Browsing by Author "Bulut, Onder"

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A Comparative Study of Artificial Intelligence Based Methods for Abnormal Pattern Identification in SPC
(SPRINGER INTERNATIONAL PUBLISHING AG, 2022) Umut Avci; Onder Bulut; Ayhan Ozgur Toy; Toy, Ayhan Ozgur; Bulut, Onder; Avci, Umut; C Kahraman; AC Tolga; SC Onar; S Cebi; B Oztaysi; IU Sari
Statistical process control techniques have been used to detect any assignable cause that may result in a lower quality. Among these techniques is the identification of any abnormal patterns that may indicate the presence of an assignable cause. These abnormal patterns may be in the form of steady movement in one direction i.e. trends, an instantaneous change in the process mean i.e. sudden shift, a series of high observations followed by a series of low observations i.e. cycles. As long as we can classify the observed data the decision maker can decide on actions to be performed to ensure quality standards and planning for interventions. In identification of these abnormal patterns rather than relying on human element intelligent tools have been proposed in the literature. We attempt to provide a comparative study of various classification algorithms used for pattern identification in statistical process control. We specifically consider six different types of patterns to classify. These different types are: (1) Normal (2) Upward trend (3) Downward trend (4) Upward shift (5) Downward shift (6) Cyclic. A recent trend in classification is to use deep neural networks (DNNs). However due to the design complexity of DNNs alternative classification methods should also be considered. Our focus on this study is to compare traditional classification methods to a recent DNN solution in the literature in terms of their efficiencies. Our numerical study indicates that basic classification algorithms perform relatively well in addition to their structural advantages.
Citation - WoS: 3
Citation - Scopus: 1
A differential evolution algorithm for the median cycle problem
(IEEE, 2011) M. Fatih Tasgetiren; Quanke Pan; Önder Bulut; Ponnuthurai Nagaratnam Suganthan; Tasgetiren, M. Fatih; Suganthan, P. N.; Pan, Quan-Ke; Bulut, Onder; Fadiloglu, M. Murat
This paper extends the applications of differential evolution algorithms to the Median Cycle Problem. The median cycle problem is concerned with constructing a simple cycle composed of a subset of vertices of a mixed graph. The objective is to minimize the cost of the cycle and the cost of assigning vertices not on the cycle to the nearest vertex on the cycle. A unique solution representation is presented for the differential evolution algorithm in order to solve the median cycle problem. To the best of our knowledge this is the first reported application of differential evolution algorithms to the median cycle problem in the literature. No local search is employed in order to see the performance of the pure differential evolution algorithm. The differential evolution algorithm is tested on a set of benchmark instances from the literature. For comparisons a continuous genetic algorithm is also developed. The computational results show that the differential evolution algorithm was superior to the genetic algorithm. In addition the computational results also show that the differential evolution algorithm is very promising in solving the median cycle problem when compared to the best performing algorithms from the literature. Ultimately given the fact that no local search is employed the DE algorithm was able to further improve the 5 out of 20 instances. © 2011 IEEE. © 2011 Elsevier B.V. All rights reserved.
Citation - Scopus: 8
A Differential Evolution Algorithm for the Median Cycle Problem
(IEEE, 2011) M. Fatih Tasgetiren; Quan-Ke Pan; Onder Bulut; P. N. Suganthan; Tasgetiren, M. Fatih; Bulut, Onder; Fadiloǧlu, M. Murat
This paper extends the applications of differential evolution algorithms to the Median Cycle Problem. The median cycle problem is concerned with constructing a simple cycle composed of a subset of vertices of a mixed graph. The objective is to minimize the cost of the cycle and the cost of assigning vertices not on the cycle to the nearest vertex on the cycle. A unique solution representation is presented for the differential evolution algorithm in order to solve the median cycle problem. To the best of our knowledge this is the first reported application of differential evolution algorithms to the median cycle problem in the literature. No local search is employed in order to see the performance of the pure differential evolution algorithm. The differential evolution algorithm is tested on a set of benchmark instances from the literature. For comparisons a continuous genetic algorithm is also developed. The computational results show that the differential evolution algorithm was superior to the genetic algorithm. In addition the computational results also show that the differential evolution algorithm is very promising in solving the median cycle problem when compared to the best performing algorithms from the literature. Ultimately given the fact that no local search is employed the DE algorithm was able to further improve the 5 out of 20 instances.
