Kizilay, Damla

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Profile URL
https://gcris.yasar.edu.tr/handle/123456789/12703
Name Variants
Job Title
Araş.Gör.Dr.
Email Address
Main Affiliation
01.01.09.03. Endüstri Mühendisliği Bölümü
Status
Former Staff
Website
ORCID ID
0000-0002-6561-8819
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
Cj58LZAAAAAJ
WoS Researcher ID
GSE-0618-2022

Sustainable Development Goals

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

27

Citations

627

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Scholarly Output

21

Articles

9

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0/0

Supervised MSc Theses

0

Supervised PhD Theses

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WoS Citation Count

325

Scopus Citation Count

408

Patents

0

Projects

0

WoS Citations per Publication

15.48

Scopus Citations per Publication

19.43

Open Access Source

6

Supervised Theses

0

JournalCount
IEEE Congress on Evolutionary Computation (CEC)3
2020 IEEE Congress on Evolutionary Computation CEC 20202
Algorithms2
Computers & Operations Research2
4th International Conference on Swarm Evolutionary and Memetic Computing (SEMCCO)1
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  • Thumbnail Image
    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: 99
    Citation - Scopus: 113
    Iterated greedy algorithms for the blocking flowshop scheduling problem with makespan criterion
    (Elsevier Ltd, 2017) M. Fatih Tasgetiren; Damla Kizilay; Quanke Pan; Ponnuthurai Nagaratnam Suganthan; Tasgetiren, M. Fatih; Kizilay, Damla; Suganthan, P.N.; Pan, Quan-Ke
    Recently iterated greedy algorithms have been successfully applied to solve a variety of combinatorial optimization problems. This paper presents iterated greedy algorithms for solving the blocking flowshop scheduling problem (BFSP) with the makespan criterion. Main contributions of this paper can be summed up as follows. We propose a constructive heuristic to generate an initial solution. The constructive heuristic generates better results than those currently in the literature. We employ and adopt well-known speed-up methods from the literature for both insertion and swap neighborhood structures. In addition an iteration jumping probability is proposed to change the neighborhood structure from insertion neighborhood to swap neighborhood. Generally speaking the insertion neighborhood is much more effective than the swap neighborhood for the permutation flowshop scheduling problems. Instead of considering the use of these neighborhood structures in a framework of the variable neighborhood search algorithm two powerful local search algorithms are designed in such a way that the search process is guided by an iteration jumping probability determining which neighborhood structure will be employed. By doing so it is shown that some additional enhancements can be achieved by employing the swap neighborhood structure with a speed-up method without jeopardizing the effectiveness of the insertion neighborhood. We also show that the performance of the iterated greedy algorithm significantly depends on the speed-up method employed. The parameters of the proposed iterated greedy algorithms are tuned through a design of experiments on randomly generated benchmark instances. Extensive computational results on Taillard's well-known benchmark suite show that the iterated greedy algorithms with speed-up methods are equivalent or superior to the best performing algorithms from the literature. Ultimately 85 out of 120 problem instances are further improved with substantial margins. © 2017 Elsevier B.V. All rights reserved.
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    Conference Object
    Citation - WoS: 5
    Citation - Scopus: 5
    A Discrete Artificial Bee Colony Algorithm for the Assignment and Parallel Machine Scheduling Problem in DYO Paint Company
    (IEEE, 2014) Damla Kizilay; M. Fatih Tasgetiren; Onder Bulut; Bilgehan Bostan; Kizilay, Damla; Tasgetiren, M. Fatih; Bulut, Onder; Bostan, Bilgehan
    This paper presents a discrete artificial bee colony algorithm to solve the assignment and parallel machine scheduling problem in DYO paint company. The aim of this paper is to develop some algorithms to be employed in the DYO paint company by using their real-life data in the future. Currently in the DYO paint company, there exist three types of filling machines groups. These are automatic semiautomatic and manual machine groups where there are several numbers of identical machines. The problem is to first assign the filling production orders (jobs) to machine groups. Then filling production orders assigned to each machine group should be scheduled on identical parallel machines to minimize the sum of makespan and the total weighted tardiness. We also develop a traditional genetic algorithm to solve the same problem. The computational results show that the DABC algorithm outperforms the GA on set of benchmark problems we have generated.
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    Conference Object
