A Simulation-Based Bi-Objective Optimization Model for Supply Chain Inventory Replenishment: A Case Study of the Electric Industry

Document Type : Original Article

Authors

1 Ph.D. student, Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabatabai University, Tehran, Iran.

2 Professor, Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabatabai University, Tehran, Iran.

3 Associate Professor, Department of Industrial Engineering, School of Industrial and Mechanical Engineering, Islamic Azad University, Qazvin Branch, Qazvin, Iran.

Abstract

Introduction: The electricity sector plays a crucial role in the country's economy. Therefore, any disruptions in the supply chain of this industry can result in the loss of economic benefits and decrease the competitiveness of industries dependent on this sector. The industry structure analysis of the electricity sector has shown that the lack of proper relationships between entities involved in the supply of goods and equipment leads to disruptions in the electricity supply chain. On the other hand, the specific political and economic conditions of the country, the presence of natural disasters, and high levels of change in the Middle East region have had significant impacts on increasing uncertainty at various levels of the supply chain in this sector. Considering the high uncertainty in the procurement of components in this industry, this study focuses on presenting a set of scenarios for replenishing goods in the supply chain entities of this sector.
Methods: To achieve this, a probabilistic four-echelon model consisting of a supplier, distributor, retailer, and customer was presented to minimize total inventory costs and the ratio of unmet customer demand based on the (R, Q) policy. Furthermore, by searching organizational documents, interviewing industry experts, and utilizing warehouse management software, data for the model was collected. Subsequently, through experimental design, initial solutions were provided for the differential evolutionary algorithm, and based on this algorithm, different values for reorder points and order quantities were determined. By employing simulation methods, the model's objective values were estimated, and the set of solutions was illustrated in a Pareto chart.
Result and Discussion: Research findings have shown that increasing the average inventory levels of retailers' warehouses leads to a decrease in the proportion of unmet customer demand. This occurs when different reorder point values for two retailers have high levels, but considering the probabilistic demand function, the order quantity can vary. On the other hand, reducing ordering and inventory costs leads to an increase in unmet customer demand. In other words, when reorder point values and low order quantities lead to inventory reduction, customer dissatisfaction increases. The Differential Evolution Algorithm used in this study has accelerated the process of finding solutions and improved model efficiency. This algorithm considers values between high and low levels of reorder points and order quantities, presenting multiple objective function values. Utilizing simulation methods to estimate the probabilistic objective functions employed in the model has increased the speed of executing multiple scenarios, aiding in cost reduction and model execution time.
Conclusions: Based on the results of this research, electric equipment with high innovation should have a low reorder point and a high order quantity in the supply chain. This is because the short product lifespan renders the product obsolete along the chain, lacking customer demand and consequently increasing the chain's costs. The computational results of this study indicate that a 105% increase in inventory leads to a 104% increase in customer satisfaction and a 95% decrease in lost sales costs. However, considering the high purchasing costs and large product volume during ordering, a scenario aligned with financial conditions and warehouse capacity should be selected for chain entities.

