Yazar "Yildirim, U. Mahir" seçeneğine göre listele

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    An Enhanced Network-Consistent Travel Speed Generation Scheme on Time-Dependent Shortest Path and Routing Problems
    (IEEE-Inst Electrical Electronics Engineers Inc, 2022) Yildirim, U. Mahir; Catay, Bulent
    The time-dependent shortest path and vehicle routing literature depends on realistic and reasonable test data for demonstration and performance evaluation. Despite the advancements in GPS and tracking technologies there is still lack and inaccessibility of publicly available real-world road networks with time-dependent arc costs and speeds. Since most of the time-dependent travel time layer generation models proposed for vehicle routing problems (VRPs) are mainly developed for synthetic networks, they cannot capture some realistic features of the real road networks and cannot be used in time-dependent shortest path problems (TDSPPs). In this paper, we first exploit spatial and temporal behavior of travel times using real life road network and speed data, and discuss the cases where the existing methods in the literature are not applicable. Then, we propose an enhanced method that is best fitted for TDSPP and time-dependent VRP (TDVRP). The proposed method can be implemented on both synthetic and real road networks. Finally, we apply our method to generate realistic speed data on Istanbul road network and demonstrate the applicability in TDSPP and TDVRP.
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    Multi-objective 3D bin packing problem with load balance and product family concerns
    (Pergamon-Elsevier Science Ltd, 2021) Erbayrak, Seda; Ozkir, Vildan; Yildirim, U. Mahir
    The classical three-dimensional bin packing problem (3D-BPP) orthogonally packs a set of rectangular items with varying dimensions into the minimum number of three-dimensional rectangular bins. While ensuring the minimum number of bins used, the safety of the logistic operations is addressed with the complementary loadbalancing objective for which concepts such as orientation and stability are used in the literature though not at the same time. In this study, we extend the load-balanced 3D-BPP by combining both orientation and stability, and introducing a new concept called family unity which encourages packing a family of products (e.g., from the same order and with the same destination) together. Although item related concerns are very common in practice, there are no multi-objective studies in the bin packing literature that includes family unity concept. Therefore, this is the first study that proposes a multi-objective mixed integer programming model for the extended problem to determine the optimal packing plan that minimizes the number of bins used and the deviation of balance from the ideal barycenter while maximizing family unity ratio via a weighted objective function. A numerical example is provided to analyze the performance of the proposed model. Furthermore, a real-life container loading problem is solved and outputs of the study implies the practical advantages of including family unity and load-balance considerations in solving 3D-BPP problems.
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    Painter Profile Clustering Using NLP Features
    (Science and Technology Publications, Lda, 2024) Ilba, N. Yagmur; Yildirim, U. Mahir; Sen, Doruk
    This study introduces a practice for clustering painter profiles using features obtained from natural language processing (NLP) techniques. The investigation of similarities among painters plays an essential function in art history. While most existing research generally focuses on the visual comparison of the artists' work, more studies should examine the textual content available for artists. As the volume of online textual information grows, the frequency of discussions about artists and their creations has gained importance, underscoring the connection between social visibility through digital discourse and an artist's recognition. This research provides a method for investigating Wikipedia profiles of painters using NLP attributes. Among unsupervised machine learning algorithms, the K-means is adopted to group the painters using the driven attributes from the content details of their profile pages. The clustering results are evaluated through a benchmark painter list and a qualitative review. The model findings reveal that the suggested approach effectively clusters the presented benchmark painter profiles, highlighting the potential of textual data analysis on painter profile similarities. Copyright © 2024 by SCITEPRESS - Science and Technology Publications, Lda.
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    User-based relocation strategy for free floating car-sharing system: An Istanbul case
    (Pamukkale Univ, 2024) Simsir, Misra; Yildirim, U. Mahir; Sen, Doruk
    In a world with limited resources, it is crucial for individuals to utilise shared systems and develop strategies to optimise their usage. To cope with this, 'servicizing' has emerged as a rapidly growing promising solution, especially in car-sharing systems. These systems can be split into two: station-based and free-floating. The latter introduces more flexibility to the customers as free-floating systems allow users to pick up and drop off vehicles anywhere within predetermined operational zones. This flexibility may come with an additional cost by bringing a potential imbalance between demand and supply. This imbalance can harm the company's profitability and customer satisfaction. In this study, the imbalance problem of the system of free-floating car sharing is considered. A mixed integer linear programming model is developed and tested with real data for free floating car sharing systems to solve this problem. The proposed system consists of four modules: clustering, forecasting, optimization model, and relocation strategy. According to the results, it is observed that the system is more balanced with satisfying 9% more demand and more profitable with earning 6% more. The study was conducted on a car-sharing company that is based in Istanbul, but the results can be applied to any free-floating car-sharing system. This ensures customer satisfaction by meeting demand and balancing the system.