Skip to main content

Research Repository

Advanced Search

Professor Ender Ozcan's Outputs (113)

Interval type-2 hesitant fuzzy set method for improving the service quality of domestic airlines in Turkey (2018)
Journal Article
Deveci, M., Özcan, E., John, R., & Öner, S. C. (2018). Interval type-2 hesitant fuzzy set method for improving the service quality of domestic airlines in Turkey. Journal of Air Transport Management, 69, https://doi.org/10.1016/j.jairtraman.2018.01.008

This study investigates the level of service quality of domestic airlines in Turkey travelling between Istanbul and London and compares those airline companies according to a set of predetermined criteria. A practical multi-criteria decision making a... Read More about Interval type-2 hesitant fuzzy set method for improving the service quality of domestic airlines in Turkey.

Proposal of a design pattern for embedding the concept of social forces in human centric simulation models (2018)
Presentation / Conference Contribution
Siebers, P.-O., Deng, Y., Thaler, J., Schnädelbach, H., & Özcan, E. (2018, March). Proposal of a design pattern for embedding the concept of social forces in human centric simulation models. Presented at Operational Research Society Simulation Workshop 2018 (SW18 )

There exist many papers that explain the social force model and its application for modelling pedestrian dynamics. None of these papers, however, explains how to implement the social force model in order to use it for systems simulation studies. In t... Read More about Proposal of a design pattern for embedding the concept of social forces in human centric simulation models.

To kit or not to kit: analysing the value of model-based kitting for additive manufacturing (2018)
Journal Article
Khajavi, S. H., Baumers, M., Holmström, J., Özcan, E., Atkin, J., Jackson, W. G., & Li, W. (2018). To kit or not to kit: analysing the value of model-based kitting for additive manufacturing. Computers in Industry, 98, https://doi.org/10.1016/j.compind.2018.01.022

The use of additive manufacturing (AM) for the production of functional parts is increasing. Thus, AM based practices that can reduce supply chain costs gain in importance. We take a forward-looking approach and study how AM can be used more effectiv... Read More about To kit or not to kit: analysing the value of model-based kitting for additive manufacturing.

A Learning Automata-Based Multiobjective Hyper-Heuristic (2017)
Journal Article
Li, W., Özcan, E., & John, R. (2019). A Learning Automata-Based Multiobjective Hyper-Heuristic. IEEE Transactions on Evolutionary Computation, 23(1), 59-73. https://doi.org/10.1109/TEVC.2017.2785346

© 1997-2012 IEEE. Metaheuristics, being tailored to each particular domain by experts, have been successfully applied to many computationally hard optimization problems. However, once implemented, their application to a new problem domain or a slight... Read More about A Learning Automata-Based Multiobjective Hyper-Heuristic.

A hyper-heuristic approach to automated generation of mutation operators for evolutionary programming (2017)
Journal Article
Hong, L., Drake, J. H., Woodward, J. R., & Özcan, E. (in press). A hyper-heuristic approach to automated generation of mutation operators for evolutionary programming. Applied Soft Computing, 62, https://doi.org/10.1016/j.asoc.2017.10.002

Evolutionary programming can solve black-box function optimisation problems by evolving a population of numerical vectors. The variation component in the evolutionary process is supplied by a mutation operator, which is typically a Gaussian, Cauchy,... Read More about A hyper-heuristic approach to automated generation of mutation operators for evolutionary programming.

Automated generation of constructive ordering heuristics for educational timetabling (2017)
Journal Article
Pillay, N., & Özcan, E. (2017). Automated generation of constructive ordering heuristics for educational timetabling. Annals of Operations Research, 275, 181-208. https://doi.org/10.1007/s10479-017-2625-x

Construction heuristics play an important role in solving combinatorial optimization problems. These heuristics are usually used to create an initial solution to the problem which is improved using optimization techniques such as metaheuristics. For... Read More about Automated generation of constructive ordering heuristics for educational timetabling.

