Restricted accessResearch articleFirst published online 2025
Location-allocation Model for the Supply Chain of Multi-Product and Multi-Period Oil and Gas Industries with Consideration of Sustainability Under Uncertainty
Oil and gas products are routinely transported from producers to distributors, and then from distributors to retailers. In the process, operating costs, transportation expenses, and environmental impacts often increase due to time constraints, high transportation frequency, limited inventory storage, and other factors. To reduce costs, meet end-customer demands, and create competitive advantages, sustainable development (SD) has become a key consideration for supply chain (SC) managers, prompting a shift towards more sustainable supply networks. This study aims to design a sustainable multi-period, multi-product oil and gas SC consisting of producers, distributors, and retailers under uncertain conditions. To address uncertainty in demand, production, transportation, holding costs, and facility capacity, and to maximize net present value (NPV) while considering social impacts, the study develops a Robust Counterpart Optimization model based on the robust optimization method. The model is implemented in the programming environment of SAS Studio 3.5. Numerical results provide valuable insights into strategic and planning decisions for SC design. Finally, sensitivity analyses are conducted on key parameters, with the changes in objective functions being thoroughly examined.
AmirianS.AmiriM.TaghavifardM. T. (2022). The emergence of a sustainable and reliable supply chain paradigm in supply chain network design. Complexity, 2022, 1–29. https://doi.org/10.1155/2022/9415465
2.
ArmanH. (2023). Fuzzy analytic hierarchy process for pentagonal fuzzy numbers and its application in sustainable supplier selection. J Clean Prod, 409(Suppl. C), 137190. https://doi.org/10.1016/j.jclepro.2023.137190
3.
ArmanH.Hadi-VenchehA.GolmohammadiA.-M.DehghaniS.Nadimi-ShahrakiM. H. (2024). Optimal locating by integrating volumetric fuzzy sets and geographic coordinate system: An application to healthcare. Comput Oper Res, 161(Suppl. C), 106377. https://doi.org/10.1016/j.cor.2023.106377
4.
Armin JabbarzadehB. F.SabouhiF. (2018). Resilient and sustainable supply chain design: Sustainability analysis under disruption risks. Int J Prod Res, 56(17), 5945–5968. https://doi.org/10.1080/00207543.2018.1461950
5.
BavarsadB.BoshaghM.KayedianA. (2014). A study on supply chain risk factors and their impact on organizational performance. Int J Oper Logist Manag, 3(3), 192–211.
ChenL.HendalianpourA.FeylizadehM. R.XuH. (2023). Factors affecting the use of blockchain technology in humanitarian supply chain: A novel fuzzy large-scale group-DEMATEL. Gr Decis Negot, 32(2), 359–394. https://doi.org/10.1007/s10726-022-09811-z
8.
ChenA.LiuY. (2023). Designing globalized robust supply chain network for sustainable biomass-based power generation problem. J Clean Prod, 413, 137403. https://doi.org/10.1016/j.jclepro.2023.137403
DubeyR.GunasekaranA.PapadopoulosT.ChildeS. J.ShibinK. T.WambaS. F. (2017). Sustainable supply chain management: Framework and further research directions. J Clean Prod, 142, 1119–1130. https://doi.org/10.1016/j.jclepro.2016.03.117
11.
EmamiI.KamranradR.JokarE. (2021). Location and allocation in multi-level supply chain network of projects. J. Qual. Eng. Prod. Optim, 6(1), 2423–3781. https://doi.org/10.22070/JQEPO.2021.13527.1173
12.
ForoozeshN.KarimiB.MousaviS. M.MojtahediM. (2023). A hybrid decision-making method using robust programming and interval-valued fuzzy sets for sustainable-resilient supply chain network design considering circular economy and technology levels. J Ind Inf Integr, 33, 100440. https://doi.org/10.1016/j.jii.2023.100440
13.
GaoX.CaoC. (2020). A novel multi-objective scenario-based optimization model for sustainable reverse logistics supply chain network redesign considering facility reconstruction. J Clean Prod, 270, 122405. https://doi.org/10.1016/j.jclepro.2020.122405
14.
