Integrating environmental and social impacts into optimal design of guayule and guar supply chains. (March 2021)

• Record Type: Journal Article
• Authors: Zuniga Vazquez, Daniel A.; Sun, Ou; Fan, Neng; Sproul, Evan; Summers, Hailey M.; Quinn, Jason C.; Khanal, Sita; Gutierrez, Paul; Mealing, VeeAnder; Landis, Amy E.; Seavert, Clark; Teegerstrom, Trent; Evancho, Blase
• Is Part Of: Computers & chemical engineering. Volume 146(2021)
• Publication Date: 2021-03
• Subjects: Guayule -- Guar -- Facility location -- Multi-objective optimization -- Stochastic optimization -- Supply chain design; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2021.107223
• Languages: English
• ISSNs: 0098-1354

Abstract

Abstract: 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 CO 2 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.