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dc.contributor.authorCobuloglu, Halil I.
dc.contributor.authorBuyuktahtakin, Esra
dc.date.accessioned2017-06-01T19:48:13Z
dc.date.available2017-06-01T19:48:13Z
dc.date.issued2017-05
dc.identifier.citationCobuloglu, 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 251–263en_US
dc.identifier.issn0360-8352
dc.identifier.otherWOS:000401387000022
dc.identifier.urihttp://dx.doi.org/10.1016/j.cie.2017.02.017
dc.identifier.urihttp://hdl.handle.net/10057/13259
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractThe 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.en_US
dc.description.sponsorshipNational Science Foundation under Grant No. EPS 0903806, the State of Kansas through the Kansas Board of Regents, and the National Science Foundation CAREER Award under Grant # CBET-1554018.en_US
dc.language.isoen_USen_US
dc.publisherElsevier Ltd.en_US
dc.relation.ispartofseriesComputers & Industrial Engineering;v.107
dc.subjectOperations research (OR) in agricultureen_US
dc.subjectOR in energyen_US
dc.subjectStochastic programmingen_US
dc.subjectUncertaintyen_US
dc.subjectDecomposition algorithmen_US
dc.subjectBenders cutting planesen_US
dc.subjectOptimizationen_US
dc.subjectAnalyticsen_US
dc.subjectBiofuel and food productionen_US
dc.subjectFood securityen_US
dc.subjectSustainabilityen_US
dc.titleA two-stage stochastic mixed-integer programming approach to the competition of biofuel and food productionen_US
dc.typeArticleen_US
dc.rights.holderCopyright © 2017 Elsevier B.V. or its licensors or contributors.en_US


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