A robust possibilistic flexible programming approach toward a resilient and cost-efficient biodiesel supply chain network

Increasing energy demand and the fast depletion of fossil fuels have prompted the quest for sustainable energy sources. Biodiesel is a potential fossil fuel replacement that can be used in engines without modification. However, the commercial feasibility of biodiesel production is a major challenge. A resilient and cost-efficient biodiesel supply chain network is essential for commercialization. In addition, disruption risks arising from operational downtime, labor strikes, natural disasters, and uncertainty embedded in the data compromise the effectiveness of tactical and strategic level supply chain planning. In line with these requirements, an animal fatbased biodiesel supply chain model that reduces the total system cost and accounts for both disruption and operational risks is proposed. The proposed model determines the optimal production-distribution quantities and supports facility location and capacity decisions against multiple supply and demand interruption scenarios. A novel interactive solution technique, robust possibilistic flexible programming, which enables decision-makers to incorporate flexibility into model constraints, has been introduced. Furthermore, a p-measure constraint that ensures the lowest cost under disruption scenarios is used to control network reliability. A real-world case study is used to assess the suggested model and solution technique's applicability. The findings demonstrate a tradeoff between system reliability and nominal cost, showing that with a marginal increase in overall cost, the decisions can be secured against an uncertain environment. Biodiesel producers and distributors, as well as investors and regulators, may potentially benefit from the proposed model.

Automated summary

This study proposes a resilient and cost-efficient biodiesel supply chain model that takes into account disruption and operational risks, and introduces an interactive solution technique that enables optimal decision-making under uncertain environments.