A multi-objective, hub-and-spoke model to design and manage biofuel supply chains

In this paper we propose a multi-objective, mixed integer linear programming model to design and manage the supply chain for biofuels. This model captures the trade-offs that exist between costs, environmental and social impacts of delivering biofuels. The in-bound supply chain for biofuel plants re...

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Bibliographic Details
Published in:Annals of operations research 2017-02, Vol.249 (1-2), p.351-380
Main Authors: Roni, Mohammad S., Eksioglu, Sandra D., Cafferty, Kara G., Jacobson, Jacob J.
Format: Article
Language:English
Subjects:
Alternative energy
Biodiesel fuels
Biofuels
Biomass
Business and Management
Case studies
Combinatorics
Cost analysis
Costs
Design
Energy consumption
Energy policy
Ethanol
Fossil fuels
Fuels
Integer programming
Laboratories
Linear programming
Logistics
Management decisions
Mathematical models
Multiple objective
Operations research
Operations Research/Decision Theory
Optimization
Pareto optimum
Railroad transportation
Renewable energy
Renewable resources
Social impact
Studies
Supply chains
Theory of Computation
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Summary:In this paper we propose a multi-objective, mixed integer linear programming model to design and manage the supply chain for biofuels. This model captures the trade-offs that exist between costs, environmental and social impacts of delivering biofuels. The in-bound supply chain for biofuel plants relies on a hub-and-spoke structure which optimizes transportation costs of biomass. The model proposed optimizes the CO 2 emissions due to transportation-related activities in the supply chain. The model also optimizes the social impact of biofuels. The social impacts are evaluated by the number of jobs created. The multi-objective optimization model is solved using an augmented ϵ -constraint method. The method provides a set of Pareto optimal solutions. We develop a case study using data from the Midwest region of the USA. The numerical analyses estimates the quantity and cost of cellulosic ethanol delivered under different scenarios generated. The insights we provide will help policy makers design policies which encourage and support renewable energy production.
ISSN:0254-5330
1572-9338
DOI:10.1007/s10479-015-2102-3