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    Applications of robust optimization in logistics and production planning
    Avishan, Farzad; Yanıkoğlu, İhsan; Yanıkoğlu, İhsan; Özener, Okan Örsan; Özener, Başak Altan; Yakıcı, E.; Yavuz, T.; Department of Industrial Engineering
    We analyze three applications of the robust optimization approach in this thesis. The initial part is dedicated to planning a dairy production and distribution problem considering uncertain demand. The second part presents an adjustable robust optimization approach for relief distribution in a post-disaster scenario where travel times are uncertain. Lastly, in the third part, we tackle the electric bus scheduling problem that incorporates uncertainty in both travel times and energy consumption. The first part of this thesis investigates a robust dairy production and distribution planning problem that considers the complexities of dairy production, including perishability, sequence dependence, and demand uncertainty. To tackle this uncertainty, we introduce an adjustable robust optimization approach that generates a robust and Pareto-efficient production and distribution management plan. This approach provides decision-making flexibility by allowing for adjustments based on the actual demand observed over a multi-period planning horizon. The effectiveness of the proposed method is evaluated through extensive Monte Carlo simulation experiments. Additionally, we conduct a case study to demonstrate how the adjustable approach outperforms the static robust approach in terms of the objective function value and solution performance. In the second part, we present an adjustable robust optimization approach for relief supply distribution in the aftermath of a disaster. The approach generates routes for relief logistics teams and determines the service times for visited sites to distribute supplies, taking into account the uncertainty of travel times. The model allows for adjustments to service decisions based on real-time information, resulting in robust solutions for the worst-case scenario of travel times but also more flexible and less conservative than those of static robust optimization. Due to the computational complexity of solving resulting models, we propose heuristic algorithms as an alternative solution approach. Using 2011 earthquake data from the Van province of Turkey, we have also demonstrated the effectiveness of our approach. In the last part, we investigate a scheduling problem for electric fleets faced with uncertain travel times and energy consumption. We propose a mixed-integer linear programming model to optimize electric fleet purchasing and charging operation costs, utilizing robust optimization to address uncertainty. A new uncertainty set is introduced to control the robustness of the solution. The model determines the required number of buses to cover all trips, schedules the trips, and designs charging plans for the buses. We evaluate the effectiveness of the model through extensive Monte Carlo simulations. Additionally, we present a case study on the off-campus transport network at Binghamton University to demonstrate the real-world applicability of the model and solution approach.