(392u) Optimizing the Oleochemicals Supply Chain Under Emission Caps and Uncertainty

The global push for sustainable supply chains [1] has placed significant pressure on the consumer packaged goods industry to meet stringent emission targets while maintaining profitability. In particular, agricultural and refinery operations for the production of fatty esters, alcohols and acids, which are essential raw materials for products in sectors such as food, healthcare and cosmetics, among others, have a significant impact on total emissions. Traditional supply chains utilize external suppliers to procure the various raw materials. However, the desire to improve the reliability of the supply chain and reduce emissions has driven companies to directly work with farmers to obtain novel sustainable crops. Ιn this talk, we present a model to plan agricultural oils production and refinery operations in the oleochemicals industry, and we show how it can be used to handle various uncertainties in the production supply chain while maintaining emission goals.

Previous work in this area has considered subsets of the aforementioned issues. Hu et al. [2] formulated an MILP model to analyze economic and environmental benefits of recovery of biogas and fatty acids from organic waste. By pre-calculating costs, they were able to incorporate non-linearities in a linear model. Zhang and Jiang [3] and Ren et al. [4] optimized a biodiesel supply chain under uncertainty. However, their work only considered price uncertainty in a single stage without any recourse. Sharafi et al. [5] developed a two-stage multi-objective model that optimizes binary decisions in the first stage and continuous variables in the second stage. Despite the effectiveness of their approach, this methodology does not model and exercise the full flexibility of the network over long horizons due to the lack of multi-stage recourse.

Our model incorporates crop planting cycles, farmer incentivization, raw material procurement and plant operations in an LP model to find the optimal supply chain operation policy. In addition to profit maximization, we also enforce strict emission caps to encourage sustainable operations. Using this, we are able to analyze the Pareto front between profit and total emissions. To enhance decision-making under various sources of uncertainty, we formulate a multi-stage robust optimization [6] model incorporating linear decision rules and non-anticipativity constraints. This highlights the shortfalls of a purely deterministic model and how the latter can lead to suboptimal decisions that can either jeopardize profitability or fall short of sustainability targets. Thus, this approach allows for proactive risk mitigation and provides insights into optimal operational policies that differ from traditional deterministic solutions. Our results and analysis demonstrate the tradeoff between profitability, sustainability and risk mitigation. This work reveals how robust optimization can de-risk operations and improve long-term planning in complex, uncertain supply chains.

Bibliography

[1] B. Christian, M. Jan and F. Kai, "Managing Information Processing Needs in Global Supply Chains: A Prerequisite to Sustainable Supply Chain Management," Journal of Supply Chain Mangement, 2016.
[2] Y. Hu, M. Scarborough, H. Aguirre-Villegas, R. A. Larson, D. R. Noguera and V. M. Zavala, "A Supply Chain Framework for the Analysis of the Recovery of Biogas and Fatty Acids from Organic Waste," ACS Sustainable Chemistry & Engineering, 2018.
[3] Y. Zhang and Y. Jiang, "Robust optimization on sustainable biodiesel supply chain produced from waste cooking oil under price uncertainty," Waste Management, 2017.
[4] J. Ren, D. A. H. Liang, L. Dong, Z. Gao, Y. G. Q. Zhu, S. Song and W. Zhao, "Life cycle energy and CO2 emission optimization for biofuel supply chain planning under uncertainties," Energy, 2016.
[5] S. M. Zahraee, N. Shiwakoti and P. Stasinopoulos, "Agricultural biomass supply chain resilience: COVID-19 outbreak vs. sustainability compliance, technological change, uncertainties, and policies," Cleaner Logistics and Supply Chain, 2022.
[6] . Ben-Tal, . El Ghaoui and . Nemirovski, Robust Optimization, Princeton University Press, 2009.