    Citation - WoS: 10
    Citation - Scopus: 23
    A Differential Evolution Algorithm with Q-Learning for Solving Engineering Design Problems
    (Institute of Electrical and Electronics Engineers Inc., 2020) Damla Kizilay; M. Fatih Tasgetiren; Hande Oztop; Levent Kandiller; Ponnuthurai Nagaratnam Suganthan; Kizilay, Damla; Tasgetiren, M. Fatih; Suganthan, P. N.; Oztop, Hande; Kandiller, Levent
    In this paper a differential evolution algorithm with Q-Learning (DE-QL) for solving engineering Design Problems (EDPs) is presented. As well known the performance of a DE algorithm depends on the mutation strategy and its control parameters namely crossover and mutation rates. For this reason the proposed DE-QL generates the trial population by using the QL method in such a way that the QL guides the selection of the mutation strategy amongst four distinct strategies as well as crossover and mutation rates from the Q table. The DE-QL algorithm is well equipped with the epsilon constraint handling method to balance the search between feasible regions and infeasible regions during the evolutionary process. Furthermore a new mutation operator namely DE/Best to current/l is proposed in the DE-QL algorithm. In this paper 57 EDPs provided in 'Problem Definitions and Evaluation Criteria for the CEC 2020 Competition and Special Session on A Test-suite of Non-Convex Constrained optimization Problems from the Real-World and Some Baseline Results' are tested by the DE-QL. We provide our results in Appendixes and will be evaluated with other competitors in the competition. © 2020 Elsevier B.V. All rights reserved.
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    Citation - WoS: 7
    Citation - Scopus: 11
    A variable block insertion heuristic for permutation flowshops with makespan criterion
    (IEEE, 2017) M. Fatih Tasgetiren; Quan-Ke Pan; Damla Kizilay; Mario C. Velez-Gallego; Tasgetiren, M. Fatih; Kizilay, Damla; Pan, Quan-Ke; Velez-Gallego, Mario C.
    This paper proposes a populated variable block insertion heuristic (PVBIH) algorithm for solving the permutation flowshop scheduling problem with the makespan criterion. The PVBIH algorithm starts with a minimum block size being equal to one. It removes a block from the current solution and inserts it into the partial solution randomly with a predetermined move size. A local search is applied to the solution found after several block moves. If the new solution generated after the local search is better than the current solution it replaces the current solution. It retains the same block size as long as it improves. Otherwise the block size is incremented by one and a simulated annealing-type of acceptance criterion is used to accept the new solution. This process is repeated until the block size reaches at the maximum block size. In addition we present a randomized profile fitting heuristic with excellent results. Extensive computational results on the Taillard's well-known benchmark suite show that the proposed PVBIH algorithm substantially outperforms the differential evolution algorithm (NS-SGDE) recently proposed in the literature.
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    Article
    Citation - WoS: 20
    Citation - Scopus: 25
    A variable block insertion heuristic for solving permutation flow shop scheduling problem with makespan criterion
    (MDPI AG indexing@mdpi.com Postfach Basel CH-4005, 2019) Damla Kizilay; M. Fatih Tasgetiren; Quanke Pan; Liang Gao; Kizilay, Damla; Tasgetiren, Mehmet Fatih; Gao, Liang; Pan, Quan-Ke
    In this paper we propose a variable block insertion heuristic (VBIH) algorithm to solve the permutation flow shop scheduling problem (PFSP). The VBIH algorithm removes a block of jobs from the current solution. It applies an insertion local search to the partial solution. Then it inserts the block into all possible positions in the partial solution sequentially. It chooses the best one amongst those solutions from block insertion moves. Finally again an insertion local search is applied to the complete solution. If the new solution obtained is better than the current solution it replaces the current solution with the new one. As long as it improves it retains the same block size. Otherwise the block size is incremented by one and a simulated annealing-based acceptance criterion is employed to accept the new solution in order to escape from local minima. This process is repeated until the block size reaches its maximum size. To verify the computational results mixed integer programming (MIP) and constraint programming (CP) models are developed and solved using very recent small VRF benchmark suite. Optimal solutions are found for 108 out of 240 instances. Extensive computational results on the VRF large benchmark suite show that the proposed algorithm outperforms two variants of the iterated greedy algorithm. 236 out of 240 instances of large VRF benchmark suite are further improved for the first time in this paper. Ultimately we run Taillard's benchmark suite and compare the algorithms. In addition to the above three instances of Taillard's benchmark suite are also further improved for the first time in this paper since 1993. © 2019 Elsevier B.V. All rights reserved.
