Kandiller, Levent

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Profile URL
https://gcris.yasar.edu.tr/handle/123456789/12588
Name Variants
Job Title
Prof.Dr.
Email Address
Main Affiliation
01.01.09.03. Endüstri Mühendisliği Bölümü
Status
Current Staff
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ORCID ID
0000-0002-7300-5561
Scopus Author ID
6506822666
Turkish CoHE Profile ID
Google Scholar ID
Kw9O-bEAAAAJ
WoS Researcher ID
B-3392-2019
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levent.kandiller.jpg (8.02 KB)

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
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QUALITY EDUCATION4
QUALITY EDUCATION
0
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GENDER EQUALITY5
GENDER EQUALITY
0
Research Products
CLEAN WATER AND SANITATION6
CLEAN WATER AND SANITATION
1
Research Products
AFFORDABLE AND CLEAN ENERGY7
AFFORDABLE AND CLEAN ENERGY
3
Research Products
DECENT WORK AND ECONOMIC GROWTH8
DECENT WORK AND ECONOMIC GROWTH
0
Research Products
INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
2
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REDUCED INEQUALITIES10
REDUCED INEQUALITIES
0
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SUSTAINABLE CITIES AND COMMUNITIES11
SUSTAINABLE CITIES AND COMMUNITIES
1
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RESPONSIBLE CONSUMPTION AND PRODUCTION12
RESPONSIBLE CONSUMPTION AND PRODUCTION
1
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CLIMATE ACTION13
CLIMATE ACTION
0
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LIFE BELOW WATER14
LIFE BELOW WATER
0
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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
0
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PARTNERSHIPS FOR THE GOALS17
PARTNERSHIPS FOR THE GOALS
1
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Documents