GaoD.XuZ.RuanY. Z.LuH. (2017). From a systematic literature review to integrated definition for sustainable supply chain innovation (SSCI). J Clean Prod, 142(Part 4), 1518–1538. https://doi.org/10.1016/j.jclepro.2016.11.153
15.
GenoveseA.AcquayeA. A.FigueroaA.KohS. C. L. (2017). Sustainable supply chain management and the transition towards a circular economy: Evidence and some applications. Omega, 66(Part PB), 344–357. https://doi.org/10.1016/j.omega.2015.05.015
16.
GovindanK.JafarianA.NourbakhshV. (2015). Bi-objective integrating sustainable order allocation and sustainable supply chain network strategic design with stochastic demand using a novel robust hybrid multi-objective metaheuristic. Comput Oper Res, 62, 112–130. https://doi.org/10.1016/j.cor.2014.12.014
17.
GovindanK.KannanD.JørgensenT. B.NielsenT. S. (2022). Supply chain 4.0 performance measurement: A systematic literature review, framework development, and empirical evidence. Transp Res Part E Logist Transp Rev, 164, 102725. https://doi.org/10.1016/j.tre.2022.102725
18.
GrosvoldJ.HoejmoseS. U.RoehrichJ. K. (2014). Squaring the circle: Management, measurement and performance of sustainability in supply chains. Supply Chain Manag, 19(3), 292–305. https://doi.org/10.1108/SCM-12-2013-0440
19.
GurzawskaA. (2020). Towards responsible and sustainable supply chains–innovation, multi-stakeholder approach and governance. Philos Manag, 19(3), 267–295. https://doi.org/10.1007/s40926-019-00114-z
20.
Hadi-VenchehA.KhodadadipourM.TanY.ArmanH.RoubaudD. (2024). Cross-efficiency analysis of energy sector using stochastic DEA: Considering pollutant emissions. J Environ Manage, 364, 121319. https://doi.org/10.1016/j.jenvman.2024.121319
21.
HanayshaJ. R.Al-ShaikhM. E.JogheeS.AlzoubiH. M. (2022). Impact of innovation capabilities on business sustainability in small and Medium enterprises. FIIB Bus Rev, 11(1), 67–78. https://doi.org/10.1177/23197145211042232
22.
HendalianpourA.FakhrabadiM.SangariM.RazmiJ. (2022). A combined benders decomposition and lagrangian relaxation algorithm for optimizing a multi-product, multi-level omni-channel distribution system. Sci Iran E, 29(1), 355––3371. https://doi.org/10.24200/sci.2020.53644.3349
23.
HomayouniZ.PishvaeeM. S.JahaniH.IvanovD. (2023). A robust-heuristic optimization approach to a green supply chain design with consideration of assorted vehicle types and carbon policies under uncertainty. Ann Oper Res, 324(1), 395–435. https://doi.org/10.1007/s10479-021-03985-6
24.
Hosseini ShekarabiS. A.Kiani MaviR.MacauF. R. (2025). An extended robust optimisation approach for sustainable and resilient supply chain network design: A case of perishable products. Eng Appl Artif Intell, 152, 110846. https://doi.org/10.1016/j.engappai.2025.110846
25.
HosseinkhaniO.KargariA. (2022). Production of high-quality drinking water from chillers and air conditioning units’ condensates using UV/GAC/MF/NF hybrid system. J Clean Prod, 368, 133177. https://doi.org/10.1016/j.jclepro.2022.133177
26.
HosseinkhaniO.KargariA.SanaeepurH. (2014). Facilitated transport of CO2 through co(II)-S-EPDM ionomer membrane. J Memb Sci, 469, 151–161. https://doi.org/10.1016/j.memsci.2014.06.021
27.
HussainR.AssavapokeeT.KhumawalaB. (2006). Supply chain management in the petroleum industry: Challenges and opportunities. Int J Glob Logist Supply Chain Manag, 1(2), 90–97.
28.
JabbarzadehA.FahimniaB.SheuJ.-B.MoghadamH. S. (2016). Designing a supply chain resilient to major disruptions and supply/demand interruptions. Transp Res Part B Methodol, 94, 121–149. https://doi.org/10.1016/j.trb.2016.09.004
29.
