Sustainable Biomass Supply Chain Optimization Under Uncertainty: Multi-Objective Scenario-Based Modeling Approach

 

1. Introduction

The transition toward renewable energy systems requires sustainable and resilient biomass supply chain networks capable of operating under uncertainty and disruption risks. This research introduces an integrated multi-period, multi-objective mathematical modeling framework that simultaneously optimizes economic, environmental, and social objectives. By incorporating scenario-based uncertainty analysis, the model enhances decision-making robustness while addressing long-term sustainability challenges in biomass energy infrastructure planning.

2. Multi-Objective Mathematical Modeling Framework

This topic explores the development of a comprehensive mathematical model that optimizes cost efficiency, carbon emissions, and social welfare simultaneously. Using multi-objective programming techniques, the study generates Pareto-optimal solutions that help decision-makers balance trade-offs between profitability, sustainability, and community development goals.

3. Scenario-Based Uncertainty and Risk Analysis

Uncertainty in biomass availability, market demand, and policy frameworks can significantly affect supply chain performance. This research incorporates scenario-based modeling and stochastic programming to simulate disruption risks such as natural disasters, supply shortages, and infrastructure failures, improving system resilience and long-term stability.

4. Integration of Social Sustainability Indicators

Beyond economic and environmental factors, the model integrates social indicators including job creation, regional economic growth, and equitable resource distribution. This approach ensures that biomass supply chains contribute positively to local communities and align with broader sustainable development objectives.

5. Multi-Period Network Design and Strategic Planning

The study emphasizes long-term planning by incorporating multi-period decision variables that allow infrastructure expansion, facility location planning, and capacity adjustment over time. This dynamic framework supports adaptive energy planning in response to evolving market and policy conditions.

6. Policy Implications and Future Renewable Energy Networks

The final topic discusses how integrated modeling approaches support evidence-based policymaking for renewable energy transitions. It highlights future research opportunities in hybrid renewable systems, digital twin simulation, AI-driven optimization, and sustainable bioenergy network innovation.


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