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    A binarization strategy for modelling mixed data in multigroup classification
    (Institute of Electrical and Electronics Engineers (IEEE), 2013) Masmoudi, Youssef; Chabchoub, Habib; Department of Industrial Engineering; Türkay, Metin; Faculty Member; Department of Industrial Engineering; College of Engineering; 24956
    This paper presents a binarization pre-processing strategy for mixed datasets. We propose that the use of binary attributes for representing nominal and integer data is beneficial for classification accuracy. We also describe a procedure to convert integer and nominal data into binary attributes. Expectation-Maximization (EM) clustering algorithms was applied to classify the values of the attributes with a wide range to use a small number of binary attributes. Once the data set is pre-processed, we use the Support Vector Machine (LibSVM) for classification. The proposed method was tested on datasets from the literature. We demonstrate the improved accuracy and efficiency of presented binarization strategy for modelling mixed and complex data in comparison to the classification of the original dataset, nominal dataset and binary dataset.
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    A fundamental experimental approach for optimal design of speed bumps
    (Elsevier, 2018) Bilgin, Ertuğrul; Lav, Abdullah Hilmi; N/A; Lav, Ahmet Hakan; Undergraduate student; School of Medicine
    Speed bumps and humps are utilized as means of calming traffic and controlling vehicular speed. Needless to say, bumps and humps of large dimensions in length and width force drivers to significantly reduce their driving speeds so as to avoid significant vehicle vertical acceleration. It is thus that this experimental study was conducted with the aim of determining a speed bump design that performs optimally when leading drivers to reduce the speed of their vehicles to safe levels. The first step of the investigation starts off by considering the following question: "What is the optimal design of a speed bump that will - at the same time - reduce the velocity of an incoming vehicle significantly and to a speed that resulting vertical acceleration does not jeopardize road safety? The experiment has been designed to study the dependent variables and collect data in order to propose an optimal design for a speed bump. To achieve this, a scaled model of 1:6 to real life was created to simulate the interaction between a car wheel and a speed bump. During the course of the experiment, a wheel was accelerated down an inclined plane onto a horizontal plane of motion where it was allowed to collide with a speed bump. The speed of the wheel and the vertical acceleration at the speed bump were captured by means of a Vernier Motion Detector.
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    An exact algorithm for integrated planning of operations in dry bulk terminals
    (Pergamon-Elsevier Science Ltd, 2019) N/A; Department of Industrial Engineering; Ünsal, Özgür; Oğuz, Ceyda; PhD Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; 328856; 6033
    We consider integrated planning problem of export dry bulk terminals. This problem consists of three important operations: (i) berth allocation, (ii) reclaimer scheduling, and (iii) stockyard allocation, and includes tidal time windows, multiple stocking pads and non-crossing of reclaimers. We exploit relationships among these operations to decompose this complex problem and propose a logic-based Benders decomposition algorithm. Master and subproblems are modeled with mixed-integer programming and constraint programming, respectively, such that complementary strengths of these programming paradigms are utilized. Computational experiments show that the proposed method can effectively solve the integrated problem for up to two weeks of planning horizon.
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    An online optimization approach to post-disaster road restoration
    (Pergamon-Elsevier Science Ltd, 2021) Akbari, Vahid; Shiri, Davood; Department of Industrial Engineering; Salman, Fatma Sibel; Faculty Member; Department of Industrial Engineering; College of Engineering; 178838
    Natural disasters impact transportation networks adversely and cause road sections to be damaged or blocked. The road network may even become disconnected, impeding accessibility between disaster-stricken areas and critical locations such as hospitals, relief aid depots and transportation hubs. In the immediate response phase, a set of blocked edges should be selected and restored to reconnect the transportation network. While locations of the disrupted roads can be identified using drone or satellite images, an accurate estimation of time to restore a road segment can be carried out only after expert observations on the field. In this article, we study a post-disaster road restoration problem modeled on an undirected edge-weighted graph with k blocked edges, where the unblocking time of a blocked edge is revealed online once the road restoration team visits an end-node of that blocked edge. The objective is to minimize the time at which the road network is reconnected. We first investigate the worst-case performance of online algorithms against offline optimal solutions by means of the competitive ratio. We prove that any online deterministic algorithm cannot achieve a competitive ratio better than 2k-1. We also provide an optimal online algorithm that is proven to achieve this lower bound. In addition, to achieve good performance on realistic instances, we implement an algorithm that solves a mixed integer programming model each time new information is revealed. Since model solution is prohibitively time-consuming, we also propose a novel polynomial time online algorithm. We compare these two algorithms with two other benchmark online algorithms on both Istanbul road network instances and several other city instances from the literature. Our experiments show that the proposed polynomial time online algorithm performs superior to the benchmark ones and obtains solutions close to the offline optimum on all the tested instances.
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    Arc routing problems to restore connectivity of a road network
    (Pergamon-Elsevier Science Ltd, 2016) Kasaei, Maziar; Department of Industrial Engineering; Salman, Fatma Sibel; Faculty Member; Department of Industrial Engineering; College of Engineering; 178838
    After a disaster, restoring accessibility in the affected area is critical for response operations. We study two arc routing problems for clearing blocked roads. The first problem minimizes the time to reconnect the road network, while the second maximizes the total benefit gained by reconnecting network components within a time limit. For each problem, we develop a mixed integer programming formulation and two versions of a heuristic algorithm. We conduct computational experiments on Istanbul data and instances adapted from the literature. The heuristics achieve near-optimal or optimal solutions quickly in most of the tested instances.
