Impact of government policies on Sustainable Petroleum Supply Chain (SPSC): A case study – Part I (Models)

• Autorzy: Ghahremanlou Davoud , Kubiak Wieslaw

Identyfikatory

DOI

10.7494/dmms.2020.14.1.3648

• Języki publikacji: EN

Abstrakty

EN

Environmental concerns and energy security have led governments to establish legislations to convert Conventional Petroleum Supply Chain (CPSC) to Sustainable Petroleum Supply Chain (SPSC). The United States (US), one of the biggest oil consumers in the world, has created regulations to manage ethanol production and consumption for the last half century. Though these regulations have created new opportunities, they have also added new burdens to the obligated parties. It is thus key for the government, the obligated parties, and related businesses to study the impact of the policies on the SPSC. We develop a two-stage stochastic programming model, General Model (GM), which incorporates Renewable Fuel Standard 2 (RFS2), Tax Credits, Tariffs, and Blend Wall (BW) to study the policy impact on the SPSC using cellulosic ethanol. The model, as any other general model available in the literature, makes it highly impractical to study the policy impact due to the model’s computational complexity. We use the GM to derive a Lean Model (LM) to study the impact by running computational experiments more efficiently and consequently by arriving at robust managerial insights much faster. We present a case study of the policy impact on the SPSC in the State of Nebraska using the LM in the accompanying part II (Ghahremanlou and Kubiak 2020).

Słowa kluczowe

EN

sustainable petroleum supply chain; two-stage stochastic programming; government policies

PL

zrównoważony łańcuch dostaw ropy naftowej; programowanie stochastyczne dwuetapowe; polityka rządu

• Wydawca: AGH University of Science and Technology Press
• Czasopismo: Decision Making in Manufacturing and Services
• Rocznik: 2020
• Tom: Vol. 14, no. 1
• Strony: 23--55
• Opis fizyczny: Bibliogr. 56 poz.

Twórcy

autor

Ghahremanlou Davoud

• Faculty of Business Administration, Memorial University of Newfoundland, St. John’s, NL, Canada

• https://orcid.org/0000-0001-5186-8035

autor

Kubiak Wieslaw

• Faculty of Business Administration, Memorial University of Newfoundland, St. John’s, NL, Canada

