A quantile-based scenario analysis approach to biomass supply chain optimization under uncertainty. (2nd February 2017)

• Record Type: Journal Article
• Authors: Zamar, David S.; Gopaluni, Bhushan; Sokhansanj, Shahab; Newlands, Nathaniel K.
• Is Part Of: Computers & chemical engineering. Volume 97(2017)
• Page Start: 114
• Page End: 123
• Publication Date: 2017-02-02
• Subjects: Uncertainty -- Scenario analysis -- Optimization -- Renewable energy systems -- Biomass; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2016.11.015
• Languages: English
• ISSNs: 0098-1354

Abstract

Abstract : Highlights: A computationally tractable scenario analysis approach is presented. Easily applied to decision making problems under uncertainty. Uses the quantiles of the distribution of the objective and constraint functions. Near optimality of the solution can be statistically tested. Applied to a biomass supply chain optimization problem 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 paper 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. The proposed approach was found to outperform alternative methods in terms of computational efficiency and ability to meet the stochastic problem requirements.