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    Constraint programming approach to quay crane scheduling problem
    (Pergamon-Elsevier Science Ltd, 2013) N/A; N/A; Department of Industrial Engineering; Ünsal, Özgür; Oğuz, Ceyda; PhD Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; 328856; 6033
    This study presents a constraint programming (CP) model for the quay crane scheduling problem (QCSP), which occurs at container terminals, with realistic constraints such as safety margins, travel times and precedence relations. Next, QCSP with time windows and integrated crane assignment and scheduling problem, are discussed. The performance of the CP model is compared with that of algorithms presented in QCSP literature. The results of the computational experiments indicate that the CP model is able to produce good results while reducing the computational time, and is a robust and flexible alternative for different types of crane scheduling problems.
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    Data-driven abnormal behavior detection for autonomous platoon
    (IEEE Computer Society, 2018) N/A; Department of Electrical and Electronics Engineering; Department of Computer Engineering; Uçar, Seyhan; Ergen, Sinem Çöleri; Özkasap, Öznur; PhD Student; Faculty Member; Faculty Member; Department of Electrical and Electronics Engineering; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; 7211; 113507
    Autonomous platoon is a technique where co-operative adaptive cruise control (CACC) enabled vehicles are organized into groups of close following vehicles through communication. It is envisioned that with the increased demand for autonomous vehicles, platoons would be a part of our life in near future. Although many efforts have been devoted to implement the vehicle platooning, ensuring the security remains challenging. Due to lack of security, platoons would be subject to modified packets which can mislead the platoon and result in platoon instability. Therefore, identifying malicious vehicles has become an important requirement. In this paper, we investigate a data-driven abnormal behavior detection approach for an autonomous platoon. We propose a novel statistical learning based technique to detect data anomalies. We demonstrate that shared speed value among platoon members would be sufficient to detect the misbehaving vehicles.
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    Design and operation of intermodal transportation network in the Marmara region of Turkey
    (Pergamon-Elsevier Science Ltd, 2015) N/A; N/A; Department of Industrial Engineering; Reşat, Hamdi Giray; Türkay, Metin; PhD Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 24956
    This paper presents a multi-objective optimization model for integrating different transportation modes in the design and operation of an intermodal transportation network in a geographical region. The problem is formulated as a mixed-integer optimization problem that accounts for time and congestion dependent vehicle speeds. We present modeling approach, data analysis and outline the important characteristics of the mathematical programming problem for minimization of transportation cost and time simultaneously by using the augmented epsilon-constraint method. The proposed approach is illustrated on a real world case using data from Marmara region where approximately 50% industrial goods and services in Turkey are produced.
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    Dual channel visible light communications for enhanced vehicular connectivity
    (IEEE Computer Society, 2016) N/A; N/A; Department of Electrical and Electronics Engineering; Department of Computer Engineering; Turan, Buğra; Uçar, Seyhan; Ergen, Sinem Çöleri; Özkasap, Öznur; PhD Student; PhD Student; Faculty Member; Faculty Member; Department of Electrical and Electronics Engineering; Department of Computer Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; N/A; 7211; 113507
    Visible Light Communication (VLC) has recently been proposed as a low-cost and low-complexity technology for vehicular communications. In this paper, we propose the usage of dual channel VLC with the goal of providing enhanced vehicular connectivity to disseminate safety-critical messages and perform an experimental study to determine the spatial and angular limits of an off-the-shelf automotive Light Emitting Diode (LED) fog light. Single channel VLC refers to the independent transmission of different data packets from each LED fog light, while the dual channel VLC offers the concurrent transmission of the same data packet from both lights. There is a trade-off between increasing the angular limitation and the performance of dual channel VLC, which needs to be experimentally evaluated to identify its efficient usage. We first show the dependency of the received optical power of single channel VLC on the angle and distance, and demonstrate that Lambertian model does not represent the automotive LED fog light radiation pattern accurately. We then demonstrate that dual channel usage increases the angular limitation by up to 10° compared to the single channel VLC. We also show that dual channel improves the packet delivery error rate performance at only short distances due to the photodiode (PD) saturation led by light intensity overlapping at higher distances.
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    Hub network design problem with capacity, congestion, and stochastic demand considerations
    (Informs, 2023) Bayram, Vedat; Farham, M. Saleh; Department of Industrial Engineering; Yıldız, Barış; Department of Industrial Engineering; College of Engineering
    Our study introduces the hub network design problem with congestion, capacity, and stochastic demand considerations (HNDC), which generalizes the classical hub location problem in several directions. In particular, we extend state-of-the-art by integrating capacity acquisition decisions and congestion cost effect into the problem and allowing dynamic routing for origin-destination (OD) pairs. Connecting strategic and operational level decisions, HNDC jointly decides hub locations and capacity acquisitions by considering the expected routing and congestion costs. A path-based mixed-integer second-order cone programming (SOCP) formulation of the HNDC is proposed. We exploit SOCP duality results and propose an exact algorithm based on Benders decomposition and column generation to solve this challenging problem. We use a specific characterization of the capacity-feasible solutions to speed up the solution procedure and develop an efficient branch-and-cut algorithm to solve the master problem. We conduct extensive computational experiments to test the proposed approach's performance and derive managerial insights based on realistic problem instances adapted from the literature. In particular, we found that including hub congestion costs, accounting for the uncertainty in demand, and whether the underlying network is complete or incomplete have a significant impact on hub network design and the resulting performance of the system.