Fehmi Burcin Ozsoydan

Fehmi Burcin Ozsoydan
Dokuz Eylul University | DEÜ · Department of Industrial Engineering

Doctor of Philosophy

About

63
Publications
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763
Citations

Publications

Publications (63)
Article
Nature-inspired computation has enjoyed a visible position among the soft computational techniques. Flower Pollination Algorithm (FPA), which is known as one of the outstanding algorithms in this domain, has been shown to be promising in numerous publications. FPA is comprised of two main phases, which are referred to as abiotic and biotic pollinat...
Article
A considerable amount of research has been devoted to solving static optimization problems via bio-inspired metaheuristic algorithms. However, most of the algorithms assume that all problem-related data remain unchanged during the optimization process, which is not a realistic assumption. Recently, dynamic optimization problems (DOPs) grabbed remar...
Conference Paper
Artificial intelligence (AI) has become quite popular lately. Since it has numerous real-life applications varying from medicine to engineering and even to society, it has grabbed notable attention of researchers particularly in the past decades. Neural networks (NNWs), which are known to be one of the building blocks of AI, has inspired most of th...
Article
Flower Pollination Algorithm (FPA) is a bio-inspired metaheuristic that simulates pollination behavior of flowers. FPA is introduced to solve global optimization problems. Subsequently, it has been applied to a variety of problems. The present study introduces some new extensions and modifications for FPA. In this respect, first, abiotic pollinatio...
Chapter
This paper is concerned with development of a hybrid flower pollination algorithm by combining fuzzy logic and chaos theory. Flower pollination algorithm is one of the recently developed algorithms which mimics the pollination process of the flowers observed in nature. Flower pollination algorithm has two main procedures which are global and local...
Chapter
Bio-inspired algorithms are emerging soft computational techniques. They have grabbed attention of researchers from various disciplines. Cuckoo Search (CS) Algorithm, which is inspired by the breeding mechanism of fascinating cuckoo birds, a well-known bio-inspired algorithm. CS is based on the obligate brood parasitic behaviour of some cuckoo spec...
Article
Metaheuristic algorithms offer unique opportunities in problem solving. Although they do not guarantee optimality, it has been shown by numerous publications that they can achieve excellent results in acceptable time. Particularly in real-life production systems, which are mostly comprised of complex discrete optimization problems, the merit should...
Article
Full-text available
Weighted superposition attraction algorithm (WSA) is a new generation population-based metaheuristic algorithm, which has been recently proposed to solve real-valued optimization problems. Inspired by the superposition of particles principle in physics, individuals of WSA generate a superposition, which leads other agents (solution vectors). Altern...
Article
Dynamic optimization problems (DOPs) have been a subject of considerable research interest mainly due to their widespread application potential. In the literature, various mechanisms have been reported to cope with the challenges of DOPs. The proposed mechanisms have usually been adopted by well-known population-based optimization algorithms, such...
Chapter
Bio-inspired algorithms are emerging soft computational techniques. They have grabbed attention of researchers from various disciplines. One can put forward that such increasing popularity of these algorithms can be summarized in twofold: first, they can be generalized to any optimization problem, secondarily; they can achieve promising results in...
Article
This paper introduces a new binary Grey Wolf Optimization (GWO) algorithm, which is one of the recent swarm intelligence-based metaheuristic algorithms. Its various extensions have been reported in the related literature. Despite numerous successful applications in real-valued optimization problems, the canonical GWO algorithm cannot directly handl...
Article
Full-text available
Due to their unique offerings, bio-inspired algorithms have become popular in problem solving. Flower Pollination Algorithm (FPA), which is relatively a new member of this family, is shown to be one promising algorithm and this optimizer is still open to possible enhancements. One of the reasons that adds to the popularity of FPA is indeed the simp...
Conference Paper
In this study, Whale Optimization Algorithm (WOA), which is a recently proposed swarm intelligence-based algorithm, has been used to solve binary optimization problems. As the WOA algorithm has been developed for optimizing real-valued functions, binary versions of the WOA algorithm have been adopted for the binary optimization problems. The perfor...
Conference Paper
Nature-inspired techniques have caught attention in optimization. Flower Pollination Algorithm (FPA) that is a recent bio-inspired optimizer, is shown to be a promising algorithm. Except for those common parameters in all other metaheuristic algorithms, this algorithm has a unique parameter, known as switching probability, which controls the search...
Conference Paper
Particle Swarm Optimization (PSO) is one of the most popular swarm intelligence optimizers. It mimics the behaviours of bird flocks. PSO is first introduced to solve unconstrained function minimization problems. Next, it has been applied to solve various types of optimization problems. However, it is apparent from the related literature that binary...