Learning heuristic selection using a time delay neural network for open vehicle routing (2017)
Presentation / Conference Contribution
Tyasnurita, R., Özcan, E., & John, R. Learning heuristic selection using a time delay neural network for open vehicle routing. Presented at IEEE Congress on Evolutionary Computation 2017

A selection hyper-heuristic is a search method that controls a prefixed set of low-level heuristics for solving a given computationally difficult problem. This study investigates a learning-via demonstrations approach generating a selection hyper-heu... Read More about Learning heuristic selection using a time delay neural network for open vehicle routing.

Sparse, continuous policy representations for uniform online bin packing via regression of interpolants (2017)
Journal Article
Swan, J., Drake, J. H., Neumann, G., & Özcan, E. (2017). Sparse, continuous policy representations for uniform online bin packing via regression of interpolants. Lecture Notes in Artificial Intelligence, 10197, 189-200. https://doi.org/10.1007/978-3-319-55453-2_13

Online bin packing is a classic optimisation problem, widely tackled by heuristic methods. In addition to human-designed heuristic packing policies (e.g. first- or best- fit), there has been interest over the last decade in the automatic generation o... Read More about Sparse, continuous policy representations for uniform online bin packing via regression of interpolants.

Multi-objective optimisation in inventory planning with supplier selection (2017)
Journal Article
Turk, S., Özcan, E., & John, R. (2017). Multi-objective optimisation in inventory planning with supplier selection. Expert Systems with Applications, 78, https://doi.org/10.1016/j.eswa.2017.02.014

Supplier selection and inventory planning are critical and challenging tasks in Supply Chain Management. There are many studies on both topics and many solution techniques have been proposed dealing with each problem separately. In this study, we pre... Read More about Multi-objective optimisation in inventory planning with supplier selection.

Fairness in examination timetabling: student preferences and extended formulations (2017)
Journal Article
Muklason, A., Parkes, A. J., Özcan, E., McCollum, B., & McMullan, P. (2017). Fairness in examination timetabling: student preferences and extended formulations. Applied Soft Computing, 55, 302-318. https://doi.org/10.1016/j.asoc.2017.01.026

Variations of the examination timetabling problem have been investigated by the research community for more than two decades. The common characteristic between all problems is the fact that the definitions and data sets used all originate from actual... Read More about Fairness in examination timetabling: student preferences and extended formulations.

Multi-objective evolutionary algorithms and hyper-heuristics for wind farm layout optimisation (2016)
Journal Article
Li, W., Özcan, E., & John, R. (2017). Multi-objective evolutionary algorithms and hyper-heuristics for wind farm layout optimisation. Renewable Energy, 105, https://doi.org/10.1016/j.renene.2016.12.022

Wind farm layout optimisation is a challenging real-world problem which requires the discovery of trade-off solutions considering a variety of conflicting criteria, such as minimisation of the land area usage and maximisation of energy production. Ho... Read More about Multi-objective evolutionary algorithms and hyper-heuristics for wind farm layout optimisation.

An investigation of tuning a memetic algorithm for cross-domain search (2016)
Presentation / Conference Contribution
Gumus, D. B., Özcan, E., & Atkin, J. (2016, July). An investigation of tuning a memetic algorithm for cross-domain search. Presented at 2016 IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada

Memetic algorithms, which hybridise evolutionary algorithms with local search, are well-known metaheuristics for solving combinatorial optimisation problems. A common issue with the application of a memetic algorithm is determining the best initial s... Read More about An investigation of tuning a memetic algorithm for cross-domain search.

Performance of selection hyper-heuristics on the extended HyFlex domains (2016)
Presentation / Conference Contribution
Almutairi, A., Özcan, E., Kheiri, A., & Jackson, W. G. (2016, October). Performance of selection hyper-heuristics on the extended HyFlex domains. Presented at ISCIS: International Symposium on Computer and Information Sciences, Krakow, Poland

Selection hyper-heuristics perform search over the space of heuristics by mixing and controlling a predefined set of low level heuristics for solving computationally hard combinatorial optimisation problems. Being reusable methods, they are expected... Read More about Performance of selection hyper-heuristics on the extended HyFlex domains.