• https://orcid.org/0000-0002-3181-3042

Bibliografia

• [1] Agarwal, A.K., 2007. Biofuels (alcohols and biodiesel) applications as fuels for internalcombustion engines.Progress in Energy and Combustion Science, 33(3), pp. 233–271.
• [2] Aguilar, F.X., Cai, Z., Mohebalian, P., Thompson, W., 2015. Exploring the drivers’ side ofthe “blend wall”: U.S. consumer preferences for ethanol blend fuels.Energy Economics,49, pp. 217–226.
• [3] Andersen, F.E., Díaz, M.S., Grossmann, I.E., 2013. Multiscale strategic planning model forthe design of integrated ethanol and gasoline supply chain.AIChE Journal, 59(12),pp. 4655–4672.
• [4] Awudu, I., Zhang, J., 2012. Uncertainties and sustainability concepts in biofuel supply chainmanagement: A review.Renewable and Sustainable Energy Reviews, 16(2), pp.1359–1368.
• [5] Azarmand, Z., Neishabouri, E., 2009.Location Allocation Problem, pp. 93–109. Heidelberg:Physica-Verlag HD.
• [6] Ba, B.H., Prins, C., Prodhon, C., 2016. Models for optimization and performance evaluationof biomass supply chains: An operations research perspective.Renewable Energy, 87(2),pp. 977–989.
• [7] Babcock, B.A., 2012. The impact of US biofuel policies on agricultural price levels andvolatility.China Agricultural Economic Review, 4(4), pp. 407–426.
• [8] Baeyens, J., Kang, Q., Appels, L., Dewil, R., Lv, Y., Tan, T., 2015. Challenges and opportunities in improving the production of bio-ethanol.Progress in Energy and CombustionScience, 47, pp. 60–88.
• [9] BETO, U., 2013. Replacing the whole barrel-to reduce U.S. dependence on oil. Bioenergy Technologies Office, US (ed.). Office of Energy Efficiency and Renewable Energy.
• [10] Bischoff, M., Dächert, K., 2009. Allocation search methods for a generalized class of location – allocation problems.European Journal of Operational Research, 192(3), pp. 793–807.
• [11] Chen, C.-W., Fan, Y., 2012. Bioethanol supply chain system planning under supply anddemand uncertainties.Transportation Research Part E: Logistics and TransportationReview, 48(1), pp. 150–164.
• [12] Chukwuma, E., 2019. Facility location allocation modelling for bio-energy system in Anambra State of Nigeria: Integration of GIS and location model.Renewable Energy, 141, pp. 460–467.
• [13] Cooper, L., 1963, June. Location – allocation problems. Operations Research, 11(3),pp. 331–343.
• [14] DiNapoli, J. Renshaw J., 2018. Exclusive: Philadelphia Energy Solutions to file forbankruptcy – memo https://www.reuters.com/article/us-philadelphiaenergysolutions-bankruptc/exclusive-philadelphia-energy-solutions-to-file-for-bankruptcy-memo-idUSKBN1FA18P [Accessed on January 21, 2018].
• [15] Dios Ortúzar, J. de, Willumsen L.G., 2011.References, pp. 551–579. John Wiley & Sons, Ltd.
• [16] Duffield, J.A., Xiarchos, I.M., Halbrook, S.A., 2008. Ethanol policy: past, present, and future. South Dakota Law Review, 53, 425.
• [17] El-Naggar, N.E.-A., Deraz, S., Khalil, A., 2014. Bioethanol production from lignocellulosicfeedstocks based on enzymatic hydrolysis: Current status and recent developments.Biotechnology, 13(1), pp 1–21.
• [18] Environmental Protection Agency, U. S. (2017). Overview for renewable fuel standard –renewable fuel standard program – US EPA. https://www.epa.gov/renewable-fuel-standard-program/overview-renewable-fuel-standard [accessed on June 17, 2017].
• [19] Gebreslassie, B.H., Yao, Y., You, F., 2012. Design under uncertainty of hydrocarbon biorefinery supply chains: Multiobjective stochastic programming models, decompositionalgorithm, and a comparison between CVaR and downside risk. AIChE Journal, 58(7),pp. 2155–2179.
• [20] Ghaderi, H., Pishvaee, M.S., Moini, A., 2016. Biomass supply chain network design: An optimization-oriented review and analysis. Industrial Crops and Products, 94, pp. 972–1000.
• [21] Ghahremanlou, D., Kubiak, W., 2020. Impact of government policies on sustainable petroleumsupply chain (spsc): A case study - Part II (The State of Nebraska). Submitted. Decision Making in Manufacturing and Services, 14(1).
• [22] Gupta, A., Verma, J.P., 2015. Sustainable bio-ethanol production from agro-residues: A review. Renewable and Sustainable Energy Reviews, 41, pp. 550–567.
• [23] Humbird, D., Davis, R., Tao, L., Kinchin, C., Hsu, D., Aden, A., Schoen, P., Lukas, J.,Olthof, B., Worley, M., Sexton, D., Dudgeon, D., 2011. Process design and economics forbiochemical conversion of lignocellulosic biomass to ethanol: dilute-acid pretreatmentand enzymatic hydrolysis of corn stover. Technical Report, National Renewable Energy Laboratory (NREL), Golden, CO.
• [24] Independent Statistics & Analysis, U.S. Energy Information Administration, 2015, November 19. Passenger travel accounts for most of world transportation energy use. Today inEnergy , https://www.eia.gov/todayinenergy/detail.php?id=23832 [accessed on February12, 2016].
• [25] Kazemzadeh, N., Hu, G., 2015. Evaluation of the impacts of governmental policies on thebiofuels supply chain design under uncertainty. International Journal of Sustainable Economy, 7(3), pp. 203–219.
• [26] Kessel, D.G., 2000. Global warming – facts, assessment, countermeasures. Journal of Petroleum Science and Engineering, 26(1), pp. 157–168.
• [27] Kuenne, R.E., Soland, R.M., 1972, December. Exact and approximate solutions to themultisource weber problem. Mathematical Programming, 3(1), 193–209.
• [28] Legal Information Institute, 2010. 40 CFR § 80.1406 – Who is an obligated party under theRFS program?, https://www.law.cornell.edu/cfr/text/40/80.1406 [accessed on March20, 2017].
• [29] Lima, C., Relvas, S., Barbosa-Póvoa, A.P.F.D., 2016. Downstream oil supply chain management: A critical review and future directions.Computers & Chemical Engineering, 92, pp. 78–92.
