Fehmi Burcin Ozsoydan

• Doctor of Philosophy
• Dokuz Eylül University

Biomass-to-bioenergy supply chain management is an integral part of the sustainable industrialization of energy conversion through biomass to bioenergy by managing economic, environmental, and social challenges encountered in each supply chain stage. Motivated by a real-world biomass-to-bioenergy supply chain network design (BSCND) problem, this st...

The present paper introduces a modified flower pollination algorithm (FPA) enhanced by evolutionary operators to solve the uncapacitated facility location problem (UFLP), which is one of the well-known location science problems. The aim in UFLP is to select some locations to open facilities among a certain number of candidate locations so as to min...

This paper proposes an improved Arithmetic Optimization Algorithm (AOA) to train artificial neural networks (ANNs) under dynamic environments. Despite many successful applications of metaheuristic training of ANNs, these studies assume static environments, which might not be realistic in real-world nonstationary processes. In this study, the traini...

As today’s one of the hottest topics, machine learning brings about opportunities in various research areas. Moreover, computational intelligence and metaheuristics open up new strategies, which are shown to be efficient in solving optimization problems. However, studies bringing such remarkable approaches together are still lacking. In this contex...

Artificial Neural Networks (ANNs) offer unique opportunities in numerous research fields. Due to their remarkable generalization capabilities, they have grabbed attention in solving challenging problems such as classification, function approximation, pattern recognition and image processing that can be quite complex to model mathematically in pract...

This paper introduces a cooperative approach of a swarm intelligence algorithm and a linear programming solver to solve the capacitated facility location problem (CFLP). Given a set of potential locations to open facilities, the aim in CFLP is to find the minimum cost, which is the sum of facility opening costs and transportation costs. The develop...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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,...

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...

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...

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...

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...

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...

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...

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...

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...

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,...

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...

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...

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...

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...

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-...

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...

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...

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...

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...

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...