Optimization and Engineering

, Volume 18, Issue 1, pp 241–268 | Cite as

Short-term operational planning of refined products pipelines

  • Diego C. Cafaro
  • Jaime Cerdá


Pipelines are the safest and least expensive mode for transporting energy products over long distances. Refined products pipelines convey multiple oil derivatives from refineries to marketing terminals, usually through the same duct. Planning the injection, transportation and delivery of batches moving into pipelines is a very complex industrial problem with many operational constraints. This work synthesizes two innovative optimization tools for the short-term planning of oil product pipelines. The first one is a continuous-time mixed-integer linear programming (MILP) formulation for the short-term planning of pipelines connecting a single source node to multiple terminals over a multiperiod horizon. In the second approach, the MILP formulation is extended to deal with the transportation planning of multi-source pipelines. Common-carrier pipelines often present input facilities at non-origin points, whose operation raises new difficulties. Solutions to real-world case studies illustrate the performance of the proposed optimization tools.


Oil products pipelines Operational planning Optimization MILP models 



Financial support received from FONCYT-ANPCyT under Grant PICT 1763, from CONICET under Grant PIP 2221 and from Universidad Nacional del Litoral under CAI + D program is fully appreciated.


  1. Boschetto SN, Magatão L, Brondani WM, Neves-Jr F, Arruda LVR, Barbosa-Póvoa APFD, Relvas S (2010) An operational scheduling model to product distribution through a pipeline network. Ind Eng Chem Res 49:5661–5682CrossRefGoogle Scholar
  2. Brooke A, Kendrick D, Meeraus A, Raman R (2006) GAMS: a user’s guide. GAMS Development Corporation, Washington, DCGoogle Scholar
  3. Cafaro DC, Cerdá J (2004) Optimal scheduling of multiproduct pipeline systems using a non-discrete MILP formulation. Comput Chem Eng 28:2053–2068CrossRefGoogle Scholar
  4. Cafaro DC, Cerdá J (2008a) Efficient tool for the scheduling of multiproduct pipelines and terminal operations. Ind Eng Chem Res 47:9941–9956CrossRefGoogle Scholar
  5. Cafaro DC, Cerdá J (2008b) Dynamic scheduling of multiproduct pipelines with multiple delivery due dates. Comput Chem Eng 32:728–753CrossRefGoogle Scholar
  6. Cafaro DC, Cerdá J (2010) Operational scheduling of refined products pipeline networks with simultaneous batch injections. Comput Chem Eng 34:1687–1704CrossRefGoogle Scholar
  7. Cafaro VG, Cafaro DC, Méndez CA, Cerdá J (2010) Oil-derivatives pipeline logistics using discrete-event simulation. In: Proceedings of the winter simulation conference, pp 2101–2113Google Scholar
  8. Cafaro VG, Cafaro DC, Méndez CA, Cerdá J (2011) Detailed scheduling of operations in single-source refined products pipelines. Ind Eng Chem Res 50:6240–6259CrossRefGoogle Scholar
  9. García-Sánchez A, Arreche LM, Ortega-Mier M (2008) Combining simulation and tabu search for oil-derivatives pipeline scheduling. Stud Comput Intell 128:301–325MATHGoogle Scholar
  10. Hane CA, Ratliff HD (1995) Sequencing inputs to multi-commodity pipelines. Ann Oper Res 57:73–101CrossRefMATHGoogle Scholar
  11. ILOG OPL Studio 3.7 (2004) A user’s manual. ILOG S.AGoogle Scholar
  12. Lopes TMT, Ciré AA, de Souza CC, Moura AV (2010) A hybrid model for a multiproduct pipeline planning and scheduling problem. Constraints 15:151–189CrossRefGoogle Scholar
  13. Magatão L, Arruda LVR, Neves-Jr FA (2004) Mixed integer programming approach for scheduling commodities in a pipeline. Comput Chem Eng 28:171–185CrossRefGoogle Scholar
  14. Miesner TO, Leffler WL (2006) Oil & gas pipelines in nontechnical language. Pennwell, TulsaGoogle Scholar
  15. Mori FM, Lüders R, Arruda LVR, Yamamoto L, Bonacin MV, Polli HL, Aires MC, Bernardo LFJ (2007) Simulating the operational scheduling of a realworld pipeline network. Comput Aided Chem Eng 24:691–696CrossRefGoogle Scholar
  16. Moura AV, de Souza CC, Cire AA, Lopes TM (2008) Planning and scheduling the operation of a very large oil pipeline network. Lect Notes Comput Sci 5202:36–51CrossRefGoogle Scholar
  17. Rabinow RA (2004) The liquid pipeline industry in the United States, where it’s been, where it’s going. Association of Oil Pipelines, WashingtonGoogle Scholar
  18. Rejowski R, Pinto JM (2003) Scheduling of a multiproduct pipeline system. Comput Chem Eng 27:1229–1246CrossRefGoogle Scholar
  19. Rejowski R, Pinto JM (2004) Efficient MILP formulations and valid cuts for multiproduct pipeline scheduling. Comput Chem Eng 28:1511–1528CrossRefGoogle Scholar
  20. Rejowski R, Pinto JM (2008) A novel continuous time representation for the scheduling of pipeline systems with pumping yield rate constraints. Comput Chem Eng 32:1042–1066CrossRefGoogle Scholar
  21. Relvas S, Matos HA, Barbosa-Póvoa APFD, Fialho J, Pinheiro AS (2006) Pipeline scheduling and inventory management of a multiproduct distribution oil system. Ind Eng Chem Res 45:7841–7855CrossRefGoogle Scholar
  22. Sasikumar M, Prakash PR, Patil SM, Ramani S (1997) PIPES: a heuristic search model for pipeline schedule generation. Knowl Based Syst 10:169–175CrossRefGoogle Scholar
  23. Zyngier D, Kelly JD (2009) Multi-product inventory logistics modeling in the process industries. Optim Logist Chall Enterp 30:61–95CrossRefMATHGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.INTEC (UNL - CONICET) - Facultad de Ingeniería Química (UNL)Santa FeArgentina

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