Multi-period stochastic optimization of a sustainable multi-feedstock second generation bioethanol supply chain − A logistic case study in Midwestern United States

•Multi-period stochastic MILP model for optimizing biomass-to- biofuel supply chains.•Multiple objectives of maximizing profit, GHG emission reduction, and jobs creation.•Multiple uncertainties in biomass supply, bioethanol demand and sale price.•Evaluation of fixed vs. variable subsidy policy for e...

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Vydáno v:Land use policy Ročník 61; s. 420 - 450
Hlavní autoři: Osmani, Atif, Zhang, Jun
Médium: Journal Article
Jazyk:angličtina
Vydáno: Kidlington Elsevier Ltd 01.02.2017
Elsevier Science Ltd
Témata:
Approximation method
Benders decomposition
bioethanol
Biofuels
Biomass
Biomass-to-biofuel conversion
Biorefineries
biorefining
Case studies
Cultivation
Design optimization
Economic conditions
Effectiveness
Ethanol
Land
Land use
Livestock industry
Logistic network
Mathematical models
Midwestern United States
Multi-period supply chain planning
Multiple objective analysis
Objectives
Optimization
Panicum virgatum
Planning
Raw materials
Second generation
Stochastic mixed integer linear programming (SMILP)
Stochastic models
Supply
supply chain
Supply chains
Sustainability
uncertainty
Midwestern United States
Logistic network
Stochastic mixed integer linear programming (SMILP)
Biomass-to-biofuel conversion
Sustainability
Land use
Multi-period supply chain planning
ISSN:0264-8377, 1873-5754
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Shrnutí:•Multi-period stochastic MILP model for optimizing biomass-to- biofuel supply chains.•Multiple objectives of maximizing profit, GHG emission reduction, and jobs creation.•Multiple uncertainties in biomass supply, bioethanol demand and sale price.•Evaluation of fixed vs. variable subsidy policy for encouraging biofuel production.•Use of Sample Average Approximation algorithm along with Bender’s decomposition. This work proposes a multi-objective optimization model to design a sustainable multi-period second generation biomass-to-bioethanol supply chain under multiple uncertainties. The objective is to simultaneously maximize the economic, environmental, and social performance. The strategic decisions such as land allocation for switchgrass cultivation, biorefinery locations and capacities, and the biomass-to-bioethanol conversion pathway are determined for each planning period which are staggered across the entire planning horizon. The augmented ε–constraint method is used to trade-off among the competing objectives and to obtain feasible solutions that achieve desired levels of sustainability. In order to solve the proposed stochastic optimization model efficiently and effectively, this work proposes a solution approach involving sequential application of a modified Sample Average Approximation method and Benders decomposition. A case study is presented to demonstrate the effectiveness of the proposed mathematical model and its impact on land usage and sustainability.
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ISSN:0264-8377
1873-5754
DOI:10.1016/j.landusepol.2016.10.028