Graduate School of Engineering and Science

Permanent URI for this collectionhttps://hdl.handle.net/10679/9877

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Browsing by Author "Elyasi, Milad"

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    PhD DissertationPublicationMetadata only
    Application of large-scale optimization methods in scheduling and routing problems
    Elyasi, Milad; Özener, Okan Örsan; Özener, Okan Örsan; Yanıkoğlu, İhsan; Ekici, Ali; Yakıcı, E.; Duran, S.; Department of Industrial Engineering; Elyasi, Milad
    In this thesis, we consider three different applications of large-scale optimization methods. We focus on the blood donation tailoring problem under uncertain demand in the first problem. In the second one, we propose a model for hybrid manufacturing consisting of flexible manufacturing systems and typical manufacturing machines. In the last one, we consider a two-echelon vehicle routing problem for last-mile delivery of groceries. In the first part of the thesis, we propose a stochastic scenario-based reformulation of the blood donation management problem that adopts multicomponent apheresis and utilizes donor pool segmentation as here-and-now and wait-and-see donors. The donation pool segmentation enables more flexible donation schedules than the orthodox donation approach because wait-and-see donors may adjust their donation schedules according to the realized values of demand over time. We propose a column generation approach to solve the associated multi-stage stochastic donation tailoring problem for realistically sized instances. The second part considers a flexible/hybrid manufacturing production setting with typically dedicated machinery to satisfy regular demand and a flexible manufacturing system to handle surged demand. We model the uncertainty in demand using a scenario-based approach and allow the business to make here-and-now and wait-and-see decisions exploiting the cost-effectiveness of the standard production and responsiveness of the flexible manufacturing systems. We propose a branch-and-price algorithm as the solution approach. Our computational analysis shows that this hybrid production setting provides highly robust response to the uncertainty in demand, even with high fluctuations. In the third part, we propose a \textit{two-echelon vehicle routing problem} (2E-VRP) under consideration of a heterogeneous fleet of vehicles and different customer types. In our model, unlike the previous studies in the literature, not only do the large vehicles visit the pre-assigned points, called satellites, to refill the smaller vehicles, but they also deliver items to the customers. On the other hand, smaller vehicles are responsible for the customers with small size demands and can get refilled whether at the depots or satellites. We propose a branch-and-price algorithm as the solution approach and obtain promising results in comprehensive numerical studies that prove its versatility.