SOLUTION TO A PIPELINE SCHEDULING PROBLEM BY USING A MIXED INTEGER LINEAR PROGRAMMING MODEL

Öz

Pipelines are efficient ways of conveying huge amounts of refined petroleum products to

distant points. Different products are pumped successively, in the pipelines without a need of a

separator between them. Pipelines should be chosen very carefully based on the pumping sequences,

volumes to be conveyed, covering the constraints involved by cutting operational costs and focusing on

market demands. The real life problem considered in this study consists of a unidirectional pipe

distribution system used for pumping petroleum products between the sources and distribution centers.

Problem was stated as a Mixed Integer Linear Programming (MILP) model and solved by using GAMS

software thorough actual data. As a result of the study, an optimal pumping schedule for pipeline

operations at a certain period of time is achieved.

Anahtar Kelimeler

• Pipeline scheduling
• GAMS
• Mixed integer linear programming

Karma Tamsayılı Doğrusal Programlama Modeli İle Bir Boru Hattı Çizelgeleme Probleminin Çözümü

Öz

Boru hatları büyük miktarlardaki rafine edilmiş petrol ürünlerinin uzak mesafelere taşınmasında ekonomik bir yoldur. Boru hatlarında farklı ürünler aralarında herhangi bir ayırıcı olmadan arka arkaya pompalanmaktadır. Pompaj hareketinin sırası ve uzunluğu, boru hattı operasyonel maliyetlerini azaltarak kısıtları karşılamak ve pazar taleplerine cevap vermek için dikkatli bir şekilde seçilmek zorundadır. Bu çalışmada ele alınan gerçek hayat problemi, kaynak ve dağıtım merkezleri arasında yakıt türevlerinin pompalanmasında kullanılan ve tek yönlü boru hattından oluşan bir dağıtım sistemini içermektedir. Problem mevcut veriler doğrultusunda Karma Tamsayılı Doğrusal Programlama modeli olarak ifade edilmiş ve GAMS yazılımı ile çözülmüştür. Çalışma neticesinde belirli bir zaman dilimindeki boru hattı operasyonlarını içeren optimal bir pompaj çizelgesi elde edilmiştir.

Anahtar Kelimeler

• Boru hattı çizelgeleme
• GAMS
• Karma tamsayılı doğrusal programlama

Kaynakça

1. Álvaro, G.S., Arreche, L.M., “A Support Decision Tool for Pipeline Scheduling”, Third International Conference on Production Research Americas’ Region 2006 (ICPR-AM06), Conference Proceeding, Curitiba, Brazil, 2006.
2. Cafaro, D. C., Cerda, J., 2004, “Optimal Scheduling of Multiproduct Pipeline Systems Using a Non- Discrete MILP Formulation”, Computers and Chemical Engineering, Vol. 28, pp. 2053-2068.
3. Cafaro, D.C., Cerd{, J., 2008, “Dynamic Scheduling of Multiproduct Pipelines with Multiple Delivery Due Dates”, Computers and Chemical Engineering, Vol. 32, pp.728-753.
4. Cafaro, D.C., Cerd{, J., 2009, “Optimal Scheduling of Refined Products Pipelines with Multiple Sources”, Industrial & Engineering Chemistry Research, Vol. 48, pp. 6675-6689.
5. Cafaro, G.V., Cafaro, D.C., Cerd{, J., Mendes, A.C., “Oil-Derivatives Pipeline Logistics Using Discrete- Event Simulation”, Proceedings of the 2010 Winter Simulation Conference, Arizona, USA, 2010, pp. 2101-2114.
6. Cafaro, D.C., Cerd{, J., 2010, “Operational Scheduling of Refined Products Pipeline Networks with Simultaneous Batch Injections”, Computers and Chemical Engineering, Vol. 34, pp. 1687–1704.
7. Cafaro, G.V., Cafaro, D.C., Mendes, A.C., Cerd{, J., 2011, “Detailed Scheduling of Operations in Single- Source Refined Products Pipelines”, Industrial & Engineering Chemistry Research, Vol. 50 (10), pp. 6240–6259.
8. Herr{n, A., de la Cruz, J. M., de Andrés, B., 2010, “A Mathematical Model for Planning Transportation of Multiple Petroleum Products in A Multi Pipeline System”, Computers and Chemical Engineering, Vol. 34, pp. 401–413.

