling problem. J. Oper. Res., 299(1), 87–103. https://doi.org/10.1016/j.ejor.2021.08.024
  • Sarvestani, H. K., Zadeh, A., Seyfi, M., et al. (2019). Integrated order acceptance and supply chain scheduling problem with supplier selection and due date assignment. Soft Comput., 75, 72–87. https://doi.org/10.1016/j.asoc.2018.10.045
  • Beemsterboer, B., Land, M., & Teunter, R. (2017). Flexible lot sizing in hybrid make-to-order/make-to-stock production planning. J. Oper. Res., 260(3), 1014–1023. https://doi.org/10.1016/j.ejor.2017.01.015
  • Peeters, K., & van Ooijen, H. (2020). Hybrid make-to-stock and make-to-order systems: A taxonomic review. J. Prod. Res., 58(15), 4659–4688. https://doi.org/10.1080/00207543.2020.1778204
  • Kuthambalayan, T. S., & Bera, S. (2020). A review of the literature on mixed make-to-stock/make-to-order production systems: Major findings and directions for future research. J. Serv. Oper. Manag., 37(3), 372–406. https://doi.org/10.1504/IJSOM.2020.111038
  • Sato, Y., Maeda, H., Toshima, R., Nagasawa, K., Morikawa, K., & Takahashi, K. (2023). Switching decisions in a hybrid MTS/MTO production system comprising multiple machines considering setup. J. Prod. Econ., 263, 108877. https://doi.org/10.1016/j.ijpe.2023.108877
  • Xiong, S., Feng, Y., & Huang, K. (2020). Optimal MTS and MTO hybrid production system for a single product under the cap-and-trade environment. Sustainability, 12(6), 2426. https://doi.org/10.3390/su12062426
  • Pereira, D. F., Oliveira, J. F., & Carravilla, M. A. (2022). Merging make-to-stock/make-to-order decisions into sales and operations planning: A multi-objective approach. Omega (Westport), 107, 102561. https://doi.org/10.1016/j.omega.2021.102561
  • Ellabban, A., & Abdelmaguid, T. (2019). Optimized production control policy for hybrid MTS-MTO glass tube manufacturing using simulation-based optimization. In 2019 8th Int. Conf. Ind. Technol. Manag. (ICITM); Cambridge, UK. IEEE. https://doi.org/10.1109/ICITM.2019.8710685
  • Kalantari, M., Rabbani, M., & Ebadian, M. (2011). A decision support system for order acceptance/rejection in hybrid MTS/MTO production systems. Math. Model., 35(3), 1363–1377. https://doi.org/10.1016/j.apm.2010.09.015
  • Rafiei, H. & Rabbani, M., (2011). Order partitioning and order penetration point location in hybrid
    make-to-stock/make-to-order production contexts. *Computers & Industrial Engineering, 61, pp.
    550-560. https://doi.org/10.5267/j.ijiec.2015.12.004
  • Rafiei, H., & Rabbani, M. (2012). Capacity coordination in hybrid make-to-stock/make-to-order production environments. J. Prod. Plann., 50, 773–789. https://doi.org/10.1016/j.cie.2011.04.010
  • Wang, Z., Qi, Y., Cui, H., & Zhang, J. (2019). A hybrid algorithm for order acceptance and scheduling problem in make-to-stock/make-to-order industries. Ind. Eng., 127, 841–852. https://doi.org/10.1016/j.cie.2018.10.021
  • Jalali, M. S., Ghomi, S. F., & Rabbani, M. (2020). A system dynamics approach towards analysis of hybrid make-to-stock/make-to-order production systems. Eng. Manag. Syst., 19(1), 143–163. https://doi.org/10.7232/iems.2020.19.1.143
  • Abedi, A., & Zhu, W. (2020). An advanced order acceptance model for hybrid production strategy. Journal of manufacturing systems, 55, 82-93. https://doi.org/10.1016/j.jmsy.2020.02.012
  • Bortolini, M., Faccio, M., Gamberi, M., & Pilati, F. (2019). MTO/MTS policy optimization for sheet metal plate parts in an ATO environment. Procedia Cirp, 81, 1046-1051. https://doi.org/10.1016/j.procir.2019.03.249
  • Wang, C., Yang, C. & Zhang, T., (2023). Order planning with an outsourcing strategy for a make-to-order/make-to-stock production system using particle swarm optimization with a self-adaptive genetic operator. *Computers & Industrial Engineering, 182, p. 109420. https://doi.org/10.1016/j.cie.2023.109420
  • Sadeghi Ahangar, S., & Rabbani, M. (2024). A scenario-based decision framework for the order promising process in hybrid MTS/MTO production systems considering product substitution. Journal of Industrial and Production Engineering, 1-18. https://doi:1080/21681015.2024.2400991
  • Aghaei, J., Amjady, N., & Shayanfar, H. A. (2011). Multi-objective electricity market clearing considering dynamic security by lexicographic optimization and augmented epsilon constraint method. Appl. Soft Comput., 11(4), 3846–3858. https://doi.org/10.1016/j.asoc.2011.02.022
  • Cui H, Luo X., (2017). An improved Lagrangian relaxation
    approach to scheduling steelmaking-continuous casting
    process    . Comput Chem Eng. 106:133–146. https://doi:10.1016/j.compchemeng.2017.05.026
  • Fisher ML. The Lagrangian relaxation method for solving integer programming problems. Manag Sci.
    1981;27(1):1–18. https://doi: 10.1287/mnsc.27.1.1
  • Rius-Sorolla G. (2020). Lagrangian relaxation of the generic materials and operations planning model. Central Eur J Operations Res. 28(1): 105–123. https://doi: 10.1007/s10100-018-0593-0
  • Klincewicz JG, Luss H. (1986). A Lagrangian relaxation heuristic
    for capacitated facility location with single-source
    constraints    . J Oper Res Soc. 37(5):495–500. https://doi:10.1057/jors.1986.84
Journal of Industrial Engineering Research in Production Systems
Volume 12, Issue 25
March 2025
Pages 137-153