37

Citations

737

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14

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Documents

33

Citations

602

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Scholarly Output

37

Articles

12

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0/0

Supervised MSc Theses

4

Supervised PhD Theses

3

WoS Citation Count

347

Scopus Citation Count

409

Patents

0

Projects

0

WoS Citations per Publication

9.38

Scopus Citations per Publication

11.05

Open Access Source

3

Supervised Theses

7

JournalCount
Computers & Operations Research3
19th International Symposium for Production Research ISPR 20192
Swarm and Evolutionary Computation2
21st International Symposium on Production Research (ISPR) - Digitizing Production System2
22nd International Symposium for Production Research ISPR 20222
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Now showing 1 - 10 of 37
  • Thumbnail Image
    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 - 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: 15
    Citation - Scopus: 15
    A constraint programming approach to a real-world workforce scheduling problem for multi-manned assembly lines with sequence-dependent setup times
    (Taylor and Francis Ltd., 2024) Funda Güner; Abdül Kadir Görür; Benhür Satır; Levent Kandiller; John H. Drake; Satir, Benhur; Gorur, Abdul K.; Kandiller, Levent; Guner, Funda; Drake, John H.
    For over five decades researchers have presented various assembly line problems. Recently assembly lines with multiple workers at each workstation have become very common in the literature. These lines are often found in the manufacturing of large vehicles where workers at a workstation may perform their assigned tasks at the same time. Most research on multi-manned assembly lines focuses on balancing tasks and workers among workstations and scheduling tasks for workers. This study however concentrates on assigning tasks to workers already assigned to a specific workstation rather than balancing the entire line. The problem was identified through an industrial case study at a large vehicle manufacturing company. The study presents two methods one using mixed integer linear programming and the other using constraint programming to minimise the number of workers required on a multi-manned assembly line with sequence-dependent setup times. The results of the computational experiments indicate that the constraint programming method performs better than the mixed integer linear programming method on several modified benchmark instances from the literature. The constraint programming model is also tested on the real-world scenario of our industrial case study and leads to significant improvements in the productivity of the workstations. © 2024 Elsevier B.V. All rights reserved.
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    Doctoral Thesis
    Multi-Objective Green Hybrid Flowshop Scheduling Problems
    (2020) Öztop, Hande; Kandiller, Levent; Taşgetiren, Mehmet Fatih
    Literatürde, hibrid akış tipi çizelgeleme problemi çeşitli üretim verimliliği bazlı amaç fonksiyonları düşünülerek yaygın bir şekilde çalışılmıştır. Ancak, hibrid akış tipi çizelgeleme problemi için enerji tüketimi ve çevresel etkileri dikkate alan çalışmalar literatürde oldukça azdır. Bu tez, makinelerin değişen hız seviyelerinde çalışabildiği hibrid akış tipi atölyelerindeki, maksimum tamamlanma zamanı ve toplam enerji tüketimi amaç fonksiyonları arasındaki çelişkiyi ele almaktadır. Bu tezde, enerji-verimli hibrid akış tipi çizelgeleme problemi için, hız ölçeklendirme yöntemi kullanılarak, özgün iki-amaçlı karma-tamsayılı doğrusal programlama ve iki-amaçlı kısıt programlama model formülasyonları önerilmiştir. Bu tezde, hız ölçeklendirme yönteminin hem iş-bazlı hem de iş-tezgah (matris)-bazlı versiyonları çalışılmıştır. Maksimum tamamlanma zamanını ve toplam enerji tüketimini minimize etme amaç fonksiyonları birbirleriyle çeliştiklerinden dolayı, Pareto-optimal çözümleri elde etmek için genişletilmiş epsilon kısıt yöntemi kullanılmıştır. Küçük örnekler için Pareto-optimal eğriye oldukça yakın yaklaşımlar elde edilirken, büyük örnekler için ise önerilen karma-tamsayılı doğrusal programlama ve kısıt programlama model formülasyonları belirli bir süre limiti altında çözülerek baskın olmayan çözüm kümeleri elde edilmiştir. Ayrıca, çalışılan problemin NP-zor sınıfına ait bir problem olmasından dolayı, enerji-verimli hibrid akış tipi çizelgeleme probleminin hem iş-bazlı hem de matris-bazlı versiyonları için özgün iki-amaçlı metasezgisel algoritmalar özgün bir yapıcı sezgisel ile birlikte önerilmiştir. Problemin iş-bazlı versiyonu için iki tip yinelemeli açgözlü algoritma, bir değişken blok yerleştirme sezgiseli ve dört tip bütünleşik-metasezgisel algoritmalar önerilmiştir. Ayrıca, problemin matris-bazlı versiyonu için iki tip yinelemeli açgözlü algoritma, bir değişken blok yerleştirme sezgiseli ve bir bütünleşik-metasezgisel algoritma önerilmiştir. Bunların yanı sıra, bu tez, hibrid akış tipi çizelgeleme problemi için iki özgün sezgisel amaç fonksiyonu değeri hesaplama yöntemi de önermektedir. Literatürde oldukça bilinen hibrid akış tipi çizelgeleme problemi örnekleri kullanılarak, önerilen iki-amaçlı metasezgisellerin performansları birbirleriyle ve karma-tamsayılı doğrusal programlama ve kısıt programlama model formülasyonlarının çözümleri ile; çözümlerin sayısallığı, çeşitliliği ve yakınlığı açılarından kıyaslanmıştır. Öncelikle, metasezgisellerin performansı küçük örnekler üzerinde Pareto-optimal çözümler ile kıyaslanarak test edilmiştir. Ardından, önerilen metasezgisellerin büyük örnekleri çözmek adına hem çözüm kalitesi hem de çözüm süresi açısından oldukça etkin olduğu gösterilmiştir.
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    Conference Object
    Citation - WoS: 9
    A Multi-compartment Vehicle Routing Problem for Livestock Feed Distribution
    (SPRINGER INTERNATIONAL PUBLISHING AG, 2017) Levent Kandiller; Deniz Tursel Eliiyi; Bahar Tasar; Kandiller, Levent; Tasar, Bahar; Eliiyi, Deniz Tursel; KF Doerner; I Ljubic; G Pflug; G Tragler
