Presenting and Solving a Three-layer Supply Chain Model to Maximize Quality and Minimize the Estimated Delivery Time

Document Type : Original Article

Authors

1 MSc, Faculty of Industrial Engineering, Islamic Azad University, Naragh branch,Naragh, Iran

2 Assistant Prof., Faculty of Industrial Engineering, Islamic Azad University, Bonab branch, Bonab, Iran

3 Assistant Prof., Faculty of Industrial Engineering, Islamic Azad University, Naragh branch, Naragh, Iran

Abstract

Today, the supply chain and evaluating its costs are of great significance. The number of layers of the supply chain and the structure of the interactions between these layers can play an important role in achieving the optimal performance of the supply chain. Besides, it is undeniable that service provision and product transportation from a chain layer to the next one do not take the same amount of time because due to different reasons such as transportation challenges, there would always be the possibility that transportation doesn’t go as planned.  Therefore, by proposing a multi-purpose model, this study seeks to maximize the profit and the quality of the products transported through the supply chain, as well as minimizing the total delivery time.  After presenting the model, it is solved by applying deterministic algorithms using GAMS software, and also NSGAII and MOIWO non-deterministic meta-heuristic algorithms using proper software. Then, these two solutions are compared with each other. Results indicate that MOIWO meta-heuristic algorithm is superior to NSGAII in all indicators except for the NPS indicator.

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- Alhaj, M. A., Svetinovic, D., & Diabat, A. (2016). A carbon-sensitive two-echelon-inventory supply chain model with stochastic demand. Resources, Conservation and Recycling, 108, 82–87. article.
- Baghalian, A., Rezapour, S., & Farahani, R. Z. (2013). Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case. European Journal of Operational Research, 227(1), 199–215. article.
- Bookbinder, J. H., & Cakanyildirim, M. (1999). Random lead times and expedited orders in (Q, r) inventory systems. European Journal of Operational Research, 115(2), 300–313. article.
- 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. article.
- Hossain, M. S. J., Ohaiba, M. M., & Sarker, B. R. (2017). An optimal vendor-buyer cooperative policy under generalized lead-time distribution with penalty cost for delivery lateness. International Journal of Production Economics, 188, 50–62. article.
- Kaya, O., & Urek, B. (2016). A mixed integer nonlinear programming model and heuristic solutions for location, inventory and pricing decisions in a closed loop supply chain. Computers & Operations Research, 65, 93–103. article.
- Kiesmüller, G. P., De Kok, A. G., & Fransoo, J. C. (2005). Transportation mode selection with positive manufacturing lead time. Transportation Research Part E: Logistics and Transportation Review, 41(6), 511–530. article.
- Liang, X., Ma, L., Wang, H., & Yan, H. (2017). Inventory Control and Pricing with Alternative Delivery Times. In Inventory Management with Alternative Delivery Times (pp. 63–72). incollection, Springer.
- Ramasesh, R. V, Ord, J. K., Hayya, J. C., & Pan, A. (1991). Sole versus dual sourcing in stochastic lead-time (s, Q) inventory models. Management Science, 37(4), 428–443. article.
- Talaei, M., Moghaddam, B. F., Pishvaee, M. S., Bozorgi-Amiri, A., & Gholamnejad, S. (2016). A robust fuzzy optimization model for carbon-efficient closed-loop supply chain network design problem: a numerical illustration in electronics industry. Journal of Cleaner Production, 113, 662–673. article.
- Wang, X., & Disney, S. M. (2017). Mitigating variance amplification under stochastic lead-time: The proportional control approach. European Journal of Operational Research, 256(1), 151–162. article.
- Wei, J., Govindan, K., Li, Y., & Zhao, J. (2015). Pricing and collecting decisions in a closed-loop supply chain with symmetric and asymmetric information. Computers & Operations Research, 54, 257–265. article.
- Zhang, J., Lam, W. H. K., & Chen, B. Y. (2016). On-time delivery probabilistic models for the vehicle routing problem with stochastic demands and time windows. European Journal of Operational Research, 249(1), 144–154. article.
- Zohal, M., & Soleimani, H. (2016). Developing an ant colony approach for green closed-loop supply chain network design: a case study in gold industry. Journal of Cleaner Production, 133, 314–337. article.
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