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    ANALYSIS OF THE EFFECT OF HEAVY INDUSTRY WORKERS’ DEMOGRAPHIC CHARACTERISTICS ON THEIR LIFE QUALITY
    (Burdur Mehmet Akif Ersoy University, 2022) Bağlan, Fatma Betül; Başlıgil, Hüseyin; Akarsu, Canan Hazal; İnan, Umut Hulusi
    Some professions may be differentiated from other occupational groups due to difficulties. The life quality of heavy industry workers is an important issue due to the difficulties it has. In this study, the effect of demographic characteristics of 38 heavy industry workers working in iron-steel and machinery industry on their life quality has been analyzed. The research hypotheses have been tested with the Partial Least Squares Structural Equation Model (PLS-SEM) and the Smart PLS 3 package program. Multi-criteria decision making techniques have been used to rank the accepted hypotheses according to their degree of impact. AHP integrated TOPSIS method has been used as a fuzzy decision-making approach and it has been seen that the ranking of the hypotheses according to their degree of impact supports the results of the KEKK-YEM analysis. As a result of the analysis, 7 hypotheses have been accepted. It has been observed that the H20 hypothesis, that is, the effect of education level on environmental conditions, has the highest effect. To the best of our knowledge, KEKK-YEM and fuzzy AHP integrated TOPSIS method have been used for the first time in the literature for the problem of determining the effect of demographic characteristics of heavy industry workers on their life quality.
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    Öğe
    Job shop scheduling with genetic algorithm-based hyperheuristic approach
    (2022) Akarsu, Canan Hazal; Küçükdeniz, Tarık
    Job shop scheduling problems are NP-hard problems that have been studied extensively in the literature as well as in real-life. Many factories all over the world produce worth millions of dollars with job shop type production systems. It is crucial to use effective production scheduling methods to reduce costs and increase productivity. Hyperheuristics are fast-implementing, low-cost, and powerful enough to deal with different problems effectively since they need limited problem-specific information. In this paper, a genetic algorithm-based hyperheuristic (GAHH) approach is proposed for job shop scheduling problems. Twenty-six dispatching rules are used as low-level heuristics. We use a set of benchmark problems from OR-Library to test the proposed algorithm. The performance of the proposed approach is compared with genetic algorithm, simulating annealing, particle swarm optimization and some of dispatching rules. Computational experiments show that the proposed genetic algorithm-based hyperheuristic approach finds optimal results or produces better solutions than compared methods.