A multi-period fuzzy mathematical programming model for crude oil supply chain network design considering budget and equipment limitations

Document Type: Research Paper

Authors

Iran University of Science and Technology

Abstract

The major oil industry upstream activities include the exploration, drilling, extraction, pipelines installation, and production of crude oil. In this paper, we develop a mathematical model to plan for theseoperations as a crude oil supply chain network design problem.The proposed multi-period mixed integer linear programming model entails both strategic (e.g., facility location and allocation) and tactical (e.g., project and production planning) decisions. With the objective of maximizing total Net Present Value (NPV) at the end of planning horizon, the decisions to be made comprise the location of the facilities, the flow of commodities and the amount of investment. The uncertain natures of important input parameters such as capital and operational cost, demand and price of crude oil, are taken into account via fuzzy theory. Finally, the performance of the developed model is investigated using the real data of Iranian South Oilfields.

Keywords

Main Subjects


ASEERI, A., GORMAN, P. & BAGAJEWICZ, M. J. 2004. Financial Risk Management in Offshore Oil Infrastructure Planning and Scheduling. Industrial & Engineering Chemistry Research, 43, 3063-3072.

BELLMAN, R. E. & ZADEH, L. A. 1970. Decision-Making in a Fuzzy Environment. Management Science, 17, B-141-B-164.

CARVALHO, M. C. A. & PINTO, J. M. 2006a. A bilevel decomposition technique for the optimal planning of offshore platforms. Brazilian Journal of Chemical Engineering, 23, 67-82.

CARVALHO, M. C. A. & PINTO, J. M. 2006b. An MILP model and solution technique for the planning of infrastructure in offshore oilfields. Journal of Petroleum Science and Engineering, 51, 97-110.

DAMGHANI, R. K. V. R. G. K. K. 2015. Optimization of multi-product, multi-period closed loop supply chain under uncertainty in product return rate: case study in Kalleh dairy company. Journal of Industrial and Systems  Engineering.

DEVINE, M. D. & LESSO, W. G. 1972. Models for the Minimum Cost Development of Offshore Oil Fields. Management Science, 18, B-378-B-387.

GUPTA, V. & GROSSMANN, I. E. 2012. An Efficient Multiperiod MINLP Model for Optimal Planning of Offshore Oil and Gas Field Infrastructure. Industrial & Engineering Chemistry Research, 51, 6823-6840.

GUPTA, V. & GROSSMANN, I. E. 2014. Multistage stochastic programming approach for offshore oilfield infrastructure planning under production sharing agreements and endogenous uncertainties. Journal of Petroleum Science and Engineering, 124, 180-197.

HENNIG, F., NYGREEN, B., CHRISTIANSEN, M., FAGERHOLT, K., FURMAN, K. C., SONG, J., KOCIS, G. R. & WARRICK, P. H. 2012. Maritime crude oil transportation – A split pickup and split delivery problem. European Journal of Operational Research, 218, 764-774.

IYER, R. R., GROSSMANN, I. E., VASANTHARAJAN, S. & CULLICK, A. S. 1998. Optimal Planning and Scheduling of Offshore Oil Field Infrastructure Investment and Operations. Industrial & Engineering Chemistry Research, 37, 1380-1397.

KOSMIDIS, V. D., PERKINS, J. D. & PISTIKOPOULOS, E. N. 2002. A Mixed Integer Optimization Strategy for Integrated Gas/Oil Production. In: JOHAN, G. & JAN VAN, S. (eds.) Computer Aided Chemical Engineering. Elsevier.

KOSMIDIS, V. D., PERKINS, J. D. & PISTIKOPOULOS, E. N. 2004. Optimization of Well Oil Rate Allocations in Petroleum Fields. Industrial & Engineering Chemistry Research, 43, 3513-3527.

LIU, B. 2010. Uncertainty Theory. Uncertainty Theory. Springer Berlin Heidelberg.

PEIDRO, D., MULA, J., POLER, R. & VERDEGAY, J.-L. 2009. Fuzzy optimization for supply chain planning under supply, demand and process uncertainties. Fuzzy Sets and Systems, 160, 2640-2657.

RIBAS, G., LEIRAS, A. & HAMACHER, S. 2011. Tactical planning of the oil supply chain: optimization under uncertainty. PRÉ-ANAIS XLIIISBPO.

SAHEBI & NICKEL 2014. Offshore oil network design with transportation alternatives. European Journal of Industrial Engineering, 8.

SAHEBI, H., NICKEL, S. & ASHAYERI, J. 2014. Strategic and tactical mathematical programming models within the crude oil supply chain context—A review. Computers & Chemical Engineering, 68, 56-77.

SHAH, N. K., LI, Z. & IERAPETRITOU, M. G. 2011. Petroleum Refining Operations: Key Issues, Advances, and Opportunities. Industrial & Engineering Chemistry Research, 50, 1161-1170.

SHAMS, H., MOGOUEE, M. D., JAMALI, F. & HAJI, A. 2012. A Survey on Fuzzy Linear Programming. American Journal of Scientific Research, 117-133.

SHEN, Q., CHU, F. & CHEN, H. 2011. A Lagrangian relaxation approach for a multi-mode inventory routing problem with transshipment in crude oil transportation. Computers & Chemical Engineering, 35, 2113-2123.

TANAKA†, H., OKUDA, T. & ASAI, K. 1973. On Fuzzy-Mathematical Programming. Journal of Cybernetics, 3, 37-46.

TARHAN, B., GROSSMANN, I. E. & GOEL, V. 2009. Stochastic Programming Approach for the Planning of Offshore Oil or Gas Field Infrastructure under Decision-Dependent Uncertainty. Industrial & Engineering Chemistry Research, 48, 3078-3097.

TSARBOPOULOU, C. 2000. Optimization of oil facilities and oil production. Optimisation of Oil Facilities and Oil Production.

VAN DEN HEEVER, S. A. & GROSSMANN, I. E. 2000. An Iterative Aggregation/Disaggregation Approach for the Solution of a Mixed-Integer Nonlinear Oilfield Infrastructure Planning Model. Industrial & Engineering Chemistry Research, 39, 1955-1971.