Design a decision structure for the order promising process in hybrid MTS/MTO environments considering product substitution, a case study

Document Type : Research Paper

Authors

School of Industrial and System Engineering, College of Engineering, University of Tehran, Tehran, Iran

Abstract

The importance of order promising process has led manufacturers to use more productive production systems. Optimizing the production system is one of the ways to increase productivity. This issue becomes even more significant when some of the raw materials needed to produce different final products are homogenous. In this paper, a decision structure for the order promising process with product homogeneity and product substitution in a Hybrid Make-To-Stock and Make-To Order environment is studied. For this purpose, a bi-objective mathematical model has been designed and solved by the Lagrangian Relaxation solution method. Despite the extensive studies that have been done in this area, there are few articles that have studied the possibility of substituting the final products by the manufacturer. In order to investigate this gap, product substitution has been studied in this article. Two different types of customers are considered in this model. A case study is also conducted to evaluate the applicability of the proposed model. The results of this article show that the possibility of products substitution will reduce rejected orders and increase system profits. Also, fulfilling orders that are more flexible in terms of product delivery time is a higher priority for the manufacturer than other orders.

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Main Subjects


Abedi, A., & Zhu, W. (2020). An advanced order acceptance model for hybrid production strategy. Journal of manufacturing systems, 55, 82-93.
Afshar-Bakeshloo, M., Flapper, S. D. P., Jolai, F., & Bozorgi-Amiri, A. (2021). A heuristic substitutions policy to control inventories for a hybrid manufacturing/remanufacturing system with product substitutions between three markets considering customers’ behavior and remanufacturing limitations. Journal of Cleaner Production, 127871.
Aghaei, J., Amjady, N., & Shayanfar, H. A. (2011). Multi-objective electricity market clearing considering dynamic security by lexicographic optimization and augmented epsilon constraint method. Applied Soft Computing, 11(4), 3846-3858.
Agra, A., Poss, M., & Santos, M. (2018). Optimizing make-to-stock policies through a robust lot-sizing model. International journal of production economics, 200, 302-310.
Ahiska, S. S., Gocer, F., & King, R. E. (2017). Heuristic inventory policies for a hybrid manufacturing/remanufacturing system with product substitution. Computers & Industrial Engineering, 114, 206-222.
Akbari, A. (2020). The Simultaneous Lot-sizing and Scheduling Problem in Process Industries Using Hybrid MTS-MTO Production Systems: An Exploratory Case Study. NTNU.  
Alemany Díaz, M. d. M., Alarcón Valero, F., Oltra Badenes, R. F., & Lario Esteban, F. C. (2013). Reasignación óptima del inventario a pedidos en empresas cerámicas caracterizadas por la falta de homogeneidad en el producto (FHP). Boletín de la Sociedad Española de Cerámica y Vidrio, 52(1), 31-41.
Aslan, B., Stevenson, M., & Hendry, L. C. (2015). The applicability and impact of Enterprise Resource Planning (ERP) systems: Results from a mixed method study on Make-To-Order (MTO) companies. Computers in Industry, 70, 127-143.
Beemsterboer, B., Land, M., & Teunter, R. (2016). Hybrid MTO-MTS production planning: An explorative study. European Journal of Operational Research, 248(2), 453-461.
Beemsterboer, B., Land, M., & Teunter, R. (2017). Flexible lot sizing in hybrid make-to-order/make-to-stock production planning. European Journal of Operational Research, 260(3), 1014-1023.
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.
Brännlund, U., Lindberg, P. O., Nou, A., & Nilsson, J.-E. (1998). Railway timetabling using Lagrangian relaxation. Transportation science, 32(4), 358-369.