JacobsM. (1999). Sustainable development as a contested concept. In Fairness and futurity: Essays on environmental sustainability and social justice (pp. 21–45). Oxford University Press. https://doi.org/10.1093/0198294891.003.0002
30.
JuttnerU. (2005). Supply chain risk management: Understanding the business requirements from a practitioner perspective. Int J Logist Manag, 16(1), 120–141. https://doi.org/10.1108/09574090510617385
31.
KhorasaniS. T. (2018). A robust optimization model for supply chain in Agile and flexible mode based on variables of uncertainty. Glob J Flex Syst Manag, 19(3), 239–253. https://doi.org/10.1007/s40171-018-0191-y
32.
LababidiH. M. S.AhmedM. A.AlatiqiI. M.Al-EnziA. F. (2004). Optimizing the supply chain of a petrochemical company under uncertain operating and economic conditions. Ind & Eng Chem Res, 43(1), 63–73. https://doi.org/10.1021/ie030555d
33.
LambertD. M.CooperM. C.PaghJ. D. (1998). Supply chain management: Implementation issues and research opportunities. Int J Logist Manag, 9(2), 1–20. https://doi.org/10.1108/09574099810805807
34.
LimaC.RelvasS.Barbosa-PóvoaA.MoralesJ. M. (2019). Adjustable robust optimization for planning logistics operations in downstream oil networks. Processes, 7(8), 1–31. https://doi.org/10.3390/pr7080507
35.
LiuP.HendalianpourA.HamzehlouM.FeylizadehM. R.RazmiJ. (2021). Identify and rank the challenges of implementing sustainable supply chain blockchain technology using the Bayesian best worst method. Technol Econ Dev Econ, 27(3), 656–680. https://doi.org/10.3846/tede.2021.14421
36.
MangunD.ThurstonD. (2002). Incorporating component reuse, remanufacture, and recycle into product portfolio design. IEEE Trans Eng Manag, 49(4), 479–490. https://doi.org/10.1109/TEM.2002.807292
37.
MarzbanS.ShafieeM.MozaffariM. R. (2022). Four-Stage supply chain design for perishable products and evaluate it by considering the triple dimensions of sustainability. J Syst Manag, 8(4), 109–132. https://doi.org/10.30495/JSM.2022.1966734.1684
38.
MastrocinqueE.RamírezF. J.Honrubia-EscribanoA.PhamD. T. (2020). An AHP-based multi-criteria model for sustainable supply chain development in the renewable energy sector. Expert Syst Appl, 150, 113321. https://doi.org/10.1016/j.eswa.2020.113321
39.
MogaleN. C. D. G.TiwariM. K. (2020). Modelling of sustainable food grain supply chain distribution system: A bi-objective approach. Int J Prod Res, 58(18), 5521–5544. https://doi.org/10.1080/00207543.2019.1669840
40.
MohammedF.SelimS. Z.HassanA.SyedM. N. (2017). Multi-period planning of closed-loop supply chain with carbon policies under uncertainty. Transp Res Part D Transp Environ, 51, 146–172. https://doi.org/10.1016/j.trd.2016.10.033
41.
MonfaredM.ArmanM. H.BaratiM. (2018). Combination of the analytic network process method and multi-objective decision-making in order to predict and reduce the future risks of suppliers. J Ind Manag Perspect, 8(2), 111–134. https://jimp.sbu.ac.ir/article_87175.html
42.
MousaviS. F.ApornakA.PourhassanM. R. (2023). Robust optimization model to improve supply chain network productivity under uncertainty. J Appl Res Ind Eng, 10(2), 273–285. https://doi.org/10.22105/jarie.2022.316357.1402
NidumoluR.PrahaladC. K.RangaswamiM. R. (2009). Why sustainability is now the key driver of innovation. Harvard Business Review, 87(9), 57–64.
45.
PagellM.WuZ. (2009). Building a more complete theory of sustainable supply chain management using case studies of 10 exemplars. Supply Chain Manag, 45(2), 37–56. https://doi.org/10.1111/j.1745-493X.2009.03162.x
46.
PatidarR.AgrawalS. (2020). A mathematical model formulation to design a traditional Indian agri-fresh food supply chain: A case study problem. Benchmarking An Int. J, 27(8), 2341–2363. https://doi.org/10.1108/BIJ-01-2020-0013
47.