    In the well-known Vehicle Routing Problem (VRP) customer demands from one or more depots are to be distributed via a fleet of vehicles. Various objectives of the problem are considered in literature including minimization of the total distance/time traversed by the fleet during distribution the total cost of vehicle usage or minimizing the maximum tour length/time. In this study we consider a multi-compartment VRP with incompatible products for the daily solution of a livestock feed distribution network where each livestock farm requests one type of feed from a single depot and the vehicles have several compartments. The objective is to minimize the total cost of distribution. Although VRP is a well-studied problem in literature multi-compartment VRP is considered only by few authors and our problem differs from the existing ones due to special operational constraints imposed by the situation on hand. We formulate a basic mathematical model for the problem and present possible extensions. We design a computational experiment for testing the effects of uncontrollable parameters over model performance on a commercial solver and report the results. The proposed model can easily be adapted to other distribution networks such as food and fuel/chemicals.
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    Article
    Citation - WoS: 15
    Citation - Scopus: 14
    Q-learning guided algorithms for bi-criteria minimization of total flow time and makespan in no-wait permutation flowshops
    (ELSEVIER, 2024) Damla Yuksel; Levent Kandiller; Mehmet Fatih Tasgetiren; Yüksel, Damla; Taşgetiren, Mehmet Fatih; Kandiller, Levent
    Combining Deep Reinforcement Learning and meta-heuristic techniques represents a new research direction for enhancing the search capabilities of meta-heuristic methods in the context of production scheduling. Q-learning is a prominent reinforcement learning in which its utilization aims to direct the selection of actions thus preventing the necessity for a random exploration in the iterative process of the metaheuristics. In this study we provide Q-learning guided algorithms for the Bi-Criteria No-Wait Flowshop Scheduling Problem (NWFSP). The problem is treated as a bi-criteria combinatorial optimization problem where total flow time and makespan are optimized simultaneously. Firstly a deterministic mixed-integer linear programming (MILP) model is provided. Then Q-learning guided algorithms are developed: Bi-Criteria Iterated Greedy Algorithm with Q-Learning (BCIGQL). Bi-Criteria Block Insertion Heuristic Algorithm with Q-Learning (BC-BIHQL). Moreover the performance of the proposed Q-learning guided algorithms is compared over a collection of Bi-Criteria Genetic Local Search Algorithms (BC-GLS) Bi-Criteria Iterated Greedy Algorithm (BC-IG) Bi-Criteria Iterated Greedy Algorithm with a Local Search (BC-IGALL) and Bi-Criteria Variable Block Insertion Heuristic Algorithm (BC-VBIH). The complete computational experiment performed on 480 problem instances of Vallada et al. (2015) which is known as the VRF benchmark set indicates that the BC-BIHQL and the BC-IGQL algorithms outperform the BC-GLS BC-IG BCIGALL and BC-VBIH algorithms in comparative performance metrics. More specifically the proposed BC-BIHQL and BC-IGQL algorithms can yield more non-dominated bi-criteria solutions with the most substantial competitiveness than the remaining algorithms. At the same time both are competitive with each other on the benchmark problems. Moreover the BC-IGQL algorithm dominates almost 97% and 99% of the solutions reached by the BC-IG BC-IGALL and BC-VBIH algorithms in small and large datasets. Similarly The BC-BIHQL algorithm dominates almost 98% and 99% of the solutions reached by the BC-IG BC-IGALL and BC-VBIH algorithms in small and large datasets respectively. This means that among all the features that have been compared the Qlearning-guided algorithms demonstrate the highest level of competitiveness. The outcomes of this study encourage us to discover many more bi-criteria NWFSPs to reveal the trade-off between other conflicting objectives such as makespan & the number of early jobs to overcome various industries' problems.
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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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    Conference Object
    Scheduling of Test Operations at Bosch Thermotechnology R&D Laboratory
    (Springer Science and Business Media Deutschland GmbH, 2020) Pelin Akçay; Mustafa Doğa Çetin; Batu Ekmekçi; Beste Yıldız; Levent Kandiller; Damla Kizilay; Akçay, Pelin; Yıldız, Beste; Kızılay, Damla; Ekmekçi, Batu; Kandiller, Levent; Çetin, Mustafa Doğa; 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 Bosch’s Research and Development (R&D) laboratories several performance tests are executed to check whether the targeted quality and reliability values of the products are attained. Each product has to go through several tests on several test machines with different processing times. The specified problem in Bosch Thermotechnology entirely fits the Flexible Job Shop Scheduling (FJSS) Problem with machine and operation dependent setup times. The most crucial factor while performing these schedules of the products is to complete them as soon as possible to provide customer satisfaction as well as test machine utilization. Hence our objective function is to minimize the makespan. Additionally a decision support system (DSS) for the company on MS Excel is developed. © 2022 Elsevier B.V. All rights reserved.
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    Conference Object
    A Warehouse Management Decision Support System for a Spare Parts Company
    (Springer Science and Business Media Deutschland GmbH, 2024) Burak Kurt; Güner Şirin; Melis Karakurt; Sevde Zümrüt İspay; Simge Güçlükol Ergin; Levent Kandiller; Karakurt, Melis; Kurt, Burak; İspay, Sevde Zümrüt; Şirin, Güner; Kandiller, Levent; Ergin, Simge Güçlükol; N.M. Durakbasa , M.G. Gençyılmaz
    Warehouse management is considered one of the essential components of a supply chain. Inadequate storage space and inefficient available storage are common problems in designing warehouses. This study underlines the need for an effective warehouse management policy because of the limited space for finished goods. The complementary solution is to ensure the highest-selling inventory is easily accessible by placing it at the most accessible point. The position of the finished goods and deciding the sequence of routes to perform the fastest loading and unloading work are critical factors in reaching maximum efficiency. This project aims to provide easy access to stored goods and minimize the travel time between the picking and the placing positions to avoid inefficient routes and disruptions by strategically planning the warehouse layout design and running each operation in the best sequential manner. © 2024 Elsevier B.V. All rights reserved.