A stochastic biomass blending problem in decentralized supply chains

Blending biomass materials of different physical or chemical properties provides an opportunity to adjust the quality of the feedstock to meet the specifications of the conversion platform. We propose a model which identifies the right mix of biomass to optimize the performance of the thermochemical...

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Bibliographic Details
Published in:Naval research logistics 2021-06, Vol.68 (4), p.434-453
Main Authors: Ekşioğlu, Sandra D., Gulcan, Berkay, Roni, Mohammad, Mason, Scott
Format: Article
Language:English
Subjects:
09 BIOMASS FUELS
Algorithms
Approximation
Approximation method
Biomass
biomass supply chain
biomass supply chain, blending problem, decentralized supply chain, stochastic optimization
Blending
blending problem
Chemical properties
Chemicophysical properties
Constraint modelling
Conversion
decentralized supply chain
Decision making
Identification
Mathematical analysis
Minimum cost
Mixtures
Softwoods
stochastic optimization
Supply chains
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Summary:Blending biomass materials of different physical or chemical properties provides an opportunity to adjust the quality of the feedstock to meet the specifications of the conversion platform. We propose a model which identifies the right mix of biomass to optimize the performance of the thermochemical conversion process at the minimum cost. This is a chance‐constraint programming (CCP) model which takes into account the stochastic nature of biomass quality. The proposed CCP model ensures that process requirements, which are impacted by physical and chemical properties of biomass, are met most of the time. We consider two problem settings, a centralized and a decentralized supply chain. We propose a mixed‐integer linear program to model the blending problem in the centralized setting and a bilevel program to model the blending problem in the decentralized setting. We use the sample average approximation method to approximate the chance constraints, and propose solution algorithms to solve this approximation. We develop a case study for South Carolina using data provided by the Billion Ton Study. Based on our results, the blends identified consist mainly of pine and softwood residues. The blends identified and the suppliers selected by both models are different. The cost of the centralized supply chain is 2%–6% lower. The implications of these results are twofold. First, these results could lead to improved collaborations in the supply chain. Second, these results provide an estimate of the approximation error from assuming centralized decision making in the supply chain.
ISSN:0894-069X
1520-6750
DOI:10.1002/nav.21971