Data-driven robust optimization to design an integrated sustainable forest biomass-to-electricity network under disjunctive uncertainties

Saeed Sadeghi Darvazeh, Farzaneh Mansoori Mooseloo, Fatemeh Gholian-Jouybari, Maghsoud Amiri, Hossein Bonakdari and Mostafa Hajiaghaei-Keshteli

Applied Energy, 2024, vol. 356, issue C, No S0306261923017683

Abstract: The consequences of using costly, nonrenewable, and nonsustainable fossil fuels have forced countries to focus on renewable energy resources. In addition, the necessity of smooth material and information flow in the supply chain has motivated scholars to invent different mathematical approaches to designing an integrated supply chain. Such an integrated biomass-to-electricity supply chain can guarantee smooth material and information flow in the supply chain. To this end, a data-driven robust optimization approach under disjunctive uncertainties is developed for designing and optimizing an integrated sustainable biomass-to-electricity supply chain network. Instead of assuming uncertain parameters data to be less scattered or almost uniform, an uncertainty set based on data with disjunctive structures is proposed to identify the uncertainty space more accurately and flexibly. Specifically, the data on uncertain parameters are clustered using the K-means technique. Then, the primary uncertainty sets are formed for each cluster. The proposed uncertain parameter space is constructed by incorporating these primary uncertainty sets, which form using support vector clustering. We studied a case in Iran to probe the performance of the proposed approach. The computational results indicate that the proposed framework provides maximum protection against disjunctive uncertainty with minimum robustness price.

Keywords: Supply chain design; Biomass-to-electricity; Machine learning; Data-driven robust optimization; Disjunctive uncertainty (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261923017683
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:356:y:2024:i:c:s0306261923017683

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2023.122404

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().