Taşgetiren, Mehmet Fatih

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https://gcris.yasar.edu.tr/handle/123456789/12701
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Prof.Dr.
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01.01.09.03. Endüstri Mühendisliği Bölümü
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Former Staff
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ORCID ID
0000-0002-5716-575X
Scopus Author ID
6505799356
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pYeRQGEAAAAJ
WoS Researcher ID
CDL-1821-2022

Sustainable Development Goals

NO POVERTY1
NO POVERTY
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ZERO HUNGER2
ZERO HUNGER
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GOOD HEALTH AND WELL-BEING3
GOOD HEALTH AND WELL-BEING
0
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QUALITY EDUCATION4
QUALITY EDUCATION
2
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GENDER EQUALITY5
GENDER EQUALITY
0
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CLEAN WATER AND SANITATION6
CLEAN WATER AND SANITATION
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AFFORDABLE AND CLEAN ENERGY7
AFFORDABLE AND CLEAN ENERGY
8
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DECENT WORK AND ECONOMIC GROWTH8
DECENT WORK AND ECONOMIC GROWTH
1
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INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
8
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REDUCED INEQUALITIES10
REDUCED INEQUALITIES
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SUSTAINABLE CITIES AND COMMUNITIES11
SUSTAINABLE CITIES AND COMMUNITIES
4
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RESPONSIBLE CONSUMPTION AND PRODUCTION12
RESPONSIBLE CONSUMPTION AND PRODUCTION
3
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CLIMATE ACTION13
CLIMATE ACTION
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LIFE BELOW WATER14
LIFE BELOW WATER
3
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LIFE ON LAND15
LIFE ON LAND
1
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PEACE, JUSTICE AND STRONG INSTITUTIONS16
PEACE, JUSTICE AND STRONG INSTITUTIONS
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PARTNERSHIPS FOR THE GOALS17
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1
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Documents

154

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9631

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Documents

148

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Scholarly Output

113

Articles

47

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8

Supervised PhD Theses

3

WoS Citation Count

5021

Scopus Citation Count

6160

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WoS Citations per Publication

