• Login
    View Item 
    •   Shocker Open Access Repository Home
    • Engineering
    • Industrial, Systems, and Manufacturing Engineering
    • ISME Faculty Scholarship
    • ISME Research Publications
    • View Item
    •   Shocker Open Access Repository Home
    • Engineering
    • Industrial, Systems, and Manufacturing Engineering
    • ISME Faculty Scholarship
    • ISME Research Publications
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    A two-stage stochastic mixed-integer programming approach to the competition of biofuel and food production

    Date
    2017-05
    Author
    Cobuloglu, Halil I.
    Buyuktahtakin, Esra
    Metadata
    Show full item record
    Citation
    Cobuloglu, Halil I.; Buyuktahtakin, I. Esra. 2017. A two-stage stochastic mixed-integer programming approach to the competition of biofuel and food production. Computers & Industrial Engineering, vol. 107:pp 251–263
    Abstract
    The multi-attribute biomass and food production (BFP) problem facing farmers and co-operatives is further complicated by uncertainties in crop yield and prices. In this paper, we present a two-stage stochastic mixed-integer programming (MIP) model that maximizes the economic and environmental benefits of food and biofuel production. The uncertain parameters of yield amount and price level are calculated using real data. Economic aspects include revenue obtained from biomass and food crop sales as well as costs related to seeding, production, harvesting, and transportation operations at the farm level. Environmental effects include greenhouse gas (GHG) emissions, carbon sequestration, soil erosion, and nitrogen leakage to water. The first-stage variables define binary decisions for allocating various land types to food and energy crops, while the second-stage variables are operational decisions related to harvesting, budget allocation, and amounts of different yield types. We present a decomposition algorithm, which is enhanced with specialized Benders cuts for solving this stochastic MIP problem. The computational efficiency of the proposed model and approach is demonstrated by applying it to a real case study involving switchgrass and corn production in the state of Kansas. We measure the solution quality and speed of the decomposition method over stochastic and deterministic models. Results indicate the significant benefit of using the stochastic yield-level information in an optimization model. The proposed stochastic MIP model provides important strategies and insights into decision making for biofuel and food production under uncertainty.
    Description
    Click on the DOI link to access the article (may not be free).
    URI
    http://dx.doi.org/10.1016/j.cie.2017.02.017
    http://hdl.handle.net/10057/13259
    Collections
    • ISME Research Publications

    Browse

    All of Shocker Open Access RepositoryCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsBy TypeThis CollectionBy Issue DateAuthorsTitlesSubjectsBy Type

    My Account

    LoginRegister

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    DSpace software copyright © 2002-2023  DuraSpace
    DSpace Express is a service operated by 
    Atmire NV