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An Economic Analysis of the Future U.S. Biofuel Industry, Facility Location, and Supply Chain Network

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  • Xiaoguang Chen

    (Research Institute of Economics and Management, Southwestern University of Finance and Economics, Chengdu 610074, China)

  • Hayri Önal

    (Department of Agricultural and Consumer Economics, University of Illinois at Urbana–Champaign, Urbana, Illinois 61801)

Abstract

This paper presents a price-endogenous, dynamic, mixed-integer nonlinear programming (MINLP) model to determine the biofuel feedstock supply response in U.S. agriculture and future biorefinery locations that meet the mandated cellulosic biofuel production targets. With a large number of supply units and potential processing locations involved, the problem could not be solved directly using MINLP solvers. We developed a sequential two-stage solution procedure to cope with this computational difficulty. The original MINLP model is decomposed into a price-endogenous agricultural sector model that solves the supply response and equilibrium in agricultural product markets, and a dynamic linear mixed-integer programming (MIP) model that solves the optimum facility location and supply chain network. The two models are solved sequentially with feedback from each other. Because of the large number of binary variables involved, computational difficulty was also encountered when solving the MIP model. We employed a heuristic backward-recursive technique to cope with this difficulty. Using moderately large test problems, we demonstrate that the heuristic solution procedures are computationally convenient and produce near-optimal solutions. We then applied this method to solve the full-scale model where nearly 3,000 U.S. counties were considered both as spatial supply units and potential refinery locations over the 2007–2022 planning horizon. Empirical results show that: (i) the U.S. biofuel mandates would lead to a significant increase in food commodity prices; (ii) regional comparative advantage in producing biofuel feedstocks would be more important than proximity to biofuel demand locations when determining the optimum refinery locations; and (iii) incorporating biofuel refinery locations in land-use decisions makes a considerable difference in the regional biomass production pattern.

Suggested Citation

  • Xiaoguang Chen & Hayri Önal, 2014. "An Economic Analysis of the Future U.S. Biofuel Industry, Facility Location, and Supply Chain Network," Transportation Science, INFORMS, vol. 48(4), pages 575-591, November.
  • Handle: RePEc:inm:ortrsc:v:48:y:2014:i:4:p:575-591
    DOI: 10.1287/trsc.2013.0488
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    References listed on IDEAS

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    2. Gökhan Memişoğlu & Halit Üster, 2016. "Integrated Bioenergy Supply Chain Network Planning Problem," Transportation Science, INFORMS, vol. 50(1), pages 35-56, February.
    3. Bhatt, Gaurav & Upadhyay, Amit & Sahoo, Kamalakanta, 2025. "Biomass supply chain network design: Integrating fixed and portable preprocessing depots for cost efficiency and sustainability," Applied Energy, Elsevier, vol. 389(C).
    4. Wang, Xin & Lim, Michael K. & Ouyang, Yanfeng, 2017. "Food-energy-environment trilemma: Policy impacts on farmland use and biofuel industry development," Energy Economics, Elsevier, vol. 67(C), pages 35-48.
    5. Sanchit Singh & Subhash C. Sarin & Sandeep Singh Sangha, 2025. "Nested benders decomposition for a deterministic biomass feedstock logistics problem," Journal of Global Optimization, Springer, vol. 91(1), pages 95-127, January.
    6. Dalemans, Floris & Muys, Bart & Verwimp, Anne & Van den Broeck, Goedele & Bohra, Babita & Sharma, Navin & Gowda, Balakrishna & Tollens, Eric & Maertens, Miet, 2018. "Redesigning oilseed tree biofuel systems in India," Energy Policy, Elsevier, vol. 115(C), pages 631-643.
    7. Sarker, Bhaba R. & Wu, Bingqing & Paudel, Krishna P., 2019. "Modeling and optimization of a supply chain of renewable biomass and biogas: Processing plant location," Applied Energy, Elsevier, vol. 239(C), pages 343-355.
    8. He-Lambert, Lixia & English, Burton C. & Lambert, Dayton M. & Shylo, Oleg & Larson, James A. & Yu, T. Edward & Wilson, Bradly, 2018. "Determining a geographic high resolution supply chain network for a large scale biofuel industry," Applied Energy, Elsevier, vol. 218(C), pages 266-281.

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