44.43

Scopus Citations per Publication

54.51

Open Access Source

15

Supervised Theses

11

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IEEE Congress on Evolutionary Computation (CEC)10
Computers & Operations Research6
Computers & Industrial Engineering5
Algorithms4
Expert Systems with Applications4
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  • Thumbnail Image
    Conference Object
    Citation - WoS: 2
    Citation - Scopus: 3
    Lagrangian heuristic for scheduling a steelmaking-continuous casting process
    (IEEE, 2013) Kun Mao; Quanke Pan; M. Fatih Tasgetiren; Tasgetiren, M. Fatih; Fatih Tasgetiren, M.; Mao, Kun; Pan, Quanke
    One of the biggest bottlenecks in iron and steel production is the steelmaking-continuous casting (SCC) process which consists of steel-making re ning and continuous casting. The production scheduling of SCC is a complex hybrid owshop (HFS) scheduling with following features: job grouping and precedence constraints no dead time inside the same group of jobs setup time constraints on the casters. A mixed-integer programming (MIP) model is established with the objective of minimizing the total weighted penalties of the earliness/tardiness and the job waiting. Through relaxing the operation precedence constraints to the objective function the relaxed problem can be decomposed into to smaller subproblems each of which corresponds a speciCE stage. A new dynamic programming algorithm is developed for solving the subproblems which are parallel machine scheduling problem with objective of minimizing total weighted completion time where the weights of jobs may be negative. The Lagrangian dual problem is solved by an improved subgradient level algorithm which can guarantee global convergence. A novel heuristic is presented to adjust subproblem solutions to obtain a feasible schedule. The computational results demonstrate that the propose LR approach can generate a high quality schedule within an acceptable computation time.
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    Article
    Citation - WoS: 298
    Citation - Scopus: 384
    A self-adaptive global best harmony search algorithm for continuous optimization problems
    (ELSEVIER SCIENCE INC, 2010) Quan-Ke Pan; P. N. Suganthan; M. Fatih Tasgetiren; J. J. Liang; Liang, J. J.; Suganthan, P. N.; Tasgetiren, M. Fatih; Pan, Quan-Ke
    This paper presents a self-adaptive global best harmony search (SGHS) algorithm for solving continuous optimization problems. In the proposed SGHS algorithm a new improvisation scheme is developed so that the good information captured in the current global best solution can be well utilized to generate new harmonies. The harmony memory consideration rate (HMCR) and pitch adjustment rate (PAR) are dynamically adapted by the learning mechanisms proposed. The distance bandwidth (BW) is dynamically adjusted to favor exploration in the early stages and exploitation during the final stages of the search process. Extensive computational simulations and comparisons are carried out by employing a set of 16 benchmark problems from literature. The computational results show that the proposed SGHS algorithm is more effective in finding better solutions than the state-of-the-art harmony search (HS) variants. (C) 2010 Elsevier Inc. All rights reserved.
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    Conference Object
    Citation - WoS: 14
    Citation - Scopus: 17
    Green Permutation Flowshop Scheduling: A Trade- off- Between Energy Consumption and Total Flow Time
    (SPRINGER INTERNATIONAL PUBLISHING AG, 2018) Hande Oztop; M. Fatih Tasgetiren; Deniz Tursel Eliiyi; Quan-Ke Pan; Tasgetiren, M. Fatih; Fatih Tasgetiren, M.; Oztop, Hande; Pan, Quan-Ke; Türsel Eliiyi, Deniz; Eliiyi, Deniz Tursel; DS Huang; MM Gromiha; K Han; A Hussain
    Permutation flow shop scheduling problem (PFSP) is a well-known problem in the scheduling literature. Even though many multi-objective PFSPs are presented in the literature with the objectives related to production efficiency and customer satisfaction studies considering energy consumption and environmental effects in scheduling is very seldom. In this paper the trade-off between total energy consumption (TEC) and total flow time is investigated in a PFSP environment where the machines are assumed to operate at varying speed levels. A multi-objective mixed integer linear programming model is proposed based on a speed-scaling strategy. Due to the NP-complete nature of the problem an efficient multi-objective iterated greedy (IGALL) algorithm is also developed. The performance of IGALL is compared with model performance in terms of quality and cardinality of the solutions.
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    Conference Object
    Citation - WoS: 9
    Citation - Scopus: 13
    Solving fuzzy job-shop scheduling problem by a hybrid PSO algorithm
    (Springer-Verlag Berlin, 2012) Junqing Li; Quanke Pan; Ponnuthurai Nagaratnam Suganthan; M. Fatih Tasgetiren; Li, Junqing; Suganthan, P.N.; Tasgetiren, M. Fatih; Pan, Quan-Ke
    This paper proposes a hybrid particle swarm optimization (PSO) algorithm for solving the job-shop scheduling problem with fuzzy processing times. The objective is to minimize the maximum fuzzy completion time i.e. the fuzzy makespan. In the proposed PSO-based algorithm performs global explorative search while the tabu search (TS) conducts the local exploitative search. One-point crossover operator is developed for the individual to learn information from the other individuals. Experimental results on three well-known benchmarks and a randomly generated case verify the effectiveness and efficiency of the proposed algorithm. © 2012 Springer-Verlag. © 2012 Elsevier B.V. All rights reserved.
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    Conference Object
    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.
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    Article
    Citation - WoS: 5
    Citation - Scopus: 5
    Solving blocking flowshop scheduling problem with makespan criterion using q-learning-based iterated greedy algorithms
    (Growing Science, 2024) M. Fatih Tasgetiren; Damla Kizilay; Levent Kandiller; Tasgetiren, M. Fatih; Kizilay, Damla; Kandiller, Levent
    This study proposes Q-learning-based iterated greedy (IGQ) algorithms to solve the blocking flowshop scheduling problem with the makespan criterion. Q learning is a model-free machine intelligence technique which is adapted into the traditional iterated greedy (IG) algorithm to determine its parameters mainly the destruction size and temperature scale factor adaptively during the search process. Besides IGQ algorithms two different mathematical modeling tech-niques. One of these techniques is the constraint programming (CP) model which is known to work well with scheduling problems. The other technique is the mixed integer linear programming (MILP) model which provides the mathematical definition of the problem. The introduction of these mathematical models supports the validation of IGQ algorithms and provides a comparison between different exact solution methodologies. To measure and compare the performance of IGQ algorithms and mathematical models extensive computational experiments have been performed on both small and large VRF benchmarks available in the literature. Computational results and statistical analyses indicate that IGQ algorithms generate substantially better results when compared to non-learning IG algorithms. © 2024 Elsevier B.V. All rights reserved.