Article
Artificial intelligence techniques bring about new opportunities in problem solving. The notion such techniques have in common is learning mechanisms that are mostly problem and environment dependent. Although optimality is not guaranteed by these techniques, they draw attention due to being able to solve challenging optimization problems efficient...
Article
Bio-inspired computation is one of the emerging soft computing techniques of the past decade. Although they do not guarantee optimality, the underlying reasons that make such algorithms become popular are indeed simplicity in implementation and being open to various improvements. Grey Wolf Optimizer (GWO), which derives inspiration from the hierarc...
Chapter
Optimizasyon problemlerinin büyük çoğunluğunda, problemin tanımlı olduğu kümenin, parametrelerinin, kısıtlarının veya değişkenlerinin önceden kesin olarak bilindiği ve değişmediği varsayılmaktadır. Ancak gerçek hayatta karşılaşılan problemlerin önemli kısmında önceden kestirilemeyen olaylar problem yapısında değişikliklere sebep olabilmekte ve buna...
Chapter
Optimizasyon problemlerinin büyük çoğunluğunda, problemin tanımlı olduğu kümenin, parametrelerinin, kısıtlarının veya değişkenlerinin önceden kesin olarak bilindiği ve değişmediği varsayılmaktadır. Ancak gerçek hayatta karşılaşılan problemlerin önemli kısmında önceden kestirilemeyen olaylar problem yapısında değişikliklere sebep olabilmekte ve buna...
Article
Full-text available
Fuzzy clustering has become an important research field in pattern recognition and data analysis. As supporting unsupervised mode of learning, fuzzy clustering brings about unique opportunities to reveal structural relationships in data. Fuzzy c-means clustering is one of the widely preferred clustering algorithms in the literature. However, fuzzy...
Article
Optimization problems have attracted attention of researchers for decades. Commonly, problem related data and problem domain are assumed to be exactly known beforehand and to remain stationary. However, numerous real life optimization problems are dynamic. In practice, unpredictable events like due date changes, arrivals of new jobs or cancellation...
Article
Swarm intelligence-based methods offer notable opportunities in problem solving. Although they do not guarantee optimality, such methods are shown to be promising particularly for non-convex and non-differentiable problem spaces. The present study proposes a simple yet effective swarm intelligence technique that is based on Genetic Algorithm and Pa...
Article
Full-text available
Particle Swarm Optimization (PSO) is a well-known swarm intelligence based algorithm that simulates the movements of school or bird flocks in problem solving. Although it is first introduced to solve unconstrained global optimization problems, there are numerous reported publications of PSO involving various types of problems. However, as one can s...
Article
Optimization in dynamic environments is a fast developing research area. Several outstanding metaheuristic algorithms were proposed to solve dynamic optimization problems (DOPs) in the past decade. However, most of the effort is devoted to real-valued DOPs. Although, great majority of real-life problems has discrete and binary spaces, research in b...
Conference Paper
Rapid growth in urban population and dramatic increase in vehicle numbers causes not only traffic jam in cities but also serious vehicle parking problem. Especially considering the limited area in highly populated cities, traditional flat systems unable to meet the demand due to large space they need. The need to utilize the scarce real estate spac...
Article
In many optimisation studies, it is assumed that problem related data does not change once the generated solution plan or schedule is currently in use. However, majority of real-life manufacturing problems are time-varying in their nature due to unpredictable events such as changes in lot sizes, fluctuating capacities of manufacturing constraints,...
Article
In the present work, a case study focusing on online and dynamic scheduling of parallel heat treatment furnaces at a real manufacturing company is presented. The problem under consideration in this study involves release times, eligibility constraints, due dates, sequence-dependent setup times due to heating up or cooling down the furnaces, breakdo...
Conference Paper
Particle Swarm Optimization (PSO) is a well-known metaheuristic algorithm mimicking the behaviors of individuals in fish or bird flocks that are seeking for food or better conditions for survival. PSO is first introduced to solve real-valued function optimization problems. Next, it is applied to various types of problems including combinatorial, di...
Conference Paper
Fuzzy clustering is one of the most significant research area in pattern recognition and data analysis fields. Fuzzy clustering gives rise to reveal structural relationships in data. In the literature, many clustering methods have been proposed under crisp and fuzzy environments. Among plethora of fuzzy clustering methods which have been proposed s...
Article
Optimization in dynamic environments is a hot research area that has attracted a notable attention in the past decade. It is clear from the dynamic optimization literature that most of the effort is devoted to continuous dynamic optimization problems although majority of the real-life problems are combinatorial. Additionally, in comparison to evol...
