Designing a Mathematical Optimizing Model of the Integrated Supply Chain Network at Strategic and Tactical Levels

Document Type : Research Paper

Authors

1 Assistance Professor, Department of Industrial Management, Faculty of Economics and Administrative Sciences, University of Mazandaran, Babolsar, Iran.

2 MSc. Student, Department of Industrial Management- Operation Research, Faculty of Management, University of Tehran, Tehran, Iran.

3 MSc. Student, Department of Industrial Management- Supply chain, Faculty of Management, University of Tehran, Tehran, Iran.

Abstract

Objective: In today's competitive economy, the supply of a product in an optimal quantity, quality and minimum cost at the right time are the pillars of success in a business. A continually efficient supply chain also plays an important role in solidifying this success. This research aims to model an integrated supply chain network to achieve maximum profit while minimizing the overall response time.
Methods: In view of the importance of supply chain management and logistics in recent times, the design and optimization of the supply chain network are extremely critical. This is mostly because, at each decision level, a well-optimized decision will result in a competitive advantage in the market.  In this research, the said design and optimization is explained in two levels; a strategic level where a supply chain is designed, and a tactical level dedicated to operational planning of the supply chain. In this paper, a mathematical model has been formulated for these levels. To verify and validate the proposed model, the acquired data from a computer case manufacturing company are examined and the outcomes were highlighted.
Results: This paper provides a tool in order to optimize the supply chain network. This tool can be useful for any manager or for those who are planning a supply chain from production or designing a product distribution network. Managers can also use this model to ensure the performance parameters of a particular supply chain.
Conclusion: The results show that the proposed model can provide an effective and easy-to-follow method to achieve a well-established plan in an integrated supply chain

