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Title: A quantile-based scenario analysis approach to biomass supply chain optimization under uncertainty

Abstract

Supply chain optimization for biomass-based power plants is an important research area due to greater emphasis on renewable power energy sources. Biomass supply chain design and operational planning models are often formulated and studied using deterministic mathematical models. While these models are beneficial for making decisions, their applicability to real world problems may be limited because they do not capture all the complexities in the supply chain, including uncertainties in the parameters. This study develops a statistically robust quantile-based approach for stochastic optimization under uncertainty, which builds upon scenario analysis. We apply and evaluate the performance of our approach to address the problem of analyzing competing biomass supply chains subject to stochastic demand and supply. Finally, the proposed approach was found to outperform alternative methods in terms of computational efficiency and ability to meet the stochastic problem requirements.

Authors:
 [1];  [1];  [2];  [3]
  1. Univ. of British Columbia, Vancouver, BC (Canada). Dept. of Chemical and Biological Engineering
  2. Univ. of British Columbia, Vancouver, BC (Canada). Dept. of Chemical and Biological Engineering; Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Resource and Engineering Systems Group. Environmental Sciences Division
  3. Summerland Research and Development Centre, BC (Canada)
Publication Date:
Research Org.:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Univ. of British Columbia, Vancouver, BC (Canada); Summerland Research and Development Centre, BC (Canada)
Sponsoring Org.:
USDOE; Natural Sciences and Engineering Research Council of Canada (NSERC); Networks of Centres of Excellence of Canada (NCE)
OSTI Identifier:
1338560
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Computers and Chemical Engineering
Additional Journal Information:
Journal Volume: 97; Journal ID: ISSN 0098-1354
Country of Publication:
United States
Language:
English
Subject:
09 BIOMASS FUELS; 29 ENERGY PLANNING, POLICY, AND ECONOMY; Uncertainty; Scenario analysis; Optimization; Renewable energy systems; Biomass

Citation Formats

Zamar, David S., Gopaluni, Bhushan, Sokhansanj, Shahab, and Newlands, Nathaniel K. A quantile-based scenario analysis approach to biomass supply chain optimization under uncertainty. United States: N. p., 2016. Web. doi:10.1016/j.compchemeng.2016.11.015.
Zamar, David S., Gopaluni, Bhushan, Sokhansanj, Shahab, & Newlands, Nathaniel K. A quantile-based scenario analysis approach to biomass supply chain optimization under uncertainty. United States. https://doi.org/10.1016/j.compchemeng.2016.11.015
Zamar, David S., Gopaluni, Bhushan, Sokhansanj, Shahab, and Newlands, Nathaniel K. 2016. "A quantile-based scenario analysis approach to biomass supply chain optimization under uncertainty". United States. https://doi.org/10.1016/j.compchemeng.2016.11.015. https://www.osti.gov/servlets/purl/1338560.
@article{osti_1338560,
title = {A quantile-based scenario analysis approach to biomass supply chain optimization under uncertainty},
author = {Zamar, David S. and Gopaluni, Bhushan and Sokhansanj, Shahab and Newlands, Nathaniel K.},
abstractNote = {Supply chain optimization for biomass-based power plants is an important research area due to greater emphasis on renewable power energy sources. Biomass supply chain design and operational planning models are often formulated and studied using deterministic mathematical models. While these models are beneficial for making decisions, their applicability to real world problems may be limited because they do not capture all the complexities in the supply chain, including uncertainties in the parameters. This study develops a statistically robust quantile-based approach for stochastic optimization under uncertainty, which builds upon scenario analysis. We apply and evaluate the performance of our approach to address the problem of analyzing competing biomass supply chains subject to stochastic demand and supply. Finally, the proposed approach was found to outperform alternative methods in terms of computational efficiency and ability to meet the stochastic problem requirements.},
doi = {10.1016/j.compchemeng.2016.11.015},
url = {https://www.osti.gov/biblio/1338560}, journal = {Computers and Chemical Engineering},
issn = {0098-1354},
number = ,
volume = 97,
place = {United States},
year = {Mon Nov 21 00:00:00 EST 2016},
month = {Mon Nov 21 00:00:00 EST 2016}
}

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Cited by: 23 works
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Works referenced in this record:

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Works referencing / citing this record:

An Overview of Current Models and Approaches to Biomass Supply Chain Design and Management
journal, June 2018


A Multilayer Model Predictive Control Methodology Applied to a Biomass Supply Chain Operational Level
journal, January 2017