An analysis of the Taguchi method for tuning a memetic algorithm with reduced computational time budget (2016)
Presentation / Conference Contribution
Gümüş, D. B., Özcan, E., & Atkin, J. (2016, October). An analysis of the Taguchi method for tuning a memetic algorithm with reduced computational time budget. Presented at ISCIS: International Symposium on Computer and Information Sciences, Krakow, Poland

Determining the best initial parameter values for an algorithm, called parameter tuning, is crucial to obtaining better algorithm performance; however, it is often a time-consuming task and needs to be performed under a restricted computational budge... Read More about An analysis of the Taguchi method for tuning a memetic algorithm with reduced computational time budget.

Ensemble move acceptance in selection hyper-heuristics (2016)
Presentation / Conference Contribution
Kheiri, A., Mısır, M., & Özcan, E. (2016, October). Ensemble move acceptance in selection hyper-heuristics. Presented at ISCIS: International Symposium on Computer and Information Sciences, Kraków, Poland

Selection hyper-heuristics are high level search methodologies which control a set of low level heuristics while solving a given problem. Move acceptance is a crucial component of selection hyper-heuristics, deciding whether to accept or reject a new... Read More about Ensemble move acceptance in selection hyper-heuristics.

Combining Monte-Carlo and hyper-heuristic methods for the multi-mode resource-constrained multi-project scheduling problem (2016)
Journal Article
Asta, S., Karapetyan, D., Kheiri, A., Özcan, E., & Parkes, A. J. (2016). Combining Monte-Carlo and hyper-heuristic methods for the multi-mode resource-constrained multi-project scheduling problem. Information Sciences, 373, 476-498. https://doi.org/10.1016/j.ins.2016.09.010

Multi-mode resource and precedence-constrained project scheduling is a well-known challenging real-world optimisation problem. An important variant of the problem requires scheduling of activities for multiple projects considering availability of loc... Read More about Combining Monte-Carlo and hyper-heuristic methods for the multi-mode resource-constrained multi-project scheduling problem.

A self-adaptive multimeme memetic algorithm co-evolving utility scores to control genetic operators and their parameter settings (2016)
Journal Article
Özcan, E., Drake, J. H., Altıntaş, C., & Asta, S. (2016). A self-adaptive multimeme memetic algorithm co-evolving utility scores to control genetic operators and their parameter settings. Applied Soft Computing, 49, https://doi.org/10.1016/j.asoc.2016.07.032

Memetic algorithms are a class of well-studied metaheuristics which combine evolutionary algorithms and local search techniques. A meme represents contagious piece of information in an adaptive information sharing system. The canonical memetic algori... Read More about A self-adaptive multimeme memetic algorithm co-evolving utility scores to control genetic operators and their parameter settings.

A comparative study of fuzzy parameter control in a general purpose local search metaheuristic (2016)
Presentation / Conference Contribution
Jackson, W. G., Özcan, E., & John, R. I. (2016, July). A comparative study of fuzzy parameter control in a general purpose local search metaheuristic. Presented at 2016 IEEE Congress on Evolutionary Computation (CEC)

There is a growing number of studies on general purpose metaheuristics that are directly applicable to multiple domains. Parameter setting is a particular issue considering that many of such search methods come with a set of... Read More about A comparative study of fuzzy parameter control in a general purpose local search metaheuristic.

Automatically designing more general mutation operators of Evolutionary Programming for groups of function classes using a hyper-heuristic (2016)
Presentation / Conference Contribution
Hong, L., Drake, J. H., Woodward, J. R., & Özcan, E. (2016, July). Automatically designing more general mutation operators of Evolutionary Programming for groups of function classes using a hyper-heuristic. Presented at The Genetic and Evolutionary Computation Conference (GECCO 2016), Denver, Colorado, USA

In this study we use Genetic Programming (GP) as an offline hyper-heuristic to evolve a mutation operator for Evolutionary Programming. This is done using the Gaussian and uniform distributions as the terminal set, and arithmetic operators as the fun... Read More about Automatically designing more general mutation operators of Evolutionary Programming for groups of function classes using a hyper-heuristic.