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    Article
    Citation - WoS: 50
    Citation - Scopus: 56
    An energy-efficient permutation flowshop scheduling problem
    (Elsevier Ltd, 2020) Hande Oztop; M. Fatih Tasgetiren; D. T. Eliiyi; Quanke Pan; Levent Kandiller; Tasgetiren, M. Fatih; Öztop, Hande; Pan, Quan-Ke; Kandiller, Levent; Eliiyi, Deniz Türsel
    The permutation flowshop scheduling problem (PFSP) has been extensively explored in scheduling literature because it has many real-world industrial implementations. In some studies multiple objectives related to production efficiency have been considered simultaneously. However studies that consider energy consumption and environmental impacts are very rare in a multi-objective setting. In this work we studied two contradictory objectives namely total flowtime and total energy consumption (TEC) in a green permutation flowshop environment in which the machines can be operated at varying speed levels corresponding to different energy consumption values. A bi-objective mixed-integer programming model formulation was developed for the problem using a speed-scaling framework. To address the conflicting objectives of minimizing TEC and total flowtime the augmented epsilon-constraint approach was employed to obtain Pareto-optimal solutions. We obtained near approximations for the Pareto-optimal frontiers of small-scale problems using a very small epsilon level. Furthermore the mathematical model was run with a time limit to find sets of non-dominated solutions for large instances. As the problem was NP-hard two effective multi-objective iterated greedy algorithms and a multi-objective variable block insertion heuristic were also proposed for the problem as well as a novel construction heuristic for initial solution generation. The performance of the developed heuristic algorithms was assessed on well-known benchmark problems in terms of various quality measures. Initially the performance of the algorithms was evaluated on small-scale instances using Pareto-optimal solutions. Then it was shown that the developed algorithms are tremendously effective for solving large instances in comparison to time-limited model. © 2020 Elsevier B.V. All rights reserved.
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    Conference Object
    Citation - Scopus: 17
    A Discrete Artificial Bee Colony Algorithm for the Permutation Flow Shop Scheduling Problem with Total Flowtime Criterion
    (IEEE, 2010) M. Fatih Tasgetiren; Quan-Ke Pan; P. Nagaratnam Suganthan; Angela H-L Chen; Tasgetiren, M. Fatih; Suganthan, P. Nagaratnam; Karabulut, Korhan; Pan, Quan-Ke; Ince, Yavuz; Chen, Angela H.-L.; Wang, Ling
    Very recently Jarboui et al. [1] (Computers & Operations Research 36 (2009) 2638-2646) and Tseng and Lin [2] (European Journal of Operational Research 198 (2009) 84-92) presented a novel estimation distribution algorithm (EDA) and a hybrid genetic local search (hGLS) algorithm for the permutation flowshop scheduling (PFSP) with the total flowtime (TFT) criterion respectively. Both algorithms generated excellent results thus improving all the best known solutions reported in the literature so far. However in this paper we present a discrete artificial bee colony (DABC) algorithm hybridized with an iterated greedy (IG) and iterated local search (ILS) algorithms embedded in a variable neighborhood search (VNS) procedure based on swap and insertion neighborhood structures. We also present a hybrid version of our previous discrete differential evolution (hDDE) algorithm employing the IG and VNS structure too. The performance of the DABC and hDDE is highly competitive to the EDA and hGLS algorithms in terms of both solution quality and CPU times. Ultimately 43 out of 60 best known solutions provided very recently by the EDA and hGLS algorithms are further improved by the DABC and hDDE algorithms with short-term search.
  • Thumbnail Image
    Conference Object
    Citation - WoS: 17
    Citation - Scopus: 23
    A Novel General Variable Neighborhood Search through Q-Learning for No-Idle Flowshop Scheduling
    (IEEE, 2020) Hande Oztop; Mehmet Fatih Tasgetiren; Levent Kandiller; Quan-Ke Pan; Tasgetiren, Mehmet Fatih; Oztop, Hande; Kandiller, Levent; Pan, Quan-Ke
    In this study a novel general variable neighborhood search through Q-learning (GVNS-QL) algorithm is proposed to solve the no-idle flowshop scheduling problem with the makespan objective. In the outer loop of the GVNS-QL insertion and exchange operators are used to shaking the permutation. On the other hand in the inner loop of variable neighborhood descent procedure variable iterated greedy and variable block insertion heuristic algorithms are employed with two effective insertion local search procedures. The proposed GVNS-QL defines the parameters of the algorithm using a Q-learning mechanism. The developed GVNS-QL algorithm is compared with the traditional iterated greedy (IG) algorithm using the well-known benchmark set. The comprehensive computational experiments show that the GVNS-QL outperforms the traditional IG algorithm. The results of the IG and GVNS-QL algorithms are also compared with the current best-known solutions reported in the literature. The computational results show that the proposed GVNS-QL algorithm improves the current best-known solutions for 104 out of 250 instances.
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    Article
    Citation - WoS: 79
    Citation - Scopus: 94
    Metaheuristic algorithms for the hybrid flowshop scheduling problem
    (PERGAMON-ELSEVIER SCIENCE LTD, 2019) Hande Oztop; M. Fatih Tasgetiren; Deniz Tursel Eliiyi; Quan-Ke Pan; Tasgetiren, M. Fatih; Fatih Tasgetiren, M.; Öztop, Hande; Pan, Quan-Ke; Eliiyi, Deniz Türsel
    The hybrid flowshop scheduling problem (HFSP) has been widely studied in the literature as it has many real-life applications in industry. Even though many solution approaches have been presented for the HFSP with makespan criterion studies on HFSP with total flow time minimization have been rather limited. This study presents a mathematical model four variants of iterated greedy algorithms and a variable block insertion heuristic for the HFSP with total flow time minimization. Based on the well-known NEH heuristic an efficient constructive heuristic is also proposed and compared with NEH. A detailed design of experiment is carried out to calibrate the parameters of the proposed algorithms. The HFSP benchmark suite is used for evaluating the performance of the proposed methods. As there are only 10 large instances in the current literature further 30 large instances are proposed as new benchmarks. The developed model is solved for all instances on CPLEX under a time limit and the performances of the proposed algorithms are assessed through comparisons with the results from CPLEX and the two best-performing algorithms in literature. Computational results show that the proposed algorithms are very effective in terms of solution time and quality. Additionally the proposed algorithms are tested on large instances for the makespan criterion which reveal that they also perform superbly for the makespan objective. Especially for instances with 30 jobs the proposed algorithms are able to find the current incumbent makespan values reported in literature and provide three new best solutions. (C) 2019 Elsevier Ltd. All rights reserved.