Optimizing a sustainable logistics problem in a renewable energy network using a genetic algorithm

Javad Sadeghi () and Karl R. Haapala ()
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Javad Sadeghi: Oregon State University
Karl R. Haapala: Oregon State University

OPSEARCH, 2019, vol. 56, issue 1, No 4, 73-90

Abstract: Abstract Renewable energy sources, including bio-energy technologies, have been introduced to overcome sustainability challenges, such as negative environmental impacts and energy insecurity due to reliance on fossil fuels. Logistics activities have a significant effect on the cost and environmental impacts of renewable energy supply chains. Understanding and reducing the carbon footprint of renewable energy supply chains can aid in mitigating environmental impacts. Thus, this research presents a mathematical model that can be used to optimize renewable energy supply chain logistics costs and carbon footprint. The proposed model considers a biomass-to-bio-oil supply chain, including harvesting and collection sites, bio-refineries, and distribution centers. It is assumed that mobile and fixed refineries will be used to produce bio-oil. The model considers the mass of biomass and bio-oil, number of mobile and fixed refineries, and number of truck trips to minimize total cost, where a carbon tax is used to represent carbon footprint in the mathematical cost model. A genetic algorithm is designed to obtain a near-optimal solution. Six scenarios for mobile and fixed refinery capacity are tested in performing sensitivity analysis of the model. The results indicate that the mathematical model of the bio-oil supply chain has reasonable relationships between input and output variables. The model is able to incorporate the impact of carbon emissions in a mixed-refinery bio-oil supply chain as a cost parameter. It was also found that increasing mobile refinery capacity has the greater effect on reducing total cost and carbon emissions than increasing fixed refinery capacity.

Keywords: Sustainable supply chains management; Carbon tax; Genetic algorithm; Mixed integer linear programming model (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s12597-019-00356-5

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