Cannas, V. G., Gosling, J., Pero, M., & Rossi, T. (2020). Determinants for order-fulfilment strategies in engineer-to-order companies: Insights from the machinery industry. International journal of production economics, 107743.
ElHafsi, M., Fang, J., & Hamouda, E. (2020). A novel decomposition-based method for solving general-product structure assemble-to-order systems. European Journal of Operational Research.
Ellabban, A., & Abdelmaguid, T. (2019). Optimized Production Control Policy for Hybrid MTS-MTO Glass Tube Manufacturing Using Simulation-Based Optimization. Paper presented at the 2019 8th International Conference on Industrial Technology and Management (ICITM).
Fathollahi-Fard, A. M., Hajiaghaei-Keshteli, M., Tian, G., & Li, Z. (2020). An adaptive Lagrangian relaxation-based algorithm for a coordinated water supply and wastewater collection network design problem. Information Sciences, 512, 1335-1359.
Fisher, M. L. (1981). The Lagrangian relaxation method for solving integer programming problems. Management science, 27(1), 1-18.
Geoffrion, A. M. (1974). Lagrangean relaxation for integer programming Approaches to integer programming (pp. 82-114): Springer.
Ghalehkhondabi, I., & Suer, G. (2018). Production line performance analysis within a MTS/MTO manufacturing framework: a queueing theory approach. Production, 28.
Hassannayebi, E., Zegordi, S. H., & Yaghini, M. (2016). Train timetabling for an urban rail transit line using a Lagrangian relaxation approach. Applied Mathematical Modelling, 40(23-24), 9892-9913.
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. Industrial Engineering & Management Systems, 19(1), 143-163.
Jansen, S., Atan, Z., Adan, I., & de Kok, T. (2019). Setting optimal planned leadtimes in configure-to-order assembly systems. European Journal of Operational Research, 273(2), 585-595.
Jia, Y., Weng, W., & Fujimura, S. (2017). A hybrid MTS-MTO production model with a dynamic decoupling point for flexible flow shops. Paper presented at the 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS).
Jing, F., & Mu, Y. (2019). Forecast horizon for dynamic lot sizing model under product substitution and perishable inventories. Computers & Operations Research, 110, 77-87.
Kalantari, M., Rabbani, M., & Ebadian, M. (2011). A decision support system for order acceptance/rejection in hybrid MTS/MTO production systems. Applied Mathematical Modelling, 35(3), 1363-1377.
Khalilabadi, S. M. G., Zegordi, S. H., & Nikbakhsh, E. (2020). A multi-stage stochastic programming approach for supply chain risk mitigation via product substitution. Computers & Industrial Engineering, 149, 106786.
Kim, E., & Min, D. (2021). A two-stage hybrid manufacturing model with controllable make-to-order production rates. Journal of manufacturing systems, 60, 676-691.
Kloos, K., & Pibernik, R. (2020). Allocation planning under service-level contracts. European Journal of Operational Research, 280(1), 203-218.
Lai, Y.-J., & Hwang, C.-L. (1994). Fuzzy multiple objective decision making Fuzzy Multiple Objective Decision Making (pp. 139-262): Springer.
Lang, J. C. (2009). Production and inventory management with substitutions (Vol. 636): Springer Science & Business Media.
Laurikainen, A. (2020). ANALYZING AND DEVELOPING THE ORDER FULFILLMENT PROCESS IN MAKE TO ORDER BUSINESS.
Li, Q., He, Q.-M., & Wu, X. (2016). Timing order fulfillment of capital goods under a constrained capacity. Annals of Operations Research, 241(1-2), 431-456.
Lupaş, A., Mache, D. H., & Müller, M. W. (1995). Weighted L p-approximation of derivatives by the method of Gammaoperators. Results in Mathematics, 28(3-4), 277-286.
Mavrotas, G. (2009). Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Applied mathematics and computation, 213(2), 455-465.
Mello, M. H., Gosling, J., Naim, M. M., Strandhagen, J. O., & Brett, P. O. (2017). Improving coordination in an engineer-to-order supply chain using a soft systems approach. Production Planning & Control, 28(2), 89-107.
Muckstadt, J. A., & Koenig, S. A. (1977). An application of Lagrangian relaxation to scheduling in power-generation systems. Operations Research, 25(3), 387-403.