Conference Paper
Nowadays rapidly growth in urban population and dramatic increase in vehicle numbers cause serious vehicle parking problem in cities. Fully Automated Parking Systems (FAPS) that use robots to store vehicles vertically utilize limited areas efficiently and provide environment friendly solution. However, complexity of the system gives rise to NP-hard...
Article
Full-text available
Minimization of nonmachining time is a crucial issue for effective and profitable utilization of automatic machining centers. Most of the automatic machining centers contain an automatic tool changer (ATC) for holding multiple cutting tools. Effective operation of ATC in relation to the parts assigned for machining is critical in minimizing nonmach...
Conference Paper
In majority of the optimization problems, the problem related data as well the problem domain is assumed to be exactly known beforehand and to remain stationary throughout optimization process. However, majority of real-life problems are dynamic in their nature. Such problems with time-varying problem domain or with changing parameters are referred...
Conference Paper
Automatic tool changers (ATCs) hold multiple cutting tools, with respect to the process plan requirements of the parts. According to these plans, ATCs are rotated from one slot to another, where the next needed cutting tool is positioned. The time elapsed through this rotation is referred as ATC indexing time, which is the first part of the non-mac...
Conference Paper
Warehouses are very important and critical components in supply chains, even in the systems adopting 'zero stock' management philosophies. Particularly such philosophies introduce new challenges like tighter inventory control and shorter response time for warehouse systems under dynamic operating conditions of today’s competitive world. Therefore,...
Article
In the current work, a solution methodology which combines a meta-heuristic algorithm with an exact solution approach is presented to solve cardinality constrained portfolio optimization (CCPO) problem. The proposed method is comprised of two levels, namely, stock selection and proportion determination. In stock selection level, a greedy randomized...
Conference Paper
In most of the optimization studies, the problem related data is assumed to be exactly known beforehand and remain stationary throughout whole optimization process. However, majority of real life problems and their practical applications are dynamic in their nature due to the reasons arising from unpredictable events, such as rush orders, fluctuati...
Conference Paper
In traditional optimization problems, problem domain, constraints and problem related data are assumed to remain stationary throughout the optimization process. However, numerous real life optimization problems are indeed dynamic in their nature due to unpredictable events such as due date changes, arrival of new jobs or cancellations. In the liter...
Article
Firefly algorithm (FA) is a newer member of bio-inspired meta-heuristics, which was originally proposed to find solutions to continuous optimization problems. Popularity of FA has increased recently due to its effectiveness in handling various optimization problems. To enhance the performance of the FA even further, an adaptive FA is proposed in th...
Article
Full-text available
Artificial neural networks (ANNs) are the models of choice in many data classification tasks. In this study, ANN classification models were used to explore user perceptions about kitchen faucet styles and investigate the relations between the overall preferences and kansei word scores of users. The scores given by consumers were obtained via a two-...
Article
Optimisation of automatic tool changer (ATC) indexing problem, where cutting tools are allocated to the stations on a turret magazine of a CNC machine, is one of the challenging problems in machining. The aim of the problem is to minimise the total indexing time of ATC. This problem becomes even more challenging if duplication of cutting tools is a...
Article
A new scenario based stochastic and possibilistic mixed integer programming model for a multi-objective closed-loop supply chain network design problem by considering financial and collection risks is proposed. Uncertainties in the form of randomness and fuzziness are handled together for a better reflection of the problem. Different risk measures...
Article
There is a wide range of publications reported in the literature, considering optimization problems where the entire problem related data remains stationary throughout optimization. However, most of the real-life problems have indeed a dynamic nature arising from the uncertainty of future events. Optimization in dynamic environments is a relatively...
Article
Cumulative capacitated vehicle routing problem (CCVRP) is an extension of the well-known capacitated vehicle routing problem, where the objective is minimization of sum of the arrival times at nodes instead of minimizing the total tour cost. This type of routing problem arises when a priority is given to customer needs or dispatching vital goods su...
Article
Full-text available
The uncapacitated warehouse location problem (UWLP) is one of the widely studied discrete location problems, in which the nodes (customers) are connected to a number (w) of warehouses in such a way that the total cost, yields from the dissimilarities (distances) and from the fixed costs of the warehouses is minimized. Despite w is considered as fix...

