Short-term crude oil scheduling with preventive maintenance operations: a fuzzy stochastic programming approach

This paper presents a fuzzy stochastic mathematical model for a short-term crude oil scheduling problem with preventive maintenance for charging tanks. The objective function of the proposed model minimizes the supply chain total cost. Lost sale is also reflected in the objective function acting as a criterion for the supply chain responsiveness. This study is among the first attempts to consider preventive maintenance operations, unavailability of the charging tanks, and minimizing lost sale for crude oil scheduling problem. The model encounters two types of uncertainty, namely fuzzy and stochastic uncertainty caused by demand, cost, and time parameters. Fuzzy programming and chance constraint method are employed to formulate the nondeterministic model. The model's applicability is illustrated with a case study. Results indicate that in cases where demand is sizable compared to storage volumes and maximum flow rate is sufficiently large, a preventive maintenance system can be scheduled regularly. Furthermore, the performed sensitivity analyses reveal that the problem is more sensitive to demand uncertainty than to cost and time uncertainty.

[1]  Oliver Braun,et al.  Optimality of Jackson's permutations with respect to limited machine availability , 2006, Int. Trans. Oper. Res..

[2]  Talal M. Alkhamis,et al.  Refinery units maintenance scheduling using integer programming , 1995 .

[3]  G. Rong,et al.  Robust Optimization Model for Crude Oil Scheduling under Uncertainty , 2010 .

[4]  David F. Percy,et al.  Scheduling preventive maintenance for oil pumps using generalized proportional intensities models , 2007, Int. Trans. Oper. Res..

[5]  Cuiwen Cao,et al.  Stochastic chance constrained mixed-integer nonlinear programming models and the solution approaches for refinery short-term crude oil scheduling problem , 2010 .

[6]  Dehua Xu,et al.  Makespan minimization for two parallel machines scheduling with a periodic availability constraint: Mathematical programming model, average-case analysis, and anomalies , 2013 .

[7]  Gang Rong,et al.  Researches on Scheduling Technology in Oil-refining Industry: A Review , 2013 .

[8]  Yu-Lan Jin,et al.  Integrating flexible-interval preventive maintenance planning with production scheduling , 2009, Int. J. Comput. Integr. Manuf..

[9]  Eduardo Camponogara,et al.  Scheduling dynamically positioned tankers for offshore oil offloading , 2014 .

[10]  Peter M. Verderame,et al.  Planning and Scheduling under Uncertainty: A Review Across Multiple Sectors , 2010 .

[11]  Lawrence V. Snyder,et al.  Facility location under uncertainty: a review , 2006 .

[12]  Jan A. Persson,et al.  Shipment planning at oil refineries using column generation and valid inequalities , 2005, Eur. J. Oper. Res..

[13]  Ahmet Palazoglu,et al.  Refinery scheduling of crude oil unloading, storage and processing using a model predictive control strategy , 2010, Comput. Chem. Eng..

[14]  Sigrid Lise Nonås,et al.  Refinery Planning and Scheduling: An Overview , 2010 .

[15]  Stanisław Heilpern,et al.  The expected value of a fuzzy number , 1992 .

[16]  J. M. Pinto,et al.  Mixed-Integer Linear Programming Model for Refinery Short-Term Scheduling of Crude Oil Unloading with Inventory Management , 1996 .

[17]  Baoding Liu,et al.  Chance constrained programming with fuzzy parameters , 1998, Fuzzy Sets Syst..

[18]  Reza Tavakkoli-Moghaddam,et al.  A general flow shop scheduling problem with consideration of position-based learning effect and multiple availability constraints , 2014 .

[19]  Yves Dallery,et al.  Scheduling of loading and unloading of crude oil in a refinery using event-based discrete time formulation , 2009, Comput. Chem. Eng..

[20]  Fernando Ordóñez,et al.  Robust solutions for network design under transportation cost and demand uncertainty , 2008, J. Oper. Res. Soc..

[21]  D. Dubois,et al.  Operations on fuzzy numbers , 1978 .

[22]  Bin Zhang,et al.  Effective MILP model for oil refinery-wide production planning and better energy utilization , 2007 .

[23]  Ignacio E. Grossmann,et al.  A simultaneous optimization approach for off-line blending and scheduling of oil-refinery operations , 2006, Comput. Chem. Eng..

[24]  Xiuxi Li,et al.  New approach for scheduling crude oil operations , 2009 .

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

[26]  R. Barlow,et al.  Optimum Preventive Maintenance Policies , 1960 .

[27]  Sylvain Mouret,et al.  A new Lagrangian decomposition approach applied to the integration of refinery planning and crude-oil scheduling , 2011, Comput. Chem. Eng..

[28]  Chung-Yee Lee,et al.  Machine scheduling with an availability constraint , 1996, J. Glob. Optim..

[29]  Ignacio E. Grossmann,et al.  A comparative study of continuous-time models for scheduling of crude oil operations in inland refineries , 2012, Comput. Chem. Eng..

[30]  Marianthi G. Ierapetritou,et al.  Petroleum Refining Operations: Key Issues, Advances, and Opportunities , 2011 .

[31]  Carlos E. Testuri,et al.  Stochastic analysis of crude oil procurement and processing under uncertain demand for bunker fuel oil , 2006, Int. Trans. Oper. Res..

[32]  Jose M. Pinto,et al.  Decomposition techniques for the long-range production planning of oil complexes , 2004 .

[33]  Nikolaos V. Sahinidis,et al.  Optimization under uncertainty: state-of-the-art and opportunities , 2004, Comput. Chem. Eng..

[34]  Qiang Xu,et al.  A Hybrid Programming Model for Optimal Production Planning under Demand Uncertainty in Refinery , 2008 .

[35]  Rui Zhang,et al.  A simulation-based differential evolution algorithm for stochastic parallel machine scheduling with operational considerations , 2013, Int. Trans. Oper. Res..

[36]  Abdelhakim Artiba,et al.  Hybrid Flow Shop Scheduling with Availability Constraints , 2014, Essays in Production, Project Planning and Scheduling.

[37]  Xingsheng Gu,et al.  Chance constrained programming models for refinery short-term crude oil scheduling problem , 2009 .

[38]  Amelia Bilbao-Terol,et al.  Linear programming with fuzzy parameters: An interactive method resolution , 2007, Eur. J. Oper. Res..

[39]  Marianthi G. Ierapetritou,et al.  Process scheduling under uncertainty: Review and challenges , 2008, Comput. Chem. Eng..