Optimal Supply Chain Network with Multi-Echelon
DOI:
https://doi.org/10.37266/ISER.2019v7i2.pp102-115Keywords:
Supply ChainAbstract
The study of the effect of redistribution strategy and aggregation, on a multi-echelon supply chain network by managing demand volatility is discussed in this research. For this an operational supply chain design is considered. Multi-echelon network consisting of manufacturing plants, distribution centers, warehouses, and retailers is used to develop the case study. Aggregation strategy was analyzed in the context of single product and multi-product for a multi-period production problem under demand uncertainty. Product sourcing between echelons and distribution strategies are considered for the study. Objective was to use the redistribution strategy to optimize the objective functions for the network. The objective functions include minimization of total cost, minimization of overage and stock-out conditions, and maximization of the customer service level. The total cost function includes product flow, transportation cost and distance cost. The mathematical formulation is carried out in Mixed Integer Linear Programming (MILP) with the help of Generic Algebraic Modeling System (GAMS). Problem formulation considers three type of demand based on volatility and uncertainty cases as high, medium, and low. The research is divided into three main phases to discuss an optimal multi-echelon supply chain network for single product using aggregation strategy.References
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Tsiakis, P., Shah, N., & Pantelides, C.C. (2001). Design of multi-echelon supply chain networks under demand uncertainty. Industrial and Engineering Chemistry Research, 40(16), 3585-3604.
Zanjani, M., Nourelfath, M., & Ait-Kadi, D. (2010). A multi-stage stochastic programming approach for production planning with uncertainty in the quality of raw materials and demand. International Journal of Production Research, 48(16), 4701-4723
Beamon, B.M., & Fernandes, C. (2004). Supply-chain network configuration for product recovery. Production Planning & Control, 15(3), 270-281.
Daskin, M.S., Snyder, L.V., & Berger, R.T. (2005). Facility location in supply chain design. in Logistics systems: Design and optimization (pp. 39-65). Springer, Boston, MA.
Farahani, R.Z., & Elahipanah, M. (2008). A genetic algorithm to optimize the total cost and service level for just-in-time distribution in a supply chain. International Journal of Production Economics, 111(2), 229-243.
Pan, F., & Nagi, R. (2013). Multi-echelon supply chain network design in agile manufacturing. Omega, 41(6), 969-983.
Petridis, K. (2015). Optimal design of multi-echelon supply chain networks under normally distributed demand. Annals of Operations Research, 227(1), 63-91.
Sabri, E.H., & Beamon, B.M. (2000). A multi-objective approach to simultaneous strategic and operational planning in supply chain design. Omega, 28(5), 581-598.
Shen, Z.J.M., Coullard, C., & Daskin, M.S. (2003). A joint location-inventory model. Transportation Science, 37(1), 40-55.
Shen, Z.J.M., & Daskin, M.S. (2005). Trade-offs between customer service and cost in integrated supply chain design. Manufacturing and Service Operations Management, 7(3), 188-207.
Tsiakis, P., Shah, N., & Pantelides, C.C. (2001). Design of multi-echelon supply chain networks under demand uncertainty. Industrial and Engineering Chemistry Research, 40(16), 3585-3604.
Zanjani, M., Nourelfath, M., & Ait-Kadi, D. (2010). A multi-stage stochastic programming approach for production planning with uncertainty in the quality of raw materials and demand. International Journal of Production Research, 48(16), 4701-4723
Published
2019-12-30
How to Cite
Alsalem, Z. H., Harikrishnakumar, R., Maru, V., & Krishnan, K. (2019). Optimal Supply Chain Network with Multi-Echelon. Industrial and Systems Engineering Review, 7(2), 102-115. https://doi.org/10.37266/ISER.2019v7i2.pp102-115
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