Keywords

Main Subjects

References

  1. Azar, A., Shahbazi, M., Yazdani, H., & Mahmoudian, O. (2019). Assessment of Supply Chain Resilience of Electric Power Industry in Iran: Fuzzy Approach. Journal of Energy Planning and Policy Research, 5(1), 7-28.
  2. Bahrampour, P., Safari, M., & Taraghdari, M. B. (2016). Modeling multi-product multi-stage supply chain network design. Procedia economics and finance, 36, 70-80.
  3. Baloch, G., Gzara, F., & Elhedhli, S. (2022). Covid-19 PPE distribution planning with demand priorities and supply uncertainties. Computers & Operations Research, 146, 105913.
  4. Blessley, M., & Mudambi, S. M. (2022). A trade war and a pandemic: Disruption and resilience in the food bank supply chain. Industrial Marketing Management, 102, 58-73.
  5. Brandao, M. S., & Godinho-Filho, M. (2022). Is a multiple supply chain management perspective a new way to manage global supply chains toward sustainability?. Journal of Cleaner Production, 375, 134046.
  6. Bressanelli, G., Pigosso, D. C., Saccani, N., & Perona, M. (2021). Enablers, levers and benefits of Circular Economy in the Electrical and Electronic Equipment supply chain: A literature review. Journal of Cleaner Production, 298, 126819.
  7. Cui, L., Yue, S., Nghiem, X. H., & Duan, M. (2023). Exploring the risk and economic vulnerability of global energy supply chain interruption in the context of Russo-Ukrainian war. Resources Policy, 81, 103373.
  8. Duan, Q., & Liao, T. W. (2013). Optimization of replenishment policies for decentralized and centralized capacitated supply chains under various demands. International Journal of Production Economics, 142(1), 194-204.
  9. Gozgor, G., Khalfaoui, R., & Yarovaya, L. (2023). Global supply chain pressure and commodity markets: Evidence from multiple wavelet and quantile connectedness analyses. Finance Research Letters, 54, 103791.
  10. Hegerty, B., Hung, C. C., & Kasprak, K. (2009, November). A comparative study on differential evolution and genetic algorithms for some combinatorial problems. In Proceedings of 8th Mexican international conference on artificial intelligence, 9, 13.
  11. Iwan, M., Akmeliawati, R., Faisal, T., & Al-Assadi, H. M. (2012). Performance comparison of differential evolution and particle swarm optimization in constrained optimization. Procedia Engineering, 41, 1323-1328.
  12. Iyer, A. V., Vedantam, A., & Lacourbe, P. (2023). Recycled content claims under demand benefit and supply uncertainty: Multi-period model and application to glasswool insulation. European Journal of Operational Research, 309(2), 745-761.
  13. Jafarnezhad Chaghooshi, A., Kazemi, A., & Arab, A. (2016). Identification and Prioritization of Supplier’s Resiliency Evaluation Criteria Based on BWM. The Journal of Industrial Management Perspective, 6(3), 159-186. (In Persian).
  14. Jha, H., & Mohan, U. (2023). A multi-period discrete event simulation model for comparing synchronous and asynchronous facility reopening in global supply chains affected by disruption. Supply Chain Analytics, 2, 100010.
  15. Juan, A. A., Kelton, W. D., Currie, C. S., & Faulin, J. (2018, December). Simheuristics applications: dealing with uncertainty in logistics, transportation, and other supply chain areas. In 2018 winter simulation conference (WSC), 3048-3059. IEEE.
  16. Karakaya, S., & Balcik, B. (2023). Developing a National Pandemic Vaccination Calendar Under Supply Uncertainty. Available at SSRN 4380147.
  17. Kazancoglu, Y., Ekinci, E., Mangla, S. K., Sezer, M. D., & Ozbiltekin-Pala, M. (2023). Impact of epidemic outbreaks (COVID-19) on global supply chains: A case of trade between Turkey and China. Socio-Economic Planning Sciences, 85, 101494.
  18. Khishtandar, S., Zandieh, M., Dorri Nokarani, B., & Ranaei Siadat, S. O. (2016). Evolutionary Algorithms for Location Allocation Biomethane Supply Chain Problem. Journal of Industrial Management Perspective, 6(3), Autumn 2016, 29-54.
  19. Kumar, S., Chary, G. H. V. C., & Dastidar, M. G. (2015). Optimization studies on coal–oil agglomeration using Taguchi (L16) experimental design. Fuel, 141, 9-16.
  20. Kwon, O., Im, G. P., & Lee, K. C. (2007). MACE-SCM: A multi-agent and case-based reasoning collaboration mechanism for supply chain management under supply and demand uncertainties. Expert systems with applications, 33(3), 690-705.
  21. Mohammed, A., Al-shaibani, M. S., & Duffuaa, S. O. (2023). A meta-heuristic-based algorithm for designing multi-objective multi-echelon supply chain network. Applied Soft Computing, 147, 110774.
  22. Mora, C. J., Malik, A., & Murray, J. (2024). From mines to consumption via global supply chains: A viewpoint on assessing modern slavery and disease interactions. Environmental Impact Assessment Review, 104, 107353.
  23. Morovati Sharifabadi, A., Mirghafouri, S. H., & Mir Fakhreddini, S. H. (2021). Designing a probabilistic model of sustainable supply chain in the electricity industry with the influence of renewable products. Organizational Resources Management Researchs, 11(1), 105-128.
  24. Mousazadeh, M., Torabi, S. A., & Zahiri, B. (2015). A robust possibilistic programming approach for pharmaceutical supply chain network design. Computers & chemical engineering, 82, 115-128.
  25. Naderi, B., Govindan, K., & Soleimani, H. (2020). A Benders decomposition approach for a real case supply chain network design with capacity acquisition and transporter planning: wheat distribution network. Annals of Operations Research, 291, 685-705.
  26. Nya, D. N., & Abouaïssa, H. (2023). A robust inventory management in dynamic supply chains using an adaptive model-free control. Computers & Chemical Engineering, 179, 108434.
  27. Pan, W., So, K. C., & Xiao, G. (2022). Benefits of backup sourcing for components in assembly systems under supply uncertainty. European Journal of Operational Research, 302(1), 158-171.
  28. Pellicelli, M. (2023). Chapter Eight - War in Europe: another blow to the global supply chains, The Digital Transformation of Supply Chain Management, Logistics and Law,  213-226.
  29. Shoja, A., Molla-Alizadeh-Zavardehi, S., & Niroomand, S. (2019). Adaptive meta-heuristic algorithms for flexible supply chain network design problem with different delivery modes. Computers & Industrial Engineering, 138, 106107.
  30. Shurrab, H. (2019). Tactical planning in engineer-to-order environments. Chalmers Tekniska Hogskola (Sweden).
  31. Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 11, 341-359.
  32. Tsai, S. C., & Chen, S. T. (2017). A simulation-based multi-objective optimization framework: A case study on inventory management. Omega, 70, 148-159.
  33. Vieira, A. A., Figueira, J. R., & Fragoso, R. (2023). A multi-objective simulation-based decision support tool for wine supply chain design and risk management under sustainability goals. Expert Systems with Applications, 120757.
  34. Yang, Z., Tang, K., & Yao, X. (2011). Scalability of generalized adaptive differential evolution for large-scale continuous optimization. Soft Computing, 15, 2141-2155.
  35. Zahiri, B., Zhuang, J., & Mohammadi, M. (2017). Toward an integrated sustainable-resilient supply chain: A pharmaceutical case study. Transportation research part e: logistics and transportation review, 103, 109-142.
  36. Zandieh, M., & Ahmadi, E. (2014). Robust and stable scheduling for FJSP under random machine breakdown by use of genetic algorithm and simulation. Industrial Management Journal, 6(3), 511-534.
  37. Zhang, F. B., Wang, Z. L., & Yang, M. Y. (2015). Assessing the applicability of the Taguchi design method to an interrill erosion study. Journal of Hydrology, 521, 65-73.

Files

Share

How to cite

Statistics