PishvaeeM. S.RabbaniM.TorabiS. A. (2011). A robust optimization approach to closed-loop supply chain network design under uncertainty. Appl Math Model, 35(2), 637–649. https://doi.org/10.1016/j.apm.2010.07.013
48.
RabbaniM.Hosseini-MokhallesunS. A. A.OrdibazarAH.Farrokhi-AslH. (2020). Masoud rabbani seyed ali akbar hosseini-mokhallesun and H. Farrokhi-asl, ‘A hybrid robust possibilistic approach for a sustainable supply chain location-allocation network design’. Int J Syst. Sci Oper & Logist, 7(1), 60–75. https://doi.org/10.1080/23302674.2018.1506061
RiceJ. B.CaniatoF. (2003). Building a secure and resilient supply network. Supply Chain Management Review, 7(5), 22–30.
51.
Saidi-MehrabadM.AazamiA.GoliA. (2017). A location-allocation model in the multi-level supply chain with multi-objective evolutionary approach. J Ind Syst Eng, 10(3), 140–160.
52.
SangborA. M.SafiM. R.AzarA.RabiehM. (2022). Identifying and prioritizing sustainable supply chain management enablers in the petrochemical industry by combined approach of meta-synthesis method and graph theory and matrix approach (GTMA). Ind Manag Stud, 20(46), 1–34. https://doi.org/10.22054/JIMS.2022.39256.2254
53.
SchonslebenP. (2007). Integral logistics management - Operations and supply chain management in comprehensive value-added networks (3rd ed). Auerbach Publications.
54.
ShafieeF.KazemiA.Jafarnejad ChaghooshiA.SazvarZ.Amoozad MahdirajiH. (2021). A robust multi-objective optimization model for inventory and production management with environmental and social consideration: A real case of dairy industry. J Clean Prod, 294, 126230. https://doi.org/10.1016/j.jclepro.2021.126230
55.
SuryaB.MenneF.SabhanH.SurianiS.AbubakarH.IdrisM. (2021). Economic growth, increasing productivity of SMEs, and open innovation. J Open Innov Technol Mark Complex, 7(1), 1–37. https://doi.org/10.3390/joitmc7010020
TavanaM.ArmanH.Hadi-VenchehA.MansooriS. (2023). A fuzzy multi-objective optimization model for sustainable location planning using volumetric fuzzy sets. Ann Oper Res, 1–29. https://doi.org/10.1007/s10479-023-05505-0
58.
TirkolaeeE. B.MardaniA.DashtianZ.SoltaniM.WeberG.-W. (2020). A novel hybrid method using fuzzy decision making and multi-objective programming for sustainable-reliable supplier selection in two-echelon supply chain design. J Clean Prod, 250, 119517. https://doi.org/10.1016/j.jclepro.2019.119517
59.
TsiakisP.ShahN.PantelidesC. C. (2001). Design of multi-echelon supply chain networks under demand uncertainty. Ind Eng Chem Res, 40(16), 3585–3604. https://doi.org/10.1021/ie0100030
60.
VadianA.MovahediM. M.AbriA. G.LotfiM. R. (2022). The impact of petrochemical industry economic activities on environmental factors using a fuzzy mathematical programming model. Int J Nonlinear Anal. Appl, 13(1), 539–553. https://doi.org/10.22075/ijnaa.2021.23377.2525
61.
ViswanadhamN.GaonkarR. S. (2008). Risk management in global supply chain networks. In TangC. S.TeoC.-P.WeiK.-K. (Eds.), Supply chain analysis: A handbook on the interaction of information, system and optimization (pp. 201–222). Springer US. https://doi.org/10.1007/978-0-387-75240-2_8
YuH.SolvangW. D. (2020). A fuzzy-stochastic multi-objective model for sustainable planning of a closed-loop supply chain considering mixed uncertainty and network flexibility. J Clean Prod, 266, 121702. https://doi.org/10.1016/j.jclepro.2020.121702
ZiaeiZ.JabbarzadehA. (2021). A multi-objective robust optimization approach for green location-routing planning of multi-modal transportation systems under uncertainty. J Clean Prod, 291, 125293. https://doi.org/10.1016/j.jclepro.2020.125293