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.
Citation - WoS: 4
Citation - Scopus: 15
A Discrete Artificial Bee Colony Algorithm For the Economic Lot Scheduling Problem
(IEEE, 2011) M. Fatih Tasgetiren; Onder Bulut; M. Murat Fadiloglu; Tasgetiren, M. Fatih; Bulut, Onder; Fadiloglu, M. Murat
In this study we present a discrete artificial bee colony (DABC) algorithm to solve the economic lot scheduling problem (ELSP) under extended basic period (EBP) approach and power-of-two (PoT) policy. In specific our algorithm provides a cyclic production schedule of n items to be produced on a single machine such that the production cycle of each item is an integer multiple of a fundamental cycle. All the integer multipliers are in the form of power-of-two and under EBP approach feasibility is guaranteed with a constraint that checks if the items assigned in each period can be produced within the length of the period. For this problem which is NP-hard our DABC algorithm employs a multi-chromosome solution representation to encode power-of-two multipliers and the production positions separately. Both feasible and infeasible solutions are maintained in the population through the use of some sophisticated constraint handling methods. A variable neighborhood search (VNS) algorithm is also fused into DABC algorithm to further enhance the solution quality. The experimental results show that the proposed algorithm is very competitive to the best performing algorithms from the existing literature under the EBP and PoT policy.
Citation - Scopus: 5
A discrete harmony search algorithm for the economic lot scheduling problem with power of two policy
(IEEE, 2012) M. Fatih Tasgetiren; Önder Bulut; Mehmet Murat Fadiloglu; Murat Fadiloglu, M.; Tasgetiren, M. Fatih; Bulut, Onder; Fadiloglu, M. Murat
In this paper we present a problem specific discrete harmony search (DHS) algorithms to solve the economic lot scheduling problem (ELSP) under the extended basic period (EBP) approach and power-of-two (PoT) policy. In particular DHS algorithms generate a cyclic production schedule consisting of n items to be produced on a single machine where the production cycle of each item is an integer multiple of a fundamental cycle. All the integer multipliers take the form of PoT which restricts the search space but provides good solution qualities. Under the EBP approach feasibility is guaranteed with a constraint checking whether or not the items assigned in each period can be produced within the length of the period. For this restricted problem which is still NP-hard the proposed DHS algorithms employ a multi-chromosome solution representation to encode power-of-two multipliers and the production positions separately. Both feasible and infeasible solutions are maintained in the population through the use of some sophisticated constraint handling methods. A variable neighborhood search (VNS) algorithm is also hybridized with DHS algorithms to further enhance the solution quality. The experimental results show that the proposed algorithms are very competitive to the best performing algorithms from the existing literature under the EBP and PoT policy. © 2012 IEEE. © 2012 Elsevier B.V. All rights reserved.
Citation - WoS: 5
Citation - Scopus: 8
A Dynamic Berth Allocation Problem with Priority Considerations under Stochastic Nature
(SPRINGER-VERLAG BERLIN, 2012) Evrim Ursavas Guldogan; Onder Bulut; M. Fatih Tasgetiren; Tasgetiren, M. Fatih; Guldogan, Evrim Ursavas; Bulut, Onder; D Huang; Y Gan; P Gupta; MM Gromiha
Stochastic nature of vessel arrivals and handling times adds to the complexity of the well-known NP-hard berth allocation problem. To aid real decision-making under customer differentiations a dynamic stochastic model designed to reflect different levels of vessel priorities is put forward. For exponential interarrival and handling times a recursive procedure to calculate the objective function value is proposed. To reveal the characteristics of the model numerical experiments based on heuristic approaches are conducted. Solution procedures based on artificial bee colony and genetic algorithms covering both global and local search features are launched to improve the solution quality. The practical inferences led by these approaches are shown to be helpful for container terminals faced with multifaceted priority considerations.
