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Endüstri Mühendisliği Bölümü

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Browsing Endüstri Mühendisliği Bölümü by Publication Category "Kitap Bölümü - Uluslararası"

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    Citation - Scopus: 2
    A Multiple Criteria Ranking Method Based on Outranking Relations: An Extension for Prospect Theory
    (Springer Science and Business Media Deutschland GmbH, 2022) Karasakal, E.; Karasakal, Orhan; Karasakal, O.; Şentürk, H.; 216553; Endüstri Mühendisliği
    In this study, Prospect Theory is integrated into a well-known multiple criteria ranking method, PROMETHEE. PROMETHEE considers the outranking relations among alternatives based on the preference functions. Prospect Theory evaluates the alternatives with a difference function based on gains and losses. The preference functions of PROMETHEE are modified to capture the choice behavior of the decision maker. The proposed method is a generalization of PROMETHEE that can handle the higher loss impact case as well as the usual equal loss and gain impact. The proposed method is compared with PROMETHEE, PT-PROMETHEE that is an extension of PROMETHEE with reference alternative, and the weighted sum method using an exemplary data set and Times Higher Education (THE) World University Ranking 2019 and 2020 data. The results show that rankings of alternatives change significantly when the impact of losses is larger than gains. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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    Citation - Scopus: 0
    Heterogeneous Sensor Data Fusion for Target Classification Using Adaptive Distance Function
    (Springer Science and Business Media Deutschland GmbH, 2021) Atıcı, B.; Karasakal, Orhan; Karasakal, E.; Karasakal, O.; 216553; Endüstri Mühendisliği
    Automatic Target Recognition (ATR) systems are used as decision support systems to classify the potential targets in military applications. These systems are composed of four phases, which are selection of sensors, preprocessing of radar data, feature extraction and selection, and processing of features to classify potential targets. In this study, the classification phase of an ATR system having heterogeneous sensors is considered. We propose novel multiple criteria classification methods based on the modified Dempster–Shafer theory. Ensemble of classifiers is used as the first step probabilistic classification algorithm. Artificial neural network and support vector machine are employed in the ensemble. Each non-imaginary dataset coming from heterogeneous sensors is classified by both classifiers in the ensemble, and the classification result that has a higher accuracy ratio is chosen for each of the sensors. The proposed data fusion algorithms are used to combine the sensors’ results to reach the final class of the target. We present extensive computational results that show the merits of the proposed algorithms. © 2021, Springer Nature Switzerland AG.
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    Citation - Scopus: 7
    Toward Sustainability: A Review of Analytical Models for Circular Supply Chains
    (Emerald Group Publishing Ltd., 2022) Ülkü, M.A.; Yıldırım, Gonca; Skinner, D.M.; Yildirim, G.; 45908; Endüstri Mühendisliği
    The earth's carrying capacity cannot withstand the pace of consumption resulting from current economic models, mainly the linear economy (LE) built on a throwaway culture. In the last few decades, the concept of a circular economy (CE), aiming to design waste out of the economy and mimic ecosystems, emerged as a strong alternative to LE. Being at the heart of the economic landscape, supply chains (SCs) need to respond to the necessary shift to CE. In so doing, the planning and execution of circular supply chains (CSCs) require a broader comprehension of CE and more sophisticated and large-scale analytical decision models. This chapter surveys extant literature on available best practices and quantitative models for sustainable supply chains (SSCs) and offers a new definition of CSC. Mapping on the knowledge extracted from this classification, potential gaps and strengths in the literature are identified. Key research papers on the "closed-loop" and "open-loop" ends of CSCs are highlighted. Challenges in developing CSC performance indicators and prescriptive models are emphasized. © 2022 by Emerald Publishing Limited. All rights reserved.