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Projects (3)
Archived project
Increase in the number of personal and commercial vehicles related to rapidly increasing population all over the world, reveals parking lot problems, particularly in highly populated cities. Conventional parking lots are inefficient due to the required space and are insufficient to meet the increasing demand. At this point, especially in the last two decades, Fully Automated Parking Systems (FAPS) offer more efficient and eco-friendly solutions by utilizing new generation technology that employs automatic robots for parking. Although FAPS provide many advantages for the public and citizens, there are also some operational issues that should be improved. There are a limited number of studies that deal with planning, scheduling, and control of FSPS's operations. Therefore, an academic research is required in order to increase the operational efficiency of FAPS.
Archived project
Generally in optimization problems, the values of all parameters are assumed to be exactly known beforehand and remain stationary throughout the optimization process. This might be valid for static environments; however, for most of the real life cases, those parameters as well as decision variables or constraints might change depending on the state or time. For example, through the ongoing optimization process, new arrivals or cancellations of decision variables (arrivals or cancellations of new jobs), revealing or cancellation of constraints or similarly the changes occurring on the system parameters are frequently encountered cases of real life manufacturing systems. For such cases, the aim is tracking of the moving optima rather than obtaining the optimum as in traditional static optimization problems. Such problems involving at least one of those mentioned features are called dynamic optimization problems in the literature. Because dynamic optimization is still a developing research area and it is closer to real life cases as well as it is open for further developments, it has attracted a notable attention from researchers. However, in the current circumstance, it can be said that it’s still at the theoretical stage and the reported studies focus on test problems provided within the scope of operations research literature. Those studies commonly include unconstrained problems where only continuous variables and changing parameters are handled as in moving peaks problem and time dependent function optimization problems. However, most of the real life problems can be represented by dynamic optimization problems where structural changes exist such as arrivals/cancellations of new decision variables or constraints including the changes in the domain set where the problem is defined. The significance of dynamic optimization for manufacturing systems has become a seriously studied research field and a notable increase in the number of the papers published on this field is observed particularly over the past decade. However, as a conflict, only few reported works are available yet with practical scope from diverse fields. As a result, this shows that practical studies in this field which will be introduced to the literature in the near feature, are demanded. In accordance with these thoughts, within the scope of the proposed project, meta-heuristic based dynamic optimization techniques, supported by representative problems which are thought to have real life reflections, are aimed to be developed. Distinctly from the existing literature, these techniques will be provided to perform under both structural and parametric changes. In this regard, greedy randomized adaptive search procedure (GRASP) as a constructive technique is selected as the base method for the first time in the literature. The preparatory work has demonstrated that a constructive based approach might provide crucial advantages and they might be successful on dynamic optimization problems as well. The reason why GRASP is selected as the base algorithm is clarified in details in "Method" section. From this perspective, with the techniques developed within the context of the proposed project, providing a novel contribution to the existing literature and scientific research articles publications are aimed. In addition, in order to scientifically demonstrate the possible gaining when the dynamic optimization techniques developed within the project are used in place of the traditional techniques, the effort will be focused on three diverse representative problems, which are thought to contain the majority of the characteristics of dynamism factors, encountered in dynamic optimization literature. The details related to these problems are presented in the "Issue, Context and Literature" section. Because the mentioned problems might frequently be encountered in numerous real life manufacturing and service systems, it is thought that the developed techniques will easily be extendable for further studies. In turn, this represents the common-public effect as well as practical, R&D (research and development), socio-economical and technological dimensions of the proposed project.
Archived project
In optimization problems, generally, the values of all parameters are assumed to be exactly known and remain stationary through the optimization process. This might be valid for static environments, however, for most of the real life cases, those parameters as well as decision variables or constraints might change periodically or due to unknown dynamic events. For example, through the ongoing process, new arrivals or cancellations of decision variables (arrivals or cancellations of new jobs), revealing or cancellation of constraints or similarly the changes occurring on the system parameters are frequently encountered cases of real life manufacturing systems. For such cases the aim is not only to obtain the optimum but tracking of the changing optima. Such problems involving at least one of those mentioned features are called dynamic optimization problems in the literature. Dynamic optimization topic is a developing research field over the world and the solution approaches based on computer aided dynamic optimization techniques developed through this project currently provide an on-line dynamic optimization opportunity to the firm, where the project was held.