Abstract
Purpose: The purpose of this paper is to propose a simulation-based robust biofuel facility location model for solving an integrated bio-energy logistics network (IBLN) problem, where biomass yield is often uncertain or difficult to determine. Design/methodology/approach: The IBLN considered in this paper consists of four different facilities: farm or harvest site (HS), collection facility (CF), biorefinery (BR), and blending station (BS). Authors propose a mixed integer quadratic modeling approach to simultaneously determine the optimal CF and BR locations and corresponding biomass and bio-energy transportation plans. The authors randomly generate biomass yield of each HS and find the optimal locations of CFs and BRs for each generated biomass yield, and select the robust locations of CFs and BRs to show the effects of biomass yield uncertainty on the optimality of CF and BR locations. Case studies using data from the State of South Carolina in the United State are conducted to demonstrate the developed model’s capability to better handle the impact of uncertainty of biomass yield. Findings: The results illustrate that the robust location model for BRs and CFs works very well in terms of the total logistics costs. The proposed model would help decision-makers find the most robust locations for biorefineries and collection facilities, which usually require huge investments, and would assist potential investors in identifying the least cost or important facilities to invest in the biomass and bio-energy industry. Originality/value: An optimal biofuel facility location model is formulated for the case of deterministic biomass yield. To improve the robustness of the model for cases with probabilistic biomass yield, the model is evaluated by a simulation approach using case studies. The proposed model and robustness concept would be a very useful tool that helps potential biofuel investors minimize their investment risk.
Highlights
Diverse and affordable energy is critical for the future of every country in the world
We assume shortage costs to be equal to zero, since the occurrence of biofuel shortage would not affect the optimal locations of BRs and collection facility (CF)
We solve the developed model for forty (40) different sets of simulated biomass yields for each probability distribution and present the frequencies of BR and CF to be included in the optimal solutions in Tables 2a through 2d. ‘1’ for BR location columns in these tables denotes that this location is selected in the optimal solution and ‘0’ otherwise
Summary
Diverse and affordable energy is critical for the future of every country in the world. The biomass transportation cost is, significant compared to the biofuel production cost. For this reason, a majority of existing biorefinery plants in the United States are located in the Midwest where biomass, such as corn and soybean, is abundant. With the soaring and unstable gasoline price and the increasing environmental concern, many other states in the U.S are seeking the opportunity to use biomass feedstocks, such as switchgrass, for producing biofuel. Under the Energy Independence and Security Act (EISA) of 2007, the United States Environmental Protection Agency (EPA) has developed a Renewable Fuel Standard Program (RFS) to ensure that gasoline in the U.S contains a minimum percentage of renewable fuel. The latest RFS (2011) “will increase the volume of renewable fuel required to be blended into gasoline from 9 billion gallons in 2008 to 36 billion gallons by 2022.” there is an immediate demand for biomass transportation cost analysis model to help locate new biorefineries optimally
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