Citation - WoS: 4
Citation - Scopus: 5
A Framework for Capacity Expansion Planning in Failure-Prone Flow-Networks via Systemic Risk Analysis
(Institute of Electrical and Electronics Engineers Inc., 2022) Nazlı Karatas Aygün; Önder Bulut; Emrah Biyik; Aygun, Nazl Karatas; Bulut, Onder; Biyik, Emrah
In this article a capacity expansion framework is proposed for failure-prone flow-networks. A systemic risk measure that quantifies the risk of unsatisfied demand due to cascaded edge failures is considered. To minimize the total cost of additional edge capacities while keeping the risk of unsatisfied demand below a certain threshold a general stochastic optimization problem is formulated. The distribution of unsatisfied demand is calculated via Monte-Carlo simulations embodied within a grid search algorithm that identifies the feasible region. Thereafter the cost-optimal edge capacity expansion plan is computed by a differential evolution algorithm. Contributions of this article are: 1) consideration of both immediate investment and future risk costs of capacity expansion plans, 2) a generic flow-network model that can be tuned for different real-life applications, 3) addressing the stochastic nature of both supply and demand simultaneously within a systemic risk framework, 4) use of eigenvector centrality for edge grouping in systemic risk analysis. An extensive numerical study is performed to investigate the effects of different edge grouping methods characteristics of stochastic components and cost parameters on the feasible region and optimal solution. The proposed framework is also demonstrated on a case study adapted from ERCOT 13-bus test system. © 2022 Elsevier B.V. All rights reserved.
Citation - Scopus: 1
A Genetic Algorithm for the Economic Lot Scheduling Problem under Extended Basic Period Approach and Power-of-Two Policy
(SPRINGER-VERLAG BERLIN, 2012) Onder Bulut; M. Fatih Tasgetiren; M. Murat Fadiloglu; Tasgetiren, M. Fatih; Bulut, Onder; Fadiloglu, M. Murat; D Huang; Y Gan; P Gupta; MM Gromiha
In this study we propose a genetic algorithm (GA) for the economic lot scheduling problem (ELSP) under extended basic period (EBP) approach and power-of-two (PoT) policy. The proposed GA employs a multi-chromosome solution representation to encode PoT multipliers and the production positions separately. Both feasible and infeasible solutions are maintained in the population through the use of some sophisticated constraint handling methods. Furthermore a variable neighborhood search (VNS) algorithm is also fused into GA to further enhance the solution quality. The experimental results show that the proposed GA is very competitive to the best performing algorithms from the existing literature under the EBP and PoT policy.
Citation - WoS: 21
Citation - Scopus: 26
An artificial bee colony algorithm for the economic lot scheduling problem
(TAYLOR & FRANCIS LTD, 2014) Onder Bulut; M. Fatih Tasgetiren; Tasgetiren, M.Fatih; Bulut, Onder
In this study we present an artificial bee colony (ABC) algorithm for the economic lot scheduling problem modelled through the extended basic period (EBP) approach. We allow both power-of-two (PoT) and non-power-of-two multipliers in the solution representation. We develop mutation strategies to generate neighbouring food sources for the ABC algorithm and these strategies are also used to develop two different variable neighbourhood search algorithms to further enhance the solution quality. Our algorithm maintains both feasible and infeasible solutions in the population through the use of some sophisticated constraint handling methods. Experimental results show that the proposed algorithm succeeds to find the all the best-known EBP solutions for the high utilisation 10-item benchmark problems and improves the best known solutions for two of the six low utilisation 10-item benchmark problems. In addition we develop a new problem instance with 50 items and run it at different utilisation levels ranging from 50 to 99% to see the effectiveness of the proposed algorithm on large instances. We show that the proposed ABC algorithm with mixed solution representation outperforms the ABC that is restricted only to PoT multipliers at almost all utilisation levels of the large instance.
Citation - WoS: 20
An embedded Markov chain approach to stock rationing
(ELSEVIER SCIENCE BV, 2010) Mehmet Murat Fadiloglu; Onder Bulut; Fadiloglu, Mehmet Murat; Bulut, Onder
We propose a new method for the analysis of lot-per-lot inventory systems with backorders under rationing. We introduce an embedded Markov chain that approximates the state-transition probabilities. We provide a recursive procedure for generating these probabilities and obtain the steady-state distribution. (C) 2010 Elsevier B.V. All rights reserved.
