<script type="text/javascript">
<!--
document.write('<div id="oa_widget"></div>');
document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=undefined&type=result"></script>');
-->
</script>

COPY SCRIPT

For further information contact us at helpdesk@openaire.eu

Stochastic optimization of sustainable hybrid generation bioethanol supply chains

descriptionPublicationkeyboard_double_arrow_right Article , Journal 01 May 2015Publisher:Elsevier BVJournal:Transportation Research Part E: Logistics and Transportation Review, volume 77, pages 1-28 (issn: 1366-5545,

Authors: Jun Zhang; Raphael Onyeaghala; Vinay Gonela; Atif Osmani;

doi: 10.1016/j.tre.2015.02.008

Stochastic optimization of sustainable hybrid generation bioethanol supply chains

- Summary
- Metrics

Abstract

This paper focuses on designing a hybrid generation bioethanol supply chain (HGBSC) that will account for economic, environmental and social aspects of sustainability under various uncertainties. A stochastic mixed integer linear programming model is proposed to design an optimal HGBSC. A case study set in the state of North Dakota in the United States is used as an application of the proposed model. The results suggest that the designs of optimal HGBSC change when different sustainability standards are applied. In addition, sensitivity analysis is conducted to provide deeper understanding of the proposed model.

Related Organizations

Southwest Minnesota State University
United States
California Baptist University
United States
Minnesota State University Moorhead
United States
University of California System
United States
Minnesota State University Moorhead
United States

View all View all

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	59
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%