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    Citation - WoS: 2
    A Variable Block Insertion Heuristic for Single Machine with Release Dates and Sequence Dependent Setup Times for Makespan Minimization
    (IEEE, 2019) Jiaxin Fan; Damla Kizilay; Hande Oztop; Kizilay, Damla; Oztop, Hande; Fan, Jiaxin
    This paper is concerned with solving the single machine scheduling problem with release dates and sequence-dependent setup times in order to minimize the makespan. For this purpose a variable block insertion heuristic (VBIH) algorithm is applied to the problem. The VBIH algorithm performs block moves on a given solution. Mainly it removes a block of jobs with a given size from the solution and inserts the block into the best position of the partial solution. Furthermore we present a novel profile-fitting constructive heuristic for the problem. We evaluate the performance of the VBIH algorithm by comparisons with the beam search heuristic and the iterated greedy algorithm from the literature. Extensive computational results on the benchmark suite consisting of 900 instances from the literature show that the proposed VBIH algorithm is very competitive to the recent beam search heuristic and iterated greedy algorithm from the literature.
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    Citation - Scopus: 6
    Optimization of Costs in Empty Container Repositioning
    (Springer Science and Business Media Deutschland GmbH, 2020) Mehmet Yasin Göçen; Öykü Çağlar; Elif Ercan; Damla Kizilay; Ercan, Elif; Kizilay, Damla; Göçen, Mehmet Yasin; Çağlar, Öykü; M.N. Osman Zahid , R. Abd. Aziz , A.R. Yusoff , N. Mat Yahya , F. Abdul Aziz , M. Yazid Abu , N.M. Durakbasa , M.G. Gençyilmaz
    In this study the empty container repositioning problem in a liner shipping company is considered. The research includes necessary information about the company and maritime transportation highlighting the importance of empty container storage. As the worldwide containerized trade increases the container traffic increases. Consequently the surplus containers are repositioned to locations where they are required which causes high costs. Therefore in the existing system the company deals with higher prices. In this study the storage location of the empty containers which are coming from the vessels to the ports are determined according to the different cost policies of the warehouses. Thus our problem is defined as an empty container repositioning problem. The primary objective of this study is to build an operations-research based decision-making system in order to minimize the total cost regarding changing prices of warehouses and transportation cost of containers. In the decision-making process this study will help the company to overcome the complexity about repositioning empty containers regarding the container size storage day of the containers changing inventory holding costs of the warehouses and the transportation costs of the containers from port to the corresponding warehouses. Taking into consideration all these parts of the problem this study will eventually help in reducing inventory holding and transportation costs of the empty container storage. As a solution methodology the mixed integer linear programming model is proposed and optimal solutions are obtained in a concise time. © 2022 Elsevier B.V. All rights reserved.
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    Citation - WoS: 12
    Citation - Scopus: 17
    A Populated Local Search with Differential Evolution for Blocking Flowshop Scheduling Problem
    (IEEE, 2015) M. Fatih Tasgetiren; Quan-Ke Pan; Damla Kizilay; Gursel Suer; Tasgetiren, M. Fatih; Kizilay, Damla; Pan, Quan-Ke; Suer, Gursel
    This paper presents a populated local search algorithm through a differential evolution algorithm for solving the blocking flowshop scheduling problem under makespan criterion. Iterated greedy and iterated local search algorithms are simple but extremely effective in solving scheduling problems. However these two algorithms have some parameters to be tuned for which it requires a design of experiments with expensive runs. In this paper we propose a novel multi-chromosome solution representation for both local search and differential evolution algorithm which is responsible for providing the parameters of IG and ILS algorithms. In other words these parameters are learned by the differential evolution algorithm in order to guide the local search process. We also present the greedy randomized adaptive search procedure (GRASP) for the problem on hand. The performance of the populated local search algorithm with differential evolution algorithm and the GRASP heuristic is tested on Taillard's benchmark suite and compared to the best performing algorithms from the literature. Ultimately 90 out of 120 problem instances are further improved.