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Abstract
While ever-growing bio-ethanol production poses considerable challenges to the bioenergy supply chain, the risk of refinery operation disruptions further compromises the efficiency and reliability of the energy supply system. This paper applies discrete and continuous reliable facility location models to the design of reliable bio-ethanol supply chains so that the system can hedge against potential operational disruptions. The discrete model is shown to be suitable for obtaining the exact optimality for small or moderate instances, while the continuous model has superior computational tractability for large-scale applications. The impacts of both site-independent and dependent disruptions (i.e., due to flooding) are analyzed in empirical case study for the State of Illinois (one of the main biomass supply states in the U.S.). The reliable solution is compared with a deterministic solution under the same setting. It is found that refinery disruptions, especially those site-dependent ones, affect both optimal refinery deployment and the supply chain cost. Sensitivity analysis is also conducted to show how refinery failure probability and fixed cost (for building biorefineries) affect optimal supply chain configuration and the total expected system cost.
Highlights
The U.S bio-ethanol industry has been experiencing phenomenal growth in recent years
The Lagrangian relaxation (LR) algorithm is coded in C++, while the continuous approximation (CA) approach is coded in MATLAB 8.3
Reliable facility location design models are applied to determine optimal refinery locations that can hedge against the risk of refinery operation disruptions and the consequence of enormous social disbenefits
Summary
The U.S bio-ethanol industry has been experiencing phenomenal growth in recent years. Tursun et al [4] proposed a discrete facility location model to optimize the total cost including transporting and processing biomass, delivering ethanol, building and operating biorefineries They focus on the biofuel industry in Illinois to draw practical implications (e.g., four new refineries would be built by the year 2022). The studied problem allows for simultaneous refinery disruptions that may force biomass providers to seek more distant refineries or to completely give up supplying the biomass Both discrete and continuous reliable location models are used to design a reliable bio-ethanol supply chain network for the State of Illinois, where (i) a significant amount of the nation’s biomass supply and bioethanol is produced; and (ii) the state has already geared up toward a rapid expansion of bio-ethanol production infrastructure.
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