9. Herr{n, A., de la Cruz, J. M., de Andrés, B., 2012, “Global Search Metaheuristics for Planning Transportation of Multiple Petroleum Products in A Multi-Pipeline System”, Computers and Chemical Engineering Vol. 37 (2012), pp.248– 261
10. Magatão, L., Arruda, L. V. R., Nevesxe, F.A., 2005, “Using CLP and MILP for Scheduling Commodities in A Pipeline”, Computer Aided Chemical Engineering, Vol. 20, pp. 1027-1032.
11. Maruyama Mori, F., Luders, R., Arruda, L. V. R., Yamamoto, L., Bonacin, M. V.; Polli, H. L., Aires, M. C.,
12. Bernardo, L. F. J., 2007, “Simulating The Operational Scheduling of a Real World Pipeline Network”, Computer Aided Chemical Engineering, Vol. 24, pp.691–696.
13. MirHassani, S.A., Ghorbanalizadeh, M., 2008, “The Multi-Product Pipeline Scheduling System”, Computers and Mathematics with Applications, Vol. 56(4), pp. 891–897.
14. MirHassani, S.A., Jahromi H.F., 2011, “Scheduling Multi-Product Tree-Structure Pipelines”, Computers and Chemical Engineering, Vol.35, pp. 165–176.
15. Moura, A.V., de Souza, Cid C., Cire, Andre A., Lopes, Tony M.T., 2008, “Planning and Scheduling the Operation of a Very Large Oil Pipeline Network, Principles and Constrained Programming”, Lecture Notes in Computer Science, P.J. Stuckey (Ed.), pp. 36-51.
16. Neiro, S., Pinto, J. M., 2004, “A General Modeling Framework for The Operational Planning of Petroleum Supply Chains”, Computers and Chemical Engineering, Vol. 28, pp. 871-896.
17. Neves B.S., Relvas, S., Barbosa-Póvoa, A.P.F.D, Neves Jr. F., 2010, “Decomposition Approach for the Operational Scheduling of a Multi-Product Pipeline”, Computer Aided Chemical Engineering Vol. 28, pp.1207–1212.
18. Rejowski R., Pinto, J. M., 2002, “An MILP Formulation for the Scheduling of Multiproduct Pipeline Systems”, Brazilian Journal of Chemical Engineering, Vol. 19, No. 4, pp. 467-474.
19. Rejowski, Jr., R., Pinto, J. M., 2003, “Scheduling of A Multiproduct Pipeline System”, Computers and Chemical Engineering, Vol.27, pp. 1229-1246.
20. Rejowski, Jr., R., Pinto, J. M., 2004, “Efficient MILP Formulations and Valid Cuts for Multiproduct Pipeline Scheduling”, Computers and Chemical Engineering, Vol. 28, pp. 1511-1528.
21. Rejowski Jr., R., Pinto, J. M., 2005, “A Rigorous MINLP for The Simultaneous Scheduling and Operation of Multiproduct Pipeline Systems”, Computer Aided Chemical Engineering V.20, pp 1063– 1068.
22. Rejowski Jr., R., Pinto, J. M., 2008, “A Novel Continuous Time Representation for The Scheduling of Pipeline Systems with Pumping Yield Rate Constraints”, Computers and Chemical Engineering, Vol.19, No. 4, pp. 467-474.
23. Relvas, S., Matos, H. A., Barbosa-Po´voa, A. P. F. D., Fialho, J., Pinheiro, A. S., 2006, “Pipeline Scheduling and Inventory Management of a Multiproduct Distribution Oil System”, Ind.Eng.Chem.Res., Vol.45, pp. 7841-7855.
24. Relvas, S., Barbosa-Po´voa, A. P. F. D., Matos, H. A., Fialho, J., 2007, “Rescheduling of Medium Term Pipeline Operation with Tank Farm Inventory Management Computer Aided Chemical Engineering Vol.24, pp. 667-773.
25. Relvas, S., Matos, H.A., Barbosa-Póvoa, A.P.F.D., Fialho, J., 2008, “Oil Products Pipeline Scheduling with Tank Farm Inventory Management”, Computer Aided Chemical Engineering, V.25, pp.277– 282.
26. Relvas, S., Barbosa-Póvoa, A.P.F.D., Matos, H.A., 2009, “Heuristic Batch Sequencing on A Multiproduct Oil Distribution System”, Computersand Chemical Engineering, Vol. 33, pp. 712-730.
27. Relvas, S., Barbosa-Póvoa, A.P.F.D., Matos, H.A., 2010, “Inventory Management MILP Modeling for Tank Farm Systems”, Computer Aided Chemical Engineering, Vol.28, pp.727–732.
28. Sasikumar, M., Prakash, P. R., Patil, S. M., and Ramani, S., 1997, “PIPES: A Heuristic Search Model for Pipeline Schedule Generation”, Knowledge-Based Systems, Vol.10, pp. 169-175.
29. Yılmaz, N.F., 2005, “A Survey on Petroleum and Natural Gas Pipelines” Journal of Installation Engineering, Vol. 87, pp.4-14.