Abstract
A rational decision regarding warehouse location can save logistics costs and improve oilfield operating efficiency. In existing research on oilfield warehouse location problems, it is usually assumed that the oil well locations are known. However, in real oilfield, operations, as well locations, are affected by underground reservoir conditions and the long-term plans of the oilfield company; future well locations that might be serviced by a warehouse are highly uncertain. In addition, previous warehouse location research has tended to solve location problems using a discrete or continuous location model without considering delivery problems. With these deficits in mind, this paper applies a Monte Carlo simulation to simulate future well locations, then selects several suitable candidates using a continuous location model and finally uses discrete location optimization to determine the optimal solution while also considering the distribution interruption problem. Finally, an oil warehouse location problem in the south of the Ordos Basin in China is given as an example of the process. Using relevant data such as number of wells, well locations and materials quantities required, Zhengning is identified as the optimal location for the storage warehouse construction. The simulation indicated that RMB 55,000 would be saved every year, proving the strength of the model to save logistics costs. In an environment in which well locations are uncertain, the combination of a continuous location model and a discrete location model can significantly enhance warehouse location logistics decisions in the oil and gas industries.
Similar content being viewed by others
References
Aikens CH (1985) Facility location models for distribution planning. Eur J Oper Res 22(3):263–279
Ardjmand E, Young WA, Weckman GR et al (2016) Applying genetic algorithm to a new bi-objective stochastic model for transportation, location, and allocation of hazardous materials. Expert Syst Appl 51:49–58
Brahimi N, Khan SA (2014) Warehouse location with production, inventory, and distribution decisions: a case study in the lube oil industry. 4OR 12(2):175–197
Brest J, Maučec MS (2008) Population size reduction for the differential evolution algorithm. Appl Intell 29(3):228–247
Camponogara E, de Castro MP, Plucenio A et al (2011) Compressor scheduling in oil fields. Optim Eng 12(1–2):153–174
Chen DQ, Cai C, Li SK (1987) The research of crude oil gathering system planning. Optim Manag Sci 3:1–8
Chou CC (2010) An integrated quantitative and qualitative FMCDM model for location choices. Soft Comput 14(7):757–771
Dai WQ, Li SM (2014) Online median location problem analysis in Euclidean plane. J Manag Sci China 17(9):88–94
Gabli M, Jaara EM, Mermri EB (2016) A possibilistic approach to UMTS base-station location problem. Soft Comput 20(7):2565–2575
Guha S, Khuller S (1999) Greedy strikes back: improved facility location algorithms. J Algorithms 31(1):228–248
Hermeto NSS, Ferreira Filho VJM, Bahiense L (2014) Logistics network planning for offshore air transport of oil rig crews. Comput Indust Eng 75:41–54
Jia H, Ordóñez F, Dessouky M (2007) A modeling framework for facility location of medical services for large-scale emergencies. IIE Trans 39(1):41–55
Liang QS, Wang XF, Liang WW (2014) Geochemical characteristics and oil-source correlation of the mesozoic crude oils in the Southern Ordos Basin. Geol J China Univ 20(2):309–316
Lu Z, Bostel N (2007) A facility location model for logistics systems including reverse flows: the case of remanufacturing activities. Comput Oper Res 34(2):299–323
Luo YX, Zhang ZJ (2014) Layout optimization model of surface gathering system in oilfield. Gas Storage Transp 33(9):1004–1009
Min H, Jayaraman V, Srivastava R (1998) Combined location-routing problems: a synthesis and future research directions. Eur J Oper Res 108(1):1–15
Mokhtarian MN (2011) A new fuzzy weighted average (FWA) method based on left and right scores: an application for determining a suitable location for a gas oil station. Comput Math Appl 61(10):3136–3145
Owen SH, Daskin MS (1998) Strategic facility location: a review. Eur J Oper Res 111(3):423–447
Paul JA, MacDonald L (2016) Location and capacity allocations decisions to mitigate the impacts of unexpected disasters. Eur J Oper Res 251(1):252–263
Sahebi H, Nickel S (2014) Offshore oil network design with transportation alternatives. Eur J Indust Eng 8(6):739–761
Soleimani H, Kannan G (2015) A hybrid particle swarm optimization and genetic algorithm for closed-loop supply chain network design in large-scale networks. Appl Math Model 39(14):3990–4012
Tao L, Tao YQ (2014) Uncertain prediction of energy supply based on Monte Carlo algorithm. J Chongqing Univ Technol (Soc Sci) 28(6):35–39
Verma M, Gendreau M, Laporte G (2013) Optimal location and capability of oil-spill response facilities for the south coast of Newfoundland. Omega 41(5):856–867
Wang F, Xu Y, Li YX (2006) Review on facility location models. Oper Res Manag Sci 15(5):64–69
Wang Z, Hu XP, Wang XP (2013) Disruption management model and algorithm for distribution vehicle scheduling problems under accidental travel time delay. Syst Eng Theory Practice 33((2)):378–387
Weber A (1929) Theory of the location of industries. University of Chicago Press, Chicago
Wei L, Jiang H, Liu Y (2009) Hybrid genetic-simulated annealing algorithm of location–allocation optimization of looped gathering and transportation pipe network. In: Fifth international conference on natural computation, 2009 (ICNC’09), vol 4. IEEE, pp 275–280
Wu QM, Xu Q (2009) Bi-levle optimization model for oil depot location decision. Oper Res Manag Sci 18(3):37–40
Yu G, Qi X (2004) Disruption management: framework, models and applications. World Scientific Publishing Company Incorporated, Singapore
Zou TF, Cai M, Liu JK (2013) Method for analyzing uncertainty of simulation results in accident reconstruction. J Syst Simul 5:009
Acknowledgments
This research has been supported by National Natural Science Foundation of China under Grant Nos. 71103163, 71573237; New Century Excellent Talents in University of China under Grant No. NCET-13-1012; Research Foundation of Humanities and Social Sciences of Ministry of Education of China No. 15YJA630019; Special Funding for Basic Scientific Research of Chinese Central University under Grant Nos. CUG120111, CUG110411, G2012002A, CUG140604, CUG160605; Open Foundation for the Research Center of Resource Environment Economics in China University of Geosciences (Wuhan) under Grant No. H2015004B.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Communicated by V. Loia.
Rights and permissions
About this article
Cite this article
Guo, H., Pan, W., Liu, X. et al. Combining a continuous location model and Heuristic techniques to determine oilfield warehouse locations under future oil well location uncertainty. Soft Comput 22, 823–837 (2018). https://doi.org/10.1007/s00500-016-2386-5
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-016-2386-5