Mühendislik Fakültesi / Faculty of Engineering

Permanent URI for this collectionhttps://hdl.handle.net/11727/1401

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Author: search.filters.author.Dengiz, Bernasearch.filters.applied.f.publicationcategory: search.filters.publicationcategory.Makale - Uluslararası Hakemli Dergi

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Now showing 1 - 10 of 27
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    Women in Engineering in Turkey - A Large Scale Quantitative and Qualitative Examination
    (2010) Smith, Alice; Dengiz, Berna; 0000-0001-8808-0663; AAK-2318-2021
    The underrepresentation of women in engineering is well known and unresolved. However, Turkey has witnessed a shift in trend from virtually no female participation in engineering to across-the-board proportions that dominate other industrialised countries within the 76 years of the founding of the Turkish Republic. This paper describes the largest known direct cross-sectional study of women in engineering in Turkey with over 800 participants. The methods include survey and facilitated focus groups. The study shows that women in Turkey choose engineering mainly because they enjoy the underlying mathematics and science. There is no gender bias on the part of teachers or fellow students; however, women students believe that they have fewer opportunities than male peers and acutely feel the lack of role models. Working professionals in industry or government perceive that women assume a more indirect, supporting role; however, women overall strongly affirm their selection of engineering despite some negative factors.
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    The Location-Routing Problem with Simultaneous Pickup and Delivery: Formulations and A Heuristic Approach
    (2012) Karaoglan, Ismail; Altiparmak, Fulya; Kara, Imdat; Dengiz, Berna; 0000-0002-6023-6918; 0000-0003-1730-4214; AAG-4982-2019; AAF-7020-2021; ABH-1078-2021
    In this paper, we consider a variant of the Location-Routing Problem (LRP), namely the LRP with simultaneous pickup and delivery (LRPSPD). The LRPSPD seeks to minimize total cost by simultaneously locating the depots and designing the vehicle routes that satisfy pickup and delivery demand of each customer at the same time. We propose two polynomial-size mixed integer linear programming formulations for the problem and a family of valid inequalities to strengthen the formulations. While the first formulation is a node-based formulation, the second one is a flow-based formulation. Furthermore, we propose a two-phase heuristic approach based on simulated annealing, tp_SA, to solve the large-size LRPSPD and two initialization heuristics to generate an initial solution for the tp_SA. We then empirically evaluate the strengths of the proposed formulations with respect to their ability to find optimal solutions or strong lower bounds, and investigate the performance of the proposed heuristic approach. Computational results show that the flow-based formulation performs better than the node-based formulation in terms of the solution quality and the computation time on small-size problems. However, the node-based formulation can yield competitive lower bounds in a reasonable amount of time on medium-size problems. Meantime, the proposed heuristic approach is computationally efficient in finding good quality solutions for the LRPSPD. (C) 2011 Elsevier Ltd. All rights reserved.
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    The Efficiency of Variance Reduction in Manufacturing and Service Systems: The Comparison of the Control Variates and Stratified Sampling
    (2009) Eraslan, Erguen; Dengiz, Berna; 0000-0002-5667-0391; AAE-7165-2019
    There has been a great interest in the use of variance reduction techniques (VRTs) in simulation output analysis for the purpose of improving accuracy when the performance measurements of complex production and service systems are estimated. Therefore, a simulation output analysis to improve the accuracy and reliability of the output is required. The performance measurements are required to have a narrow and strong confidence interval. For a given confidence level, a smaller confidence interval is supposed to be better than the larger one. The wide of confidence interval, determined by the half length, will depend on the variance. Generally, increased replication of the simulation model appears to have been the easiest way to reduce variance but this increases the simulation costs in complex-structured and large-sized manufacturing and service systems. Thus, VRTs are used in experiments to avoid computational cost of decision-making processes for more precise results. In this study, the effect of Control Variates (CVs) and Stratified Sampling (SS) techniques in reducing variance of the performance measurements of M/M/1 and GI/G/1 queue models is investigated considering four probability distributions utilizing randomly generated parameters for arrival and service processes. Copyright (C) 2009 E. Eraslan and B. Dengiz.
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    Simulation Optimization of A Multi-Stage Multi-Product Paint Shop Line with Response Surface Methodology
    (2014) Dengiz, Berna; Belgin, Onder; 0000-0001-6702-2608; K-1080-2019
    Recently, Response Surface Methodology (RSM) has attracted a growing interest, along with other simulation optimization (SO) techniques, for non-parametric modeling and robust optimization of systems. In the optimization stage of this study, the authors use RSM to find optimum working conditions of a system. The authors also use discrete event simulation modeling, optimization stage integration, design of experiment (DOE) and sensitivity analysis (a) to investigate the behavior of a real paint shop production line via construction of response surface plots and (b) to reveal the influence of input variables, as well as to determine interaction effects between them. The proposed approach presents an approximation model management structure for the computation-intensive optimization problem of an automotive factory with reduced variance, computational cost and amount of effort.
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    New Integer Linear Programming Formulation for the Traveling Salesman Problem with Time Windows: Minimizing Tour Duration with Waiting Times
    (2013) Kara, Imdat; Koc, Ozge Nimet; Altiparmak, Fulya; Dengiz, Berna; 0000-0003-1730-4214; ABH-1078-2021; ABH-1078-2021