• [30] Liu, P., Pistikopoulos, E.N., Li, Z., 2010. Decomposition based stochastic programmingapproach for polygeneration energy systems design under uncertainty. Industrial & Engineering Chemistry Research, 49(7), pp. 3295–3305.
• [31] Mafakheri, F., Nasiri, F., 2014. Modeling of biomass-to-energy supply chain operations:applications, challenges and research directions. Energy Policy, 67, pp. 116–126.
• [32] IHS Markit, 2019. Gasoline octane improvers/oxygenates. Chemical Economics Handbook, https://ihsmarkit.com/products/gasoline-octane-improvers-chemical-economics-handbook.html [accessed on October 20, 2019].
• [33] McPhail, L., Westcott, P., Lutman, H., 2011. The renewable identification number system and U.S. biofuel mandates. Economic Research Service (ERS), United States Department ofAgriculture (USDA).
• [34] Mestre, A.M., Oliveira, M.D., Barbosa-Póvoa, A.P., 2015. Location–allocation approaches forhospital network planning under uncertainty. European Journal of Operational Research, 240(3), pp. 791–806.
• [35] Meyer, S., 2007. U.S. biofuels analysis under uncertaintiy and volatility in the biofuel industry: FAPRI stochastic modelind. “Biofuels, Food and Feed Tradeoffs” Farm Foundation, 12–13 April 2007, Saint Louis, Missouri.
• [36] Murray, A.T., Church, R.L., 1996, June. Applying simulated annealing to location-planningmodels. Journal of Heuristics, 2(1), pp. 31–53.
• [37] Najmi, A., Hamed Shakouri G., Nazari, S., 2016. An integrated supply chain: A large scalecomplementarity model for the biofuel markets.Biomass and Bioenergy, 86, pp. 88–104.
• [38] Qiu, C., Colson, G., Wetzstein, M., 2014. An ethanol blend wall shift is prone to increasepetroleum gasoline demand.Energy Economics, 44, pp. 160–165.
• [39] Renewable Fuels Association (RFA), 2015. Blend wall, http://ethanolrfa.org/issues/blend-wall/ [accessed on August 12, 2016].
• [40] Renshaw, J., 2018. U.S. biofuels sector blasts EPA settlement with bankrupt Philadelphia refinery, https://www.reuters.com/article/us-usa-biofuels-rins/epa-settlement-with-bankrupt-philadelphia-refinery-roils-biofuels-market-idUSKCN1GP1RC [accessed onMarch 13, 2018].
• [41] Sahebi, H., Nickel, S., Ashayeri, J., 2014. Strategic and tactical mathematical programmingmodels within the crude oil supply chain context – A review. Computers & Chemical Engineering, 68, pp. 56–77.
• [42] Serrano-Hernandez, A., Faulin, J., Belloso, J., Sawik B., 2017. The effect of environmentalcriteria on locating a biorefinery: A green facility location problem. Decision Making in Manufacturing and Services, 11(1–2), pp. 19–30.
• [43] Shankar, B.L., Basavarajappa, S., Chen, J.C., Kadadevaramath, R.S., 2013. Location and allocation decisions for multi-echelon supply chain network – A multiobjective evolutionaryapproach. Expert Systems with Applications, 40(2), pp. 551–562.
• [44] Sharma, B., Ingalls, R., Jones, C., Khanchi, A., 2013. Biomass supply chain design andanalysis: basis, overview, modeling, challenges, and future. Renewable and Sustainable Energy Reviews, 24, pp. 608–627.
• [45] Simeone, C., 2018. Part 1: Philadelphia energy solutions bankruptcy basics, https://kleinmanenergy.upenn.edu/blog/2018/02/02/part-1-philadelphia-energy-solutions-bankruptcy-basics [KCEP blog] [accessed on 02 May 2018].
• [46] Stein, K., 2018. An entirely predictable bankruptcy, https://www.instituteforenergyresearch.org/renewable/entirely-predictable-bankruptcy [accessed on January 23, 2018].
• [47] Thompson, W., Meyer, S., Westhoff, P., 2009. Renewable identification numbers are thetracking instrument and bellwether of U.S. biofuel mandates. Identifikationsnummernfür Erneuerbare Energien als Mittel der Rückverfolgbarkeit und Indikator für US-amerikanische Biokraftstoffverpflichtungen. Les numéros d’identification des carburantsrenouvelables sont les instruments et baromètres de suivi des obligations légales concernant les biocarburants aux États-Unis.EuroChoices, 8(3), pp. 43–50.
• [48] Tong, K., Gong, J. Yue, D., You, F., 2013. Stochastic programming approach to optimaldesign and operations of integrated hydrocarbon biofuel and petroleum supply chains. ACS Sustainable Chemistry&Engineering, 2(1), pp. 49–61.
• [49] Tong, K., Gleeson, M.J., Rong, G., You, F., 2014. Optimal design of advanced drop-in hydrocarbon biofuel supply chain integrating with existing petroleum refineries underuncertainty. Biomass and Bioenergy, 60, pp. 108–120.
• [50] Tong, K., You, F., Rong, G., 2014. Robust design and operations of hydrocarbon biofuelsupply chain integrating with existing petroleum refineries considering unit cost objective. Computers & Chemical Engineering, 68, pp. 128–139.
• [51] Wang, K.-J., Lee, C.-H., 2015. A revised ant algorithm for solving location–allocation problemwith risky demand in a multi-echelon supply chain network. Applied Soft Computing, 32, pp. 311–321.
• [52] Whistance, J., Ripplinger, D., Thompson, W., 2016. Biofuel-related price transmission usingrenewable identification number prices to signal mandate regime. Energy Economics, 55, pp. 19–29.
• [53] Willette, J.K., 2018. Bankruptcy judge rules on Philadelphia Energy Solutions case, https://www.farmprogress.com/energy/bankruptcy-judge-rules-philadelphia-energy-solutions-case [accessed on April 06, 2018].
• [54] Yacobucci, B.D., 2010. Intermediate-level blends of ethanol in gasoline, and the ethanol ‘blendwall’. Congressional Research Service Report (R40445).
• [55] Yan, L., 2012. Analysis of the international oil price fluctuations and its influencing factors.American Journal of Industrial and Business Management, 2(2), pp. 39–46.
• [56] Yue, D., You, F., Snyder, S.W., 2014. Biomass-to-bioenergy and biofuel supply chain opti-mization: Overview, key issues and challenges.Computers & Chemical Engineering, 66,pp. 36–56.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021)