(511g) Multi-Objective Optimization of a Sustainable Bio-Based Isopropanol Supply Chain Under Uncertainty

Multi-objective optimization is a well-established approach in sustainable manufacturing, allowing decision-makers to balance competing objectives such as economic performance, environmental impact, and operational risk. Multi-criteria decision-making (MCDM), a common method for evaluating such trade-offs, seeks to identify the most favorable alternative by simultaneously considering multiple objectives. However, traditional MCDM typically relies on predefined criteria weights, often determined by stakeholders, which can limit flexibility and restrict the scope for fully exploring the decision-making space.

In this study, we aim to explore supply chain design and decision-making under uncertainty through a case study focused on a sustainable bio-based isopropanol (IPA) supply chain, utilizing sugar beet biomass as feedstock. The studied supply chain consists of multiple interconnected components, including beet-producing farms, sugar beet piling centers, sugar-processing plants, bio-IPA manufacturing facilities, and IPA retailers. The network operates under uncertainties such as fluctuating demands, supply disruptions, and environmental constraints.

Firstly, we propose a scenario tree-based stochastic optimization model to capture uncertainties in supply and demand, as variations in supply availability can disrupt production schedules, while fluctuations in demand significantly influence inventory levels. Additionally, the model applies different critical constraints, including environmental performance targets and penalties for unfulfilled demand, to ensure both economic viability and sustainability. Lastly, the ε-constraint method is applied to study the trade-off between maximum cost and greenhouse gas emissions while maintaining the required demand level. To compare supply chain performance under various targets, we employed MCDM techniques, particularly the Analytic Hierarchy Process. A detailed and systematic approach is presented to calculate key decision criteria, including economic costs, environmental impacts, supply chain robustness to disruptions, and potential risks (e.g., measures of uncertainty).

This work applies the coefficient of variation of each supply chain alternative’s performance to assign objective weights to the criteria. The weighting depends on the relative variability of the data, where a greater variation indicates higher sensitivity and thus results in a higher assigned weight. Based on the method, our results reveal that variability in production cost has a greater impact on decision outcomes than the average cost. Additionally, under strict environmental constraints, the supply chain’s ability to sustain performance amid disruptions (e.g., quantify by the similarity of supply chain configurations over time) becomes a more influential factor than its service level, defined as the ability to consistently meet demand. Finally, sustainable ternary graphs are proposed as a tool to enhance informed decision-making by exploring a diverse range of weight distributions that reflect varying stakeholder values.

[1] Jamwal, Anbesh, et al. "Review on multi-criteria decision analysis in sustainable manufacturing decision making." International Journal of Sustainable Engineering 14.3 (2021): 202-225.

[2] Pelissari, R., Khan, S. A., & Ben-Amor, S. (2022). Application of multi-criteria decision-making methods in sustainable manufacturing management: a systematic literature review and analysis of the prospects. International Journal of Information Technology & Decision Making, 21(02), 493-515.