Mathematical Model for Planning and Optimisation of Petroleum Supply Chain Under Uncertainty
Article Sidebar
Main Article Content
Abstract
This work focuses on the planning and petroleum
industry logistic and supply chains from raw materials to
production distribution by developing a two-stage stochastic
linear programming with recourse techniques. The paper
investigates and creates a set of mathematical models which
considers different parameters such as production of crude
oil, transportation plan, production levels, operating
conditions, production distribution plans, prices of raw
materials and products under significant source of
uncertainty which represent the market demand of refinery
products. Expected Value of Perfect Information (EVPI) is
computed a maximum amount a decision maker that should
pay for additional information gives a perfect signal as to
the state of nature.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
Lababidi, H. M., Ahmed, M. A., Alatiqi, I. M., & Al-Enzi, A. F. (2004). Optimizing the supply chain of a petrochemical company under uncertain operating and economic conditions. Industrial & Engineering Chemistry Research,43(1), 63-73.
Das, S. K., & Abdel-Malek, L. (2003). Modelling the flexibility of order quantities and lead-times in supply chains. International Journal of Production Economics, 85(2), 171-181.
Al-Othman, W. B., Lababidi, H. M., Alatiqi, I. M., & Al-Shayji, K. (2008). Supply chain optimization of petroleum organization under uncertainty in market demands and prices. European Journal of Operational Research, 189(3), 822-840.
Grossmann, I. E., Van Den Heever, Susara A, & Harjunkoski, I. (2002). Discrete optimization methods and their role in the integration of planning and scheduling. AIChE Symposium Series, 150-168.
McDonald, C. M., & Karimi, I. A. (1997). Planning and scheduling of parallel semicontinuous processes. 1. Production planning. Industrial & Engineering Chemistry Research, 36(7), 2691-2700.
Perea, E., Grossmann, I., Ydstie, E., & Tahmassebi, T. (2000). Dynamic modeling and classical control theory for supply chain management. Computers & Chemical Engineering, 24(2), 1143-1149
Lyer, R. R., & Grossmann, I. E. (1998). A bi-level decomposition algorithm for long-range planning of process networks. Industrial & Engineering Chemistry Research, 37(2), 474-481.
Moro, L., Zanin, A., & Pinto, J. (1998). A planning model for refinery diesel production. Computers & Chemical Engineering, 22, S1039-S1042.
Dantzig, G. B. (1955). Linear programming under uncertainty. Management Science, 1(3-4), 197-206.
Vladimirou, H., & Zenios, S. A. (1997). Stochastic linear programs with restricted recourse. European Journal of Operational Research, 101(1), 177-192.
Sahinidis, N., Grossmann, I., Fornari, R., & Chathrathi, M. (1989). Optimization model for long range planning in the chemical industry. Computers & Chemical Engineering, 13(9), 1049-1063.
Helton, J. C., Johnson, J. D., Sallaberry, C. J., & Storlie, C. B. (2006). Survey of sampling-based methods for uncertainty and sensitivity analysis. Reliability Engineering & System Safety, 91(10), 1175-1209.