An Optimization Model for Natural Gas Supply Chain through a Cost Approach under Uncertainty
Abstract:
Natural gas, as one of the most important sources of energy for many of the industrial and domestic users all over the world, has a complex, huge supply chain which is in need of heavy investments in all the phases of exploration, extraction, production, transportation, storage and distribution. The main purpose of supply chain is to meet customers’ need efficiently and with minimum cost. In this study, with the aim of minimizing economic costs, different levels of natural gas supply chain in the form of a multi-echelon, multi-period fuzzy linear programming have been modeled. In this model, different constraints including constraints on demand satisfaction, capacity, input/output balance and presence/absence of a path have been defined. The obtained results suggest efficiency of the recommended model in optimal allocation and reduction of supply chain costs.
Keywords: Cost Approach, Fuzzy Theory, Linear Programming, Natural Gas Supply Chain.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1097028
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[1] MohammadiBidhandi, H., MohdYusuff, R., Megat Ahmad, M. M. H., & Abu Bakar, M. R. (2009). Development of a new approach for deterministic supply chain network design. European Journal of Operational Research, 198(1), 121-128.
[2] Contesse, L., Ferrer, J. C., &Maturana, S. (2005). A mixed-integer programming model for gas purchase and transportation. Annals of Operations Research, 139(1), 39-63.
[3] Hamedi, M., ZanjiraniFarahani, R., Husseini, M. M., &Esmaeilian, G. R. (2009). A distribution planning model for natural gas supply chain: A case study. Energy Policy, 37(3), 799-812.
[4] Vasconcelos, C. D., Lourenço, S. R., Gracias, A. C., &Cassiano, D. A. (2013). Network flows modeling applied to the natural gas pipeline in Brazil. Journal of Natural Gas Science and Engineering, 14, 211-224.
[5] Iran Energy Balance. (2011). Energy Management Department. Institute for International Energy Studies (IIES), subsidiary of Ministry of Oil & Gas.
[6] HO, C. J. (1989). Evaluating the impact of operating environments on MRP system nervousness. The International Journal of Production Research, 27(7), 1115-1135.
[7] Zimmermann, H. J. (1978). Fuzzy programming and linear programming with several objective functions. Fuzzy sets and systems, 1(1), 45-55.
[8] Jiménez, M., Arenas, M., & Bilbao, A. (2007). Linear programming with fuzzy parameters: an interactive method resolution. European Journal of Operational Research, 177(3), 1599-1609.
[9] Wang, R. C., & Liang, T. F. (2005). Applying possibilistic linear programming to aggregate production planning. International Journal of Production Economics, 98(3), 328-341.
[10] Arenas Parra, M., Bilbao Terol, A., Pérez Gladish, B., &Rodrı́guezUrı́a, M. V. (2005). Solving a multiobjectivepossibilistic problem through compromise programming. European Journal of Operational Research, 164(3), 748-759.