A Bi-objective, Risk-Aversion Optimization Model and Its Application in a Biofuel Supply Chain

Krystel K. Castillo-Villar () and Yajaira Cardona-Valdes
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Krystel K. Castillo-Villar: Mechanical Engineering Department and Texas Sustainable Energy Research Institute, University of Texas at San Antonio, One UTSA Circle
Yajaira Cardona-Valdes: Autonomous University of Coahuila, Unidad Camporredondo s/n, Edificio S

A chapter in Supply Chain Risk Mitigation, 2022, pp 275-291 from Springer

Abstract: Abstract This chapter discusses approaches to incorporate risk aversion in supply chain network design. The design of supply chain networks involves multiple uncertainty sources. Most of the previous works took a risk-neutral approach by modeling the problem as two-stage stochastic formulation. However, most decision-makers are not risk neutral, and a better understanding of the risk involved is germane. The contributions of this chapter are threefold: methodological, algorithmic, and application. From the methodological perspective, we propose a novel mathematical formulation of a bi-objective two-stage stochastic programming model that measures the trade-off between the expected cost and the conditional value at risk (CVaR). From the algorithmic point of view, the augmented ε-constraint method is used for solving the model and getting the Pareto solutions set. From the application side, a real-life data-driven case study at a state level is solved to optimality to obtain pragmatic and managerial insights that enable the investigation of solutions for different levels of risk aversion; this, in turn, helps to increase the production of reliable and cost-effective biofuel. The mathematical model can be transferable to other applications that seek to provide risk-averse solutions to decision-makers.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-031-09183-4_12

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DOI: 10.1007/978-3-031-09183-4_12

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