In today’s rapidly evolving global landscape, supply chains must navigate a host of challenges—including demand variability, sustainability regulations, geopolitical shifts, and technological disruption. The ability to model and optimize operations under uncertainty is becoming essential for maintaining efficiency, meeting environmental goals, and staying competitive. This poster presents a portfolio of four research projects that apply advanced optimization techniques to address real-world problems across diverse sectors. These projects combine mathematical modeling, robust optimization, and meta-heuristics to develop scalable solutions for complex, uncertain environments.
Hydrogen Infrastructure Planning: We design a robust MILP model to support investment planning in hydrogen production, storage, and transportation. The model captures uncertain demand growth and transitions among hydrogen types (gray, blue, green), offering a strategic tool for decarbonization-focused infrastructure development.
Sustainable Oleochemicals Supply Chain: This project models upstream agricultural and downstream refinery operations in the oleochemicals industry under strict emission caps. Through a multi-stage robust optimization framework, we capture uncertainty in crop yields, prices, and demand, enabling emissions-compliant, cost-effective supply chain design.
Vendor-Managed Inventory in Mixed Networks: We develop a practical solution to generate optimal delivery coordination strategies and convert them into periodic replenishment plans for networks with both VMIC and CMIC customers. A sampling-based methodology is used to identify clusters and their optimal coordination. Then, an MILP model aligns delivery frequencies and fleet constraints to reduce logistics costs without compromising service levels.
Genetic Algorithm for Batch Plant Scheduling: We present a GA-based meta-heuristic for scheduling in MPMP batch plants with variable batch sizes. The algorithm dynamically adjusts population size and search behavior, solving large-scale scheduling problems that are intractable using exact optimization methods.
Together, these projects demonstrate how modern optimization methods can be leveraged to de-risk decision-making, improve long-term planning, and enhance operational resilience in uncertain and dynamic supply chain environments. The techniques showcased are directly applicable to challenges in energy, chemicals, logistics, and manufacturing—highlighting how academic research can generate practical value for industry.