Yazar "Cakmak, Emre" seçeneğine göre listele

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    DETERMINATION OF FLEET SIZE AND ASSIGNMENT OF TRAINS AND ROUTES BY MAXIMIZING THE NUMBER OF TRIPS
    (Univ Cincinnati Industrial Engineering, 2023) Cakmak, Emre; Yildiz, Feride Suheda; As, Yakup; Onden, Ismail
    To advance a globally sustainable mobility paradigm, nations are increasingly prioritizing environmentally responsible transportation modes. Within this context, railway systems have assumed paramount significance, encompassing infrastructure development and operational efficiency. The challenge arises in optimizing capacity utilization while minimizing investment costs, given the limitations of available resources. This paper introduces an ingenious integer optimization model aimed at maximizing the frequency of train trips. Simultaneously, it determines the ideal composition of high-speed and conventional train fleets through empirical data analysis. What sets this study apart is its comprehensive approach, encompassing not only trip frequency but also the intricate dynamics of mixed train types, route assignments, and the identification of bottlenecks within railway segments. The findings affirm the model's effectiveness in addressing contemporary global challenges, highlighting its practical applicability.
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    A Hybrid Metaheuristic Solution Method to Traveling Salesman Problem with Drone
    (Mdpi, 2023) Gunay-Sezer, Noyan Sebla; Cakmak, Emre; Bulkan, Serol
    The challenging idea of using drones in last-mile delivery systems of logistics addresses a new routing problem referred to as the traveling salesman problem with drone (TSP-D). TSP-D aims to construct a route to deliver parcels to a set of customers by either a truck or a drone, thereby minimizing operational costs. Since TSP-D is considered NP-hard, using metaheuristics is one of the most promising solutions. This paper presents a hybrid metaheuristic solution method of TSP-D based on two state-of-the-art algorithms: the genetic algorithm and ant colony optimization algorithm. Heuristics in TSP-D literature are based on two consequent decisions: truck routing and drone assignment. Unlike those in the existing literature, the proposed metaheuristic constructs both truck and drone routes simultaneously. Additionally, to the best of our knowledge, we introduce for the first time a solution method on the basis of an ant colony optimization approach to TSP-D. Additionally, we propose a binary pheromone framework for both drone and truck, diverging from the traditional pheromone structure. Computational experiments indicate that the proposed hybrid metaheuristic algorithm is able to generate optimal routes for provided instances of TSP-D benchmarking. In addition, the algorithm improves the best-known solutions of some instances found by rival heuristics.
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    A proposed emergency response site selection for preventing pollution and collision effects using neutrosophic evaluation based on distance from average solution method and a numerical application in the Strait of Canakkale (Dardanelles)
    (Pergamon-Elsevier Science Ltd, 2023) Usluer, H. Bora; Bora, Ali Gokhan; Cakmak, Emre; Arslan, Aykut
    The Turkish Straits Sea Area, encompassing the Sea of Marmara, the Strait of Canakkale, and the Strait of Istanbul, presents exceptional challenges due to its constricting and winding shape, making it one of the world's most difficult, busy, and hazardous waterways. The surge in marine traffic density in this vital waterway has raised concerns about collision risks. This study addresses two main problems: evaluating the adequacy of current Search and Rescue station locations for swift emergency response and identifying potential improved locations, if necessary. The research adopts a novel approach by utilizing neutrosophic data sets, a recently developed statistical method suitable for complex or indeterminate environments. This contributes to the advancement of alternative and contemporary techniques that enhance optimized decision-making. Notably, this study is the first to focus on the location and prioritization of emergency response points in the canakkale region, offering unique insights and original contributions to the field.