Keywords

References


Altiparmak, F., Gen, M., Lin, L., and T. Paksoy (2006). A genetic algorithm approach for multi-objective optimization of supply chain networks. Computers & Industrial Engineering, 51 (1), 196-215.
Aminpour, Saeed, Irajpour, Alireza, Yazdani, Mehdi & Mohtashami, Ali (2020). The Design of a Multi-directional Network Chain Model Offering a Closed Loop in the Automotive Industry by Providing Energy and Time Efficiency Programs. Industrial Management Journal, 12(1), 319-343. (in Persian).
Anthony, R. N. (1965). Planning and control systems: A framework for analysis [by]. Division of Research, Graduate School of Business Administration, Harvard University.
Aras, N., & Bilge, Ü. (2018). Robust supply chain network design with multi-products for a company in the food sector. Applied Mathematical Modelling, 60, 526-539.
Barbosa-Povoa, A. P., Mota, B., & Carvalho, A. (2018). How to design and plan sustainable supply chains through optimization models? Pesquisa Operacional, 38(3), 363-388.
Bashiri, M., Rezanezhad, M., Tavakkoli-Moghaddam, R., & Hasanzadeh, H. (2018). Mathematical modeling for a p-mobile hub location problem in a dynamic environment by a genetic algorithm. Applied Mathematical Modelling, 54, 151-169.
Biuki, M., Kazemi, A., & Alinezhad, A. (2020). An integrated location-routing-inventory model for sustainable design of a perishable products supply chain network. Journal of Cleaner Production, 260, 120842.
Cigolini, R., Pero, M., Rossi, T., & Sianesi, A. (2014). Linking supply chain configuration to supply chain perfrmance: A discrete event simulation model. Simulation Modelling Practice and Theory, 40, 1-11.
da Silveira Farias, E., Li, J. Q., Galvez, J. P., & Borenstein, D. (2017). Simple heuristic for the strategic supply chain design of large-scale networks: A Brazilian case study. Computers & Industrial Engineering, 113, 746-756.
Durmaz, Y. G., & Bilgen, B. (2020). Multi-objective optimization of sustainable biomass supply chain network design. Applied Energy, 272, 115259.
Ejikeme-Ugwu, E., Liu, S., & Wang, M. (2011). Integrated refinery planning under product demand uncertainty. In Computer Aided Chemical Engineering (Vol. 29, pp. 950-954).
Fallah Lajimi, H., Jafarnejad, A., Mehrgan, M., Olfat, L. (2015). Configuring integrated supply chain network stochastic strategic. Industrial Management Journal, 7(1), 83-105. (in Persian).
Fazlollahtabar, H., Mahdavi, I., & Mohajeri, A. (2013). Applying fuzzy mathematical programming approach to optimize a multiple supply network in uncertain condition with comparative analysis. Applied Soft Computing, 13(1), 550-562.
Gholizadeh, H., Tajdin, A., & Javadian, N. (2020). A closed-loop supply chain robust optimization for disposable appliances. Neural Computing and Applications, 32(8), 3967-3985.
Govindan, K., Jafarian, A., & Nourbakhsh, V. (2015). Bi-objective integrating sustainable order allocation and sustainable supply chain network strategic design with stochastic demand using a novel robust hybrid multi-objective metaheuristic. Computers & Operations Research, 62, 112-130.
Graves, S. C., & Willems, S. P. (2005). Optimizing the supply chain configuration for new products. Management science, 51(8), 1165-1180.
Handfield, R. B., & Nichols Jr, E. L. (1999). Introduction to. Supply Chain Management, Prentice Hall, Englewood Cliffs, NJ.
Hong, Z., Dai, W., Luh, H., & Yang, C. (2018). Optimal configuration of a green product supply chain with guaranteed service time and emission constraints. European Journal of Operational Research, 266(2), 663-677.
Hosseini-Motlagh, S. M., Samani, M. R. G., & Saadi, F. A. (2019). Strategic optimization of wheat supply chain network under uncertainty: a real case study. Operational Research, 1-41.
Jabbarzadeh, A., Haughton, M., & Pourmehdi, F. (2019). A robust optimization model for efficient and green supply chain planning with postponement strategy. International Journal of Production Economics, 214, 266-283.
Keyvanshokooh, E., Ryan, S. M., & Kabir, E. (2016). Hybrid robust and stochastic optimization for closed-loop supply chain network design using accelerated Benders decomposition. European Journal of Operational Research, 249(1), 76-92.
Kim, J., & Rogers, K. J. (2005). An object‐oriented approach for building a flexible supply chain model. International Journal of Physical Distribution & Logistics Management.
Kumar, S. K., & Tiwari, M. K. (2013). Supply chain system design integrated with risk pooling. Computers & Industrial Engineering, 64(2), 580-588.
Lee, Y. H., & Kwon, S. G. (2010). The hybrid planning algorithm for the distribution center operation using tabu search and decomposed optimization. Expert systems with applications, 37(4), 3094-3103.
Mohammadi, A., Alem Tabriz, A., Pishvaee, M. (2018). Designing Green Closed-loop Supply Chain Network with Financial Decisions under Uncertainty. Industrial Management Journal, 10(1), 61-84. (in Persian).
Mota, B., Gomes, M. I., Carvalho, A., & Barbosa-Povoa, A. P. (2018). Sustainable supply chains: An integrated modeling approach under uncertainty. Omega, 77, 32-57.
Negahban, A., & Dehghanimohammadabadi, M. (2018). Optimizing the supply chain configuration and production-sales policies for new products over multiple planning horizons. International Journal of Production Economics, 196, 150-162.
Nezamoddini, N., Gholami, A., & Aqlan, F. (2020). A risk-based optimization framework for integrated supply chains using genetic algorithm and artificial neural networks. International Journal of Production Economics, 225, 107569.
Qiu, X., Zhang, L., Ren, Y., Suganthan, P. N., & Amaratunga, G. (2014, December). Ensemble deep learning for regression and time series forecasting. In 2014 IEEE symposium on computational intelligence in ensemble learning (CIEL) (pp. 1-6). IEEE.
Rahmani, D., & Mahoodian, V. (2017). Strategic and operational supply chain network design to reduce carbon emission considering reliability and robustness. Journal of Cleaner Production, 149, 607-620.
Ramezani, M., Bashiri, M., & Tavakkoli-Moghaddam, R. (2013). A new multi-objective stochastic model for a forward/reverse logistic network design with responsiveness and quality level. Applied Mathematical Modelling, 37(1-2), 328-344.
Ross, J. W. (2003). Creating a strategic IT architecture competency: Learning in stages.
Soleimani, H., & Kannan, G. (2015). A hybrid particle swarm optimization and genetic algorithm for closed-loop supply chain network design in large-scale networks. Applied Mathematical Modelling, 39(14), 3990-4012.
Thanh, P. N., Bostel, N., & Péton, O. (2012). A DC programming heuristic applied to the logistics network design problem. International Journal of Production Economics, 135(1), 94-105.
Vahdani, B., & Mohammadi, M. (2015). A bi-objective interval-stochastic robust optimization model for designing closed loop supply chain network with multi-priority queuing system. International Journal of Production Economics, 170, 67-87.
Wang, R. C., & Liang, T. F. (2005). Applying possibilistic linear programming to aggregate production planning. International journal of production economics, 98(3), 328-341.
Yang, D., Li, X., Jiao, R. J., & Wang, B. (2018). Decision support to product configuration considering component replenishment uncertainty: A stochastic programming approach. Decision Support Systems, 105, 108-118.
Zhang, L. L., Lee, C., & Zhang, S. (2016). An integrated model for strategic supply chain design: Formulation and ABC-based solution approach. Expert Systems with Applications, 52, 39-49
Industrial Management Journal
Volume 12, Issue 4
2020
Pages 545-577

Files

Share

How to cite

Statistics