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A general modeling framework for the operational planning of petroleum supply chainsIntroductionPetroleum supply chainProblem statementGeneral problemCase studyMathematical modelsProcessing unit modelTank modelPipeline modelPetroleum supply chain modelIllustrative examplePetroleum tank modelCD1 modelVD1 modelPDA modelFCC modelHT3 modelProduct tank modelPetroleum supply chain-case studyCase study revisitedResults and discussionComputational resultsConclusions and future researchAcknowledgementsAppendix AReferencesComputers and Chemical Engineering 28 (2004) 871–896A general modeling framework for the operationalplanning of petroleum supply chainsSérgio M.S. Neiroa,1, José M. Pintoa,b,∗,2aDepartment of Chemical Engineering, University of São Paulo, 05508-900 São Paulo, SP, BrazilbDepartment of Chemical Engineering and Chemistry, Polytechnic University, Brooklyn, NY 11201, USAAbstractIn the literature, optimization models deal with planning and scheduling of several subsystems of the petroleum supply chain such asoilfield infrastructure, crude oil supply, refinery operations and product transportation. The focus of the present work is to propose a generalframework for modeling petroleum supply chains. As a starting point, processing units are modeled based on the framework developed byPinto et al. [Computers and Chemical Engineering 24 (2000) 2259]. Particular frameworks are then proposed to storage tanks and pipelines.Nodes of the chain are considered as grouped elementary entities that are interconnected by intermediate streams. The complex topologyis then built by connecting the nodes representing refineries, terminals and pipeline networks. Decision variables include stream flow rates,properties, operational variables, inventory and facilities assignment. The resulting multiperiod model is a large-scale MINLP. The proposedmodel is applied to a real-world corporation and results show model performance by analyzing different scenarios.© 2003 Elsevier Ltd. All rights reserved.Keywords: Petroleum complex; Supply chain management; Mixed-integer optimization1. IntroductionCompanies have been forced to overstep their physicalfrontiers and to visualize the surrounding business environ-ment before planning their activities. Range vision shouldcover all members that participate direct or indirectly in thework to satisfy a customer necessity. Coordination of thisvirtual corporation may result in benefits for all members ofthe chain individually. Beamon (1998) defines such virtualcorporation as an integrated process wherein a number ofbusiness entities (suppliers, manufacturers, distributors andretailers) work together in an effort to acquire raw materi-als, convert them into specified final products and deliverthese final products to retailers. Under another point of view,Tan (2001) states that there is a definition of supply chainmanagement (SCM), which emerges from transportation andlogistics literature of the wholesaling and retailing indus-try that emphasizes the importance of physical distributionand integrated logistics. According to Lamming (1996), this∗Corresponding author. Tel.: +1-718-260-3569;fax: +1-718-260-3125.E-mail address: [email protected] (J.M. Pinto).1Tel.: +55-11-3091-2237; fax: +55-11-3813-2380.2Tel.: +1-718-260-3569; fax: +1-718-260-3125.is probably where the term supply chain management wasoriginally used.According to Thomas and Griffin (1996), current researchin the area of SCM can be classified in three categories:Buyer–Vendor, production–distribution and inventory–dis-tribution coordination. The authors present an extensive lit-erature review for each category. Vidal and Goetschalckx(1997) present a review of mixed integer problems (MIP)that focuses on the identification of the relevant factors in-cluded in formulations of the chain or its subsystems andalso highlights solution methodologies.Bok, Grossmann, and Park (2000) present an appli-cation to the optimization of continuous flexible processnetworks. Modeling considers intermittent deliveries, pro-duction shortfalls, delivery delays, inventory profiles andjob changeovers. A bi-level solution methodology is pro-posed to reduce computational expense. Zhuo, Cheng, andHua (2000) introduce a supply chain model that involvesconflicting decisions in the objective function. Goal pro-gramming is used to solve the multi-objective optimizationproblem. Perea, Grossmann, Ydstie, and Tahmassebi (2000)and Perea-López, Grossmann, and Ydstie (2001) present anapproach that is capable of capturing the dynamic behaviorof the supply chain by modeling flow of materials and infor-mation within the supply chain. Information is considered0098-1354/$ – see front matter © 2003 Elsevier Ltd. All rights reserved.doi:10.1016/j.compchemeng.2003.09.018872 S.M.S. Neiro, J.M. Pinto/Computers and Chemical Engineering 28 (2004) 871–896NomenclatureIndices:p propertys streamt time periodu, u unitv operating variableSets:PIuproperties of the inlet stream of unit uPOu,sproperties of outlet stream s of unit uSOuoutlet streams of unit uT time periods {t|t = 1,...,NT}Ucoproduct tanks at refinery sites dedicated tosupply local marketUdemproduct tanks that present direct demandfrom a consumerUfpetroleum tanksUIuunits whose outlet streams feed unit uUncproduct tanks at refinery sites thatsupply local and other marketsUOu,sunits that are fed by stream s of unit uUpproduct tanksUpipeunits that represent pipelinesUportpetroleum tanks that store the crudeoil from suppliersUpuprocessing units at refinery sitesUrproduct tanks at refineriesUSuordered pairs unit/stream (u , s) thatfeeds unit uUtankall storage tanks of the supply chainVOuoperating variables of unit uParameters:Cinvu,tinventory cost of tank u at time period tCpetu,tprice of petroleum u (u ∈ Uport)attime period tCpu,tsale price of product u (u ∈ Up)at time period tCrufixed operating cost coefficient of unit uCtutransportation cost for pipeline uCvu,vvariable coefficient cost ofthe operating variable v of unit uDemu,tdemand of product u at time periodt (u ∈ Up)PFLu,p,tlower bound of inlet property p of unit uat time period tPFUu,p,tupper bound of inlet property p of unit uat time period tPropu,s,pstandard property value p of theoutlet stream s from unit uQFLulower bound for feed flow rate of unit uQFUuupper bound for feed flow rateof unit uQGainu,s,vflow rate gain of outlet stream s of unitu due to operating variable uQSLulower bound for outlet flow rate of unit


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