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    Spare parts inventory classification using Neutrosophic Fuzzy EDAS method in the aviation industry
    (Pergamon-Elsevier Science Ltd, 2023) Cakmak, Emre; Guney, Eda
    Every physical asset and product that contributes to production or service is called inventory in the production or service industry. Inventories counted among companies' assets are important since they protect companies from various undesirable situations. Inventories often include large numbers, although they vary from company to company. Changes in the market affect the customer's procurement behavior. Inventories shaped by customers' demands are therefore seen as a source of uncertainty and cost for companies. The aviation industry is one of the most important transportation modes in the transportation industry and has the ability to transport faster than other industries. One of the vital issues in aviation businesses is the uninterrupted continuation of services. Accordingly, spare parts management is one of the issues that aviation companies attach importance to. Generally, spare parts management in most aviation companies aims to achieve a high customer service level with minimum inventory and minimum inventory investment. Aviation companies can achieve this goal through inventory classification. By using well-prepared inventory classification, these companies can reduce their inventory costs and classify their inventory to increase customer satisfaction and efficient production. This study aims to classify spare parts inventories by using the Neutrosophic Fuzzy EDAS method to achieve high inventory management efficiency in cases of inconsistent and uncertain information in the aviation industry. By adopting this recent multi-criteria decision-making method (MCDM), this study not only provides high inventory management efficiency but also determines required spare parts classification criteria for the aviation industry.
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    Supplier Selection for a Power Generator Sustainable Supplier Park: Interval-Valued Neutrosophic SWARA and EDAS Application
    (Mdpi, 2023) Cakmak, Emre
    Power generator manufacturers play a critical role in maintaining electric flow for sustainable product and service production. The aim of this study is to extract the criteria necessary for a generator manufacturer to evaluate and select its suppliers for its sustainable supplier park, and to prioritize them to form the supply network. The methodology of this research covers the phases as (i) extracting the criteria affecting the supplier selection decision process of a power generator company via an in-depth literature and industrial report review, (ii) evaluating these criteria by industry experts, (iii) identifying the weights of each criterion via SWARA (step-wise weight assessment ratio analysis), (iv) prioritizing the alternative suppliers fitting to the criteria so that the power generator company can construct its sustainable supplier park via IVN EDAS (interval valued neutrosophic Evaluation Based on Distance from Average Solution), (v) conducting a sensitivity analysis to check for the robustness of the results by changing the weights, and (vi) applying a comparative analysis to validate the methodology's accuracy by comparing the results with IVN TOPSIS and IVN CODAS. Moreover, this paper contributes to the literature by elaborating on the integration details of the IVN SWARA and IVN EDAS as the first research paper of the author' knowledge. A practitioner can understand which factors to consider prominently in forming a sustainable supplier park, or in deciding on which suppliers to select to plan the strategic operations of a power generator company.
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    Traveling salesman problem with drone and bicycle: multimodal last-mile e-mobility
    (Wiley, 2024) Tirkolaee, Erfan Babaee; Cakmak, Emre; Karadayi-Usta, Saliha
    Recently, the multimodal last-mile e-mobility concept has been at the center of attention for cleaner, greener, and more accessible urban deliveries. As part of sustainable transportation systems, multimodal e-mobility is proper for a variety of logistics operations as well as medical applications. This work tries to address a novel application of multimodal e-mobility through introducing and modeling the traveling salesman problem with drone and bicycle (TSP-D-B). Therefore, a novel mixed integer linear programming model is developed to formulate the problem wherein the total traveling time is minimized. As part of the last-mile delivery, a fleet of three vehicles including a truck, a drone, and a bicycle is taken into account to serve the customers in a single visit. The truck is considered as the main vehicle, while the drone and bicycle can be preferred in case of emergencies such as traffic or route failures. In order to assess the complexity, validity and applicability of the offered model, a dataset including 64 different benchmarks is generated, and according to the findings, the model is able to efficiently solve the benchmarks for up to 50 customers in 685 s maximum. A comparison is also made between TSP-D-B, the classic version of the TSP and the TSP-D, which reveals that TSP-D-B provides appropriate service time savings in all benchmarks. Finally, another comparative analysis is made using several instances adapted from the literature. It is revealed that TSP-D-B leads to significant time savings in most instances.