Tactical and Operational Planning of Multirefinery Networks under Uncertainty: An Iterative Integration Approach

The oil industry is increasingly interested in improving the planning of their operations, because of the dynamic nature of the oil business. This study intends to establish an iterative integration approach for the tactical and operational planning of multisite refining networks. Tactical and operational mathematical models are proposed. Both models are two-stage stochastic linear programs in which uncertainty is incorporated into the dominant random parameters at each decision level. Decisions made in the oil industry differ based on multisite network echelon (spatial integration) and planning horizon (temporal integration). Spatial integration is discussed at the tactical level, whereas temporal integration is discussed with respect to the interaction between the two levels. In the proposed temporal integration approach (iterative approach), there is a cyclic information flow between the two models. An industrial scale study using data from the Brazilian oil industry was conducted to discuss the benefi...

[1]  M. Bagajewicz,et al.  Financial risk management in the planning of refinery operations , 2006 .

[2]  Silvio Hamacher,et al.  Operational planning of oil refineries under uncertainty Special issue: Applied Stochastic Optimization , 2012 .

[3]  José M. Pinto,et al.  A general modeling framework for the operational planning of petroleum supply chains , 2004, Comput. Chem. Eng..

[4]  Ali Elkamel,et al.  Planning and Integration of Refinery and Petrochemical Operations: AL-QAHTANI:REFINERY O-BK , 2010 .

[5]  David E. Boyce,et al.  Construction of a real-world bilevel linear programming model of the highway network design problem , 1992, Ann. Oper. Res..

[6]  Ignacio E. Grossmann,et al.  Enterprise‐wide optimization: A new frontier in process systems engineering , 2005 .

[7]  Fengqi You,et al.  Design under uncertainty of hydrocarbon biorefinery supply chains: Multiobjective stochastic programming models, decomposition algorithm, and a Comparison between CVaR and downside risk , 2012 .

[8]  Stein W. Wallace,et al.  Generating Scenario Trees for Multistage Decision Problems , 2001, Manag. Sci..

[9]  J. M. Pinto,et al.  Mixed-Integer Programming Approach for Short-Term Crude Oil Scheduling , 2004 .

[10]  Efstratios N. Pistikopoulos,et al.  A bilevel programming framework for enterprise-wide process networks under uncertainty , 2004, Comput. Chem. Eng..

[11]  C. Floudas,et al.  Active constraint strategy for flexibility analysis in chemical processes , 1987 .

[12]  Jose B. Cruz,et al.  A note on an extended fuzzy bi-level optimization approach for water exchange in eco-industrial parks with hub topology , 2011 .

[13]  J. F. Benders Partitioning procedures for solving mixed-variables programming problems , 1962 .

[14]  Laureano F. Escudero,et al.  CORO, a modeling and an algorithmic framework for oil supply, transformation and distribution optimization under uncertainty , 1999, Eur. J. Oper. Res..

[15]  Gang Rong,et al.  A Strategy for the Integration of Production Planning and Scheduling in Refineries under Uncertainty , 2009 .

[16]  Omar Ben-Ayed,et al.  Bilevel linear programming , 1993, Comput. Oper. Res..

[17]  Fengqi You,et al.  Risk Management for a Global Supply Chain Planning Under Uncertainty : Models and Algorithms , 2009 .

[18]  Roy Kouwenberg,et al.  Scenario generation and stochastic programming models for asset liability management , 2001, Eur. J. Oper. Res..

[19]  Ignacio E. Grossmann,et al.  A Lagrangean decomposition approach for oil supply chain investment planning under uncertainty with risk considerations , 2013, Comput. Chem. Eng..

[20]  George B. Dantzig,et al.  Linear Programming Under Uncertainty , 2004, Manag. Sci..

[21]  Jose M. Pinto,et al.  Planning and scheduling for petroleum refineries using mathematical programming , 2002 .

[22]  Silvio Hamacher,et al.  Optimization under uncertainty of the integrated oil supply chain using stochastic and robust programming , 2010, Int. Trans. Oper. Res..

[23]  Jose M. Pinto,et al.  PLANNING AND SCHEDULING MODELS FOR REFINERY OPERATIONS , 2000 .

[24]  Fengqi You,et al.  Stochastic inventory management for tactical process planning under uncertainties: MINLP models and algorithms , 2011 .

[25]  Fengqi You,et al.  Planning and scheduling of flexible process networks under uncertainty with stochastic inventory: MINLP models and algorithm , 2013 .

[26]  Jose B. Cruz,et al.  Bi-level fuzzy optimization approach for water exchange in eco-industrial parks , 2010 .

[27]  Maria C. Carneiro,et al.  Risk Management in the Oil Supply Chain: A CVaR Approach , 2010 .

[28]  Kumaraswamy Ponnambalam,et al.  Two-stage stochastic programming with fixed recourse via scenario planning with economic and operational risk management for petroleum refinery planning under uncertainty , 2008 .

[29]  José Fortuny-Amat,et al.  A Representation and Economic Interpretation of a Two-Level Programming Problem , 1981 .

[30]  Jose M. Pinto,et al.  Multiperiod Optimization for Production Planning of Petroleum Refineries , 2005 .

[31]  Michael A. H. Dempster,et al.  Planning logistics operations in the oil industry , 2000, J. Oper. Res. Soc..

[32]  Christos T. Maravelias,et al.  Integration of production planning and scheduling: Overview, challenges and opportunities , 2009, Comput. Chem. Eng..

[33]  Christodoulos A. Floudas,et al.  A new robust optimization approach for scheduling under uncertainty: II. Uncertainty with known probability distribution , 2007, Comput. Chem. Eng..