Multiobjective optimization of hydrocarbon biorefinery supply chain designs under uncertainty

Berhane H. Gebreslassie, Yuan Yao, Fengqi You*

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

6 Scopus citations

Abstract

In this work we propose a bi-criterion, multi-period, stochastic mixed-integer linear programming model that address the optimal design and planning of hydrocarbon biorefinery supply chains under supply and demand uncertainties. The model accounts for diverse conversion technologies, feedstock seasonality and fluctuation, geographical diversity, biomass degradation, demand variation, government incentives and risk management. The objective is simultaneous minimization of the expected annualized cost and the financial risk. The financial risk is measured by conditional value-at-risk. The model simultaneously determines the optimal network design, technology selection, capital investment, production planning, and logistics management decisions. Multi-cut L-shaped decomposition approach is implemented to circumvent the computational burden of solving large scale problems. The capabilities of the proposed modeling framework and solution algorithm are illustrated through the optimal design of the hydrocarbon biorefinery supply chain in the State of Illinois.

Original languageEnglish (US)
Article number6426661
Pages (from-to)5560-5565
Number of pages6
JournalProceedings of the IEEE Conference on Decision and Control
DOIs
StatePublished - Dec 1 2012
Event51st IEEE Conference on Decision and Control, CDC 2012 - Maui, HI, United States
Duration: Dec 10 2012Dec 13 2012

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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