    The travelling salesman problem, being one of the most attractive and well-studied combinatorial optimization problems, has many variants, one of which is called travelling salesman problem with Time Windows (TSPTW)'. In this problem, each city (nodes, customers) must be visited within a time window defined by the earliest and the latest time. In TSPTW, the traveller has to wait at a city if he/she arrives early; thus waiting times directly affect the duration of a tour. It would be useful to develop a new model solvable by any optimizer directly. In this paper, we propose a new integer linear programming formulation having O(n(2)) binary variables and O(n(2)) constraints, where (n) equals the number of nodes of the underlying graph. The objective function is stated to minimize the total travel time plus the total waiting time. A computational comparison is made on a suite of test problems with 20 and 40 nodes. The performances of the proposed and existing formulations are analysed with respect to linear programming relaxations and the CPU times. The new formulation considerably outperforms the existing one with respect to both the performance criteria. Adaptation of our formulation to the multi-traveller case and some additional restrictions for special situations are illustrated.
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    Multi-Objective Optimization of A Stochastic Assembly Line Balancing: A Hybrid Simulated Annealing Algorithm
    (2011) Cakir, Burcin; Altiparmak, Fulya; Dengiz, Berna; 0000-0003-1730-4214; AAF-7020-2021
    This paper deals with multi-objective optimization of a single-model stochastic assembly line balancing problem with parallel stations. The objectives are as follows: (1) minimization of the smoothness index and (2) minimization of the design cost. To obtain Pareto-optimal solutions for the problem, we propose a new solution algorithm, based on simulated annealing (SA), called m_SAA, m_SAA implements a multinomial probability mass function approach, tabu list, repair algorithms and a diversification strategy. The effectiveness of m_SAA is investigated comparing its results with those obtained by another SA (using a weight-sum approach) on a suite of 24 test problems. Computational results show that m_SAA with a multinomial probability mass function approach is more effective than SA with weight-sum approach in terms of the quality of Pareto-optimal solutions. Moreover, we investigate the effects of properties (i.e., the tabu list, repair algorithms and diversification strategy) on the performance of m_SAA. (C) 2010 Elsevier Ltd. All rights reserved.
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    Fractional Universal Kriging Metamodel
    (2022) Balaban, Muzaffer; Dengiz, Berna
    In this study, a Kriging-based metamodel is proposed that can be used instead of the simulation model for complex problems where data generation with a simulation model may be costly. In this new model structure, which is proposed for cases where the drift function structure of the Universal Kriging meta-model is not known. A power function of the variables that can also take fractional values is used instead of the first and second order regression models used as the drift function in the Universal Kriging metamodel. The predictive power of this metamodel, which is called Fractional Universal Kriging metamodel, has been investigated by experimentally computational analysis. Validation analysis reveals that the Fractional Universal Kriging metamodels have superior predictive power with respect to Mean Squared Error and Maximum Squared Error performance measures. Thus, in the case that the input-output relationship of the simulation model can be expressed with a power function that includes the effects of higher order and different from the quadratic polynomial case, Fractional Universal Kriging metamodels are proposed as a new metamodel approach.
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    Driver Performance Appraisal Using GPS Terminal Measurements: A Conceptual Framework
    (2013) Simsek, Baris; Pakdil, Fatma; Dengiz, Berna; Testik, Murat Caner; 0000-0003-2389-4759; AAE-3672-2019; G-6133-2013
    Objective measurement for performance appraisal is vital but rarely conducted in a methodologically sound manner. In this paper, we provide a thorough assessment of how objective and fair performance appraisals of drivers can be conducted. Furthermore, a unique conceptual framework is provided for evaluation of safety interventions and operational performance through monitoring quantitative driver performance measures. The conceptual framework makes use of online-measurements obtained from Global Positioning System (GPS) terminals, and the data are evaluated using statistical process control (SPC) tools. SPC tools are useful in comparing individual driver performance to overall performance as well as for identifying time-dependent factors that influence performance. Quantitative performance measures considered in the study include speed violations, vehicle idle duration, and fuel consumption. As an illustration of the concepts and implementation at a logistics firm is provided. (C) 2012 Elsevier Ltd. All rights reserved.
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    Development of a Decision Support System for Selection of Reviewers to Evaluate Research and Development Projects
    (2023) Kocak, Serdar; Ic, Yusuf Tansel; Sert, Mustafa; Atalay, Kumru Didem; Dengiz, Berna
    The evaluation of Research and Development (R&D) projects consists of many steps depending on the government funding agencies and the support program. It is observed that the reviewer evaluation reports have a crucial impact on the support decisions of the projects. In this study, a decision support system (DSS), namely R&D Reviewer, is developed to help the decision-makers with the assignment of the appropriate reviewer to R&D project proposals. It is aimed to create an artificial intelligence-based decision support system that enables the classification of Turkish R&D projects with natural language processing (NLP) methods. Furthermore, we examine the reviewer ranking process by using fuzzy multi-criteria decision-making methods. The data in the database is processed primarily to classify the R&D projects and the word embedding model NLP, "Word2Vec". Also, we designed the Convolutional Neural Network (CNN) model to select the features by using the automatic feature learning approach. Moreover, we incorporate a new integrated hesitant fuzzy VIKOR and TOPSIS methodology into the developed DSS for the reviewer ranking process.