Browsing by Author "Eliiyi, Uğur"

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Citation - Scopus: 8
An energy-efficient single machine scheduling with release dates and sequence-dependent setup times
(Association for Computing Machinery Inc acmhelp@acm.org, 2018) Uǧur Eliiyi; M. Fatih Tasgetiren; Damla Kizilay; Hande Oztop; Quanke Pan; Kizilay, Damla; Fatih Tasgetiren, M.; Öztop, Hande; Pan, Quan-Ke; Eliiyi, Uğur
This study considers single machine scheduling with the machine operating at varying speed levels for different jobs with release dates and sequence-dependent setup times in order to examine the trade-off between makespan and total energy consumption. A bi-objective mixed integer linear programming model is developed employing this speed scaling scheme. The augmented ε-constraint method with a time limit is used to obtain a set of non-dominated solutions for each instance of the problem. An energy-efficient multi-objective variable block insertion heuristic is also proposed. The computational results on a benchmark suite consisting of 260 instances with 25 jobs from the literature reveal that the proposed algorithm is very competitive in terms of providing tight Pareto front approximations for the problem. © 2018 Elsevier B.V. All rights reserved.
Citation - WoS: 9
Citation - Scopus: 12
Artificial Intelligence Approaches to Estimate the Transport Energy Demand in Turkey
(Springer Science and Business Media Deutschland GmbH, 2021) Mert Sinan Turgut; Uǧur Eliiyi; Oğuz Emrah Turgut; Erdinc Oner; D. T. Eliiyi; Turgut, Oguz Emrah; Eliiyi, Uğur; Turgut, Mert Sinan; Öner, Erdinç; Eliiyi, Deniz Türsel
In this study eight parameters are selected and their historical data are collected to predict the future of the energy demand of Turkey. The initial eight parameters were the gross domestic product (GDP) of Turkey average annual US crude oil price (COP) inflation for Turkey in percentages (INF) the population of Turkey total vehicle travel in kilometers for Turkey total amount of goods transported on motorways employment for Turkey and trade of Turkey. However after these eight parameters data are analyzed using Pearson and Spearman correlation methods it is found out that five of these parameters are highly correlated. The remaining three parameters are the GDP of Turkey COP and INF for Turkey. Afterward five separate scenarios are developed to forecast the future of the energy demand of Turkey. The first two scenarios involve the third- and fourth-order polynomial fitting the third and fourth scenarios employ static and recurrent neural networks and the fifth scenario utilizes autoregressive models to predict the future energy demand of Turkey. The efficient hybridization of the seagull optimization and very optimistic method of minimization metaheuristic algorithms is carried out to achieve the polynomial fitting of the data. The optimization performance of the hybrid algorithm is assessed by applying the algorithm on benchmark optimization problems and comparing the results with that of some other metaheuristic optimizers. Moreover it is seen that the forecasts of the first scenario agree well with the Ministry of the Energy and Natural Resources estimates. © 2021 Elsevier B.V. All rights reserved.