A Flexible Robust Possibilistic Programming Approach for Sustainable Second-Generation Biogas Supply Chain Design under Multiple Uncertainties

Abstract

The goal of this research is to develop a novel second-generation-based biogas supply chain network design (BG-SCND) model that takes into account the triple bottom line approach. Biogas is a promising renewable energy source that can be obtained from a variety of easily accessible second-generation wastes, including animal manure, municipal waste, and agricultural leftovers. Integrated optimization of the biogas generation system is essential for a speedy and environmentally friendly transition to sustainable biodiesel production. The dynamic environment of the energy market significantly impairs the decisions of the BG-SCND model; therefore, a hybrid solution approach using flexible programming and possibilistic programming is suggested. To verify the suggested model and approach for solving the problem, a thorough computational analysis of a case study is conducted. The case study findings demonstrate that considerable investment is necessary to attain social and environmental well-being goals and safeguard decisions against epistemic uncertainty. Policymakers involved in the planning of biogas production and distribution projects may find the proposed approach useful.