Naderi, B., & Roshanaei, V. (2019). Branch-Relax-and-Check: A tractable decomposition method for order acceptance and identical parallel machine scheduling. European Journal of Operational Research.
Nguyen, S. (2016). A learning and optimizing system for order acceptance and scheduling. The International Journal of Advanced Manufacturing Technology, 86(5), 2021-2036.
Noroozi, A., Mazdeh, M. M., Heydari, M., & Rasti-Barzoki, M. (2018). Coordinating order acceptance and integrated production-distribution scheduling with batch delivery considering Third Party Logistics distribution. Journal of manufacturing systems, 46, 29-45.
Ongsakul, W., & Petcharaks, N. (2004). Unit commitment by enhanced adaptive Lagrangian relaxation. IEEE Transactions on Power Systems, 19(1), 620-628.
Peeters, K., & van Ooijen, H. (2020). Hybrid make-to-stock and make-to-order systems: a taxonomic review. International Journal of Production Research, 58(15), 4659-4688.
Pibernik, R. (2005). Advanced available-to-promise: Classification, selected methods and requirements for operations and inventory management. International journal of production economics, 93, 239-252.
Rafiei, H., Rabbani, M., & Alimardani, M. (2013). Novel bi-level hierarchical production planning in hybrid MTS/MTO production contexts. International Journal of Production Research, 51(5), 1331-1346.
Rafiei, H., Rabbani, M., Vafa-Arani, H., & Bodaghi, G. (2017). Production-inventory analysis of single-station parallel machine make-to-stock/make-to-order system with random demands and lead times. International Journal of Management Science and Engineering Management, 12(1), 33-44.
Raturi, A. S., Meredith, J. R., McCutcheon, D. M., & Camm, J. D. (1990). Coping with the build‐to‐forecast environment. Journal of Operations Management, 9(2), 230-249.
Sarvestani, H. K., Zadeh, A., Seyfi, M., & Rasti-Barzoki, M. (2019). Integrated order acceptance and supply chain scheduling problem with supplier selection and due date assignment. Applied Soft Computing, 75, 72-83.
Shabtay, D., Gaspar, N., & Kaspi, M. (2013). A survey on offline scheduling with rejection. Journal of scheduling, 16(1), 3-28.
Shapiro, B. P., Rangan, V. K., Sviokla, J. J., Paul, & Meisel, i. (1992). Staple yourself to an order: Harvard Business Review.
Slotnick, S. A. (2011). Order acceptance and scheduling: A taxonomy and review. European Journal of Operational Research, 212(1), 1-11.
Steuer, R. E. (1986). Multiple criteria optimization. Theory, computation and applications.
Tapia-Leon, R., Vega-Neyra, X., Chavez-Soriano, P., & Ramos-Palomino, E. (2019). Improving the Order Fulfillment Process in a Textile Company using Lean Tools. Paper presented at the 2019 Congreso Internacional de Innovación y Tendencias en Ingenieria (CONIITI).
Tsao, Y.-C., Raj, P. V. R. P., & Yu, V. (2019). Product substitution in different weights and brands considering customer segmentation and panic buying behavior. Industrial Marketing Management, 77, 209-220.
Van Roy, T. J. (1983). Cross decomposition for mixed integer programming. Mathematical programming, 25(1), 46-63.
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. Computers & Industrial Engineering, 127, 841-852.
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.
Yano, S., Nagasawa, K., Morikawa, K., & Takahashi, K. (2019). A Dynamic Switching Policy with Thresholds of Inventory Level and Waiting Orders for MTS/MTO Hybrid Production Systems. Procedia Manufacturing, 39, 1076-1081.
Yousefnejad, H., & Esmaeili, M. (2020). Tactical production planning in a hybrid MTS/MTO system using Stackelberg game. Operational Research, 20(3), 1791-1809.
Zhang, Z., Guo, C., Wei, Q., Guo, Z., & Gao, L. (2021). A bi-objective stochastic order planning problem in make-to-order multi-site textile manufacturing. Computers & Industrial Engineering, 158, 107367.
Zhao, X., Luh, P. B., & Wang, J. (1999). Surrogate gradient algorithm for Lagrangian relaxation. Journal of optimization Theory and Applications, 100(3), 699-712.
Zheng, J.-N., Chien, C.-F., & Wu, J.-Z. (2018). Multi-objective demand fulfillment problem for solar cell industry. Computers & Industrial Engineering, 125, 688-694.