TY - JOUR
T1 - Integrating environmental and social impacts into optimal design of guayule and guar supply chains
AU - Zuniga Vazquez, Daniel A.
AU - Sun, Ou
AU - Fan, Neng
AU - Sproul, Evan
AU - Summers, Hailey M.
AU - Quinn, Jason C.
AU - Khanal, Sita
AU - Gutierrez, Paul
AU - Mealing, Vee Ander
AU - Landis, Amy E.
AU - Seavert, Clark
AU - Teegerstrom, Trent
AU - Evancho, Blase
N1 - Funding Information:
This material is based upon funding provided by the USDA-NIFA, Grant # 2017-68005-26867. Any opinions, findings, conclusions, or recommendations expressed in this publication/work are those of the authors and do not necessarily reflect the view of the U.S. Department of Agriculture. All authors state that there is no conflict of interest. D. A. Zuniga Vazquez is also supported by the Mexican National Council of Science and Technology (CONACYT) and the Mexican Department of Energy (SENER) for his Ph.D. program.
Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/3
Y1 - 2021/3
N2 - Guayule and guar are two desert-dwelling crops that can provide raw materials year-round for bioproducts such as rubber, resin, guar gum, and guar meal. Both crops are low-water-use, drought-tolerant, as well as heat-resistant, and these features enable their great potential for the agricultural economy in the Southwestern U.S. However, there exist challenges when considering the design of their supply chains in not only the economic benefits but also the environmental and social impacts, such as the process facility location and transportation problems. Furthermore, the optimal supply chains are heavily dependent on the amount of crop production, which can be measured by the adoption rate, i.e., the percentage of current crops in the field that is switched to either guayule or guar. In this paper, stochastic scenarios are utilized to capture the uncertainties of the adoption rates of each field. Afterward, a stochastic optimization is deployed to identify optimal decisions for facility locations, transportations from fields to facilities, and finally to customers, with a multi-objective approach to quantify the economic benefits (minimizing the costs of supply chains), environmental impacts (minimizing CO2 equivalent greenhouse gas emissions), and social impacts (maximizing the local accrued jobs). Based on the Geographic Information System for capturing field information and relevant factors, and deciding facility locations, the model is formulated as a complex large-scale mixed-integer linear optimization problem. For an efficient solution, the Benders Decomposition algorithm is implemented. The proposed approaches are evaluated based on the cases of two areas: Maricopa and Pinal counties in Arizona for the guayule supply chain, and Dona Ana County in New Mexico for the guar supply chain.
AB - Guayule and guar are two desert-dwelling crops that can provide raw materials year-round for bioproducts such as rubber, resin, guar gum, and guar meal. Both crops are low-water-use, drought-tolerant, as well as heat-resistant, and these features enable their great potential for the agricultural economy in the Southwestern U.S. However, there exist challenges when considering the design of their supply chains in not only the economic benefits but also the environmental and social impacts, such as the process facility location and transportation problems. Furthermore, the optimal supply chains are heavily dependent on the amount of crop production, which can be measured by the adoption rate, i.e., the percentage of current crops in the field that is switched to either guayule or guar. In this paper, stochastic scenarios are utilized to capture the uncertainties of the adoption rates of each field. Afterward, a stochastic optimization is deployed to identify optimal decisions for facility locations, transportations from fields to facilities, and finally to customers, with a multi-objective approach to quantify the economic benefits (minimizing the costs of supply chains), environmental impacts (minimizing CO2 equivalent greenhouse gas emissions), and social impacts (maximizing the local accrued jobs). Based on the Geographic Information System for capturing field information and relevant factors, and deciding facility locations, the model is formulated as a complex large-scale mixed-integer linear optimization problem. For an efficient solution, the Benders Decomposition algorithm is implemented. The proposed approaches are evaluated based on the cases of two areas: Maricopa and Pinal counties in Arizona for the guayule supply chain, and Dona Ana County in New Mexico for the guar supply chain.
KW - Facility location
KW - Guar
KW - Guayule
KW - Multi-objective optimization
KW - Stochastic optimization
KW - Supply chain design
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U2 - 10.1016/j.compchemeng.2021.107223
DO - 10.1016/j.compchemeng.2021.107223
M3 - Article
AN - SCOPUS:85100085287
SN - 0098-1354
VL - 146
JO - Computers and Chemical Engineering
JF - Computers and Chemical Engineering
M1 - 107223
ER -