Analysis of M/M/s Make-to-Stock Queues with Production Start-up Costs for Both Lost Sales and Backordering Cases
(Taylor & Francis Ltd, 2025) Ozkan, Sinem; Bulut, Onder; Dincer, Mehmet Cemali
This study considers a production-inventory system with production start-up costs and parallel production lines. Production times are independent and identically distributed exponential random variables and demands are generated according to a stationary Poisson process. Production and inventory are controlled by the extended-two-critical-number policy. The system is modelled as an M/M/s make-to-stock queue and analysed for both lost sales and backordering cases. A renewal approach is developed to calculate the expected average system cost. Furthermore, an approximation is proposed to calculate the control parameters of the extended-two-critical-number policy. An extensive numerical study is conducted to illustrate the effects of changes in system parameters and the effectiveness of the proposed approximation. The analysis shows that the proposed approximation performs well across various system conditions.
Hotel overbooking capacity rationing and cooperation with third-parties: a two-period optimisation model
(INDERSCIENCE ENTERPRISES LTD, 2024) Nazli Karatas Aygun; Onder Bulut; Aygun, Nazli Karatas; Bulut, Onder
We propose a two-period optimisation model for a hotel revenue management (RM) problem where overbooking capacity rationing and cooperation with third-party websites are simultaneously considered. In a Stackelberg game structure the hotel first sets the price and overbooking and rationing levels and as the followers third-parties decide their effort levels by a Nash game. The proposed model is solved using a genetic algorithm. An extensive numerical study is performed to investigate the effects of multiple night stays hotel effort level and hotel capacity on the decisions and the hotel profit. It is shown that the value of capacity rationing increases with multiple night stays and the expected profit of the third-parties is decreasing with the hotel effort level but the relation between the hotel effort level and profit is not monotone. As the hotel capacity is expanded the effort level of the third-parties and the hotel profit increase. [Received: 1 December 2021, Accepted: 26 May 2023]
Citation - WoS: 2
Citation - Scopus: 3
Markovian Decision Process Modeling Approach for Intervention Planning of Partially Observable Systems Prone to Failures
(Springer Science and Business Media Deutschland GmbH, 2022) Oktay Karabağ; Önder Bulut; Ayhan Özgür Toy; Toy, Ayhan Ozgur; Karabag, Oktay; Bulut, Onder; C. Kahraman , S. Cevik Onar , B. Oztaysi , I.U. Sari , A.C. Tolga , S. Cebi
In this work we consider a system which gradually deteriorates over time. The system is fully functional in the beginning. Over time the system eventually becomes malfunctional. Once malfunctional the system must be replaced with a (new) fully functional system. There is a cost associated with this system replacement. However there is an option of repair/correction of partially deteriorated system at a lower cost. Once replaced or repaired/corrected the system is as good as new. The information about the deterioration level of the system is monitored through signals which provide only partial information. These signals are based on classification of intelligent sensors for deterioration monitoring. Signals are received as green yellow or red. The green signal indicates a system in a condition from fully functional to a predefined level of partially deteriorated system, the yellow signal indicates a system in a condition from the predefined level of partially deteriorated system to malfunctional system, finally the red signal indicates a malfunctional system. We model this system as a discrete time Markovian decision process and solve it through Linear Programming. Our work herein comprises model development and extensive numerical studies for impact of system parameters on the maintenance decisions and costs. © 2022 Elsevier B.V. All rights reserved.
Stochastic Microgrid Control Problems: Effects of Load Distribution and Planning Horizon
(IEEE, 2019) Aysegul Kahraman; Onder Bulut; Emrah Biyik; Cuneyt Guzelis; Gokhan Demirkiran; Demirkiran, Gokhan; Guzelis, Cuneyt; Kahraman, Aysegul; Bulut, Onder; Biyik, Emrah
Microgrids enable the integration of distributed energy resources with high renewable penetration into the main power grid. In this study a microgrid problem that takes into account the stochastic nature of the net load defined as the difference between actual demand and renewable generation is studied. The problem is formulated as a Mixed Integer Linear Stochastic Optimization Programming and is solved under different net load distributions and planning horizons. Numerical results show that increasing variance causes a rise in total system cost for the approach that solves the stochastic problem by ignoring randomness (as in most real-life applications) as well as for the one that solves the problem with the true distribution. It is observed that enlarging the planning horizon also has similar effects.