Mühendislik Fakültesi / Faculty of Engineering

Permanent URI for this collectionhttps://hdl.handle.net/11727/1401

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Yöneylem araştırmasının yöntembilimi
(1985) Kara, İmdat
Efficient Optimization of All-terminal Reliable Networks, Using an Evolutionary Approach (vol 46, pg 18, 1997)
(1997) Dengiz, B.; Altiparmak, F.; Smith, AE; AAF-7020-2021
Simulation Optimization Based DSS Application: A Diamond Tool Production Line in İndustry
(2006) Dengiz, B.; Bektas, T; Ultanir, AE; 0000-0003-0634-144X
A diamond tool manufacturing system simulation is developed to predict the number of machines and the number of workers necessary to maintain desired levels of production for a company in Ankara, Turkey. The current manufacturing system is analysed by a simulation model emphasizing the bottlenecks and the poorly utilized machines. Validated simulation outputs are collected and used to build a multiple regression meta-model as a simulation optimization based decision support system (DSS). The proposed DSS involves analysis and evaluation of the system's behaviour through the use of a meta-model with an integrated optimization module. It enables the decision maker to perform sensitivity analysis by considering several combinations of decision variables. The aim of this study is two fold. The first is to represent a simulation optimization based DSS application for a real system by considering all the required steps. The second is to analyse the performance of the current production system and determine the optimum working conditions by simulation with greatly reduced cost, time, and effort. (c) 2005 Elsevier B.V. All rights reserved.
Buffer Allocation and Performance Modeling in Asynchronous Assembly System Operations: An Artificial Neural Network Metamodeling Approach
(2007) Altiparmak, Fulya; Dengiz, Berna; Bulgak, Akif A.; 0000-0003-1730-4214; AAF-7020-2021
This article investigates metamodeling opportunities in buffer allocation and performance modeling in asynchronous assembly systems ( AAS). Practical challenges to properly design these complex systems are emphasized. A critical review of various approaches in modeling and evaluation of assembly systems reported in the recently published literature, with a special emphasis on the buffer allocation problems, is given. Various applications of artificial intelligence techniques on manufacturing systems problems, particularly those related to artificial neural networks, are also reviewed. Advantages and the drawbacks of the metamodeling approach are discussed. In this context, a metamodeling application on AAS buffer design/performance modeling problems in an attempt to extend the application domain of metamodeling approach to manufacturing/assembly systems is presented. An artificial neural network ( ANN) metamodel is developed for a simulation model of an AAS. The ANN and regression metamodels for each AAS are compared with respect to their deviations from the simulation results. The analysis shows that the ANN metamodels can successfully be used to model of AASs. Consequently, one concludes that practising engineers involved in assembly system design can potentially benefit from the advantages of the metamodeling approach. (c) 2006 Elsevier B. V. All rights reserved.
Aintshop Production Line Optimization Using Response Surface Methodology
(2007) Dengiz, Berna; Belgin, Onder; 0000-0001-6702-2608; K-1080-2019
This paper deals with the problem of determining the optimum number of workstations to be used in parallel and workers at some stations using simulation optimization approach in a paint shop line of an automotive factory in Ankara, Turkey. In the optimization stage of the study Response Surface Methodology (RSM) is used to find the optimum levels of considered factors. Simulation model and optimization stage integration is used both to analyse the performance of the current paint shop line and determine the optimum working conditions, respectively, with reduced cost, time and effort.
A hybrid ant colony optimization approach for the design of reliable networks
(2007) Dengiz, B.; Altiparmak, F.; Belgin, O.; 0000-0003-1730-4214; 0000-0001-6702-2608; AAF-7020-2021; K-1080-2019
This paper presents a new solution approach, which is a hybridization of ant colony optimization (ACO) and simulated annealing (SA), called (h_ACO) to design of communication networks. The design problem is to find the optimal network topology where total cost is minimum and all-terminal reliability is not less-than a given level of reliability. The effectiveness of the h_ACO is investigated comparing its results with those obtained by SA and ACO, which are basic forms of the h_ACO, and also GAs given in the literature for the design problem. Computational results show that the h_ACO is an effective heuristic approach to design of reliable networks.
An International Facility Design Project
(2008) Lacksonen, Thomas; Dengiz, Berna
This paper describes an international facilities design project for Manufacturing and Industrial Engineering students. American and Turkish engineering students collaborated to create and implement the re-design of a Turkish wheelchair manufacturing facility. The company needed engineering assistance to improve the efficiency and increase the capacity of their existing factory. Turkish Industrial Engineering students went on-site to collect data and draw the existing facility layout. American Manufacturing Engineering students analyzed the data and developed new layout designs. Four American students traveled to Turkey between semesters to implement the initial phases of their design. In the second semester, the Turkish students simulated the new layout to see the performance improvements, completing their project. Student learning outcomes were positive for both groups of students. The paper explains critical steps in identifying projects and partners. Lessons are shown about successes and shortcomings in planning, operating, and communicating with design teams across cultures.
A Hybrid Simulated Annealing For A Multi-Objective Stochastic Assembly Line Balancing Problem
(2008) Cakir, Burcin; Dengiz, Berna; Altiparmak, Fulya; Xia, GP; Deng, XQ; 0000-0003-1730-4214; AAF-7020-2021
Asssembly line balancing is the problem of assigning tasks to the workstations, while optimizing one or more objectives without violating restrictions imposed on the line. In practice, task times may be random due to the worker fatigue, low skill levels, job dissatisfaction, poorly maintained equipment, defects in raw material, etc. When stochastic task times are taken consideration in assembly lines, balancing procedure is more complex due to the probability of incompleteness of stations times in a given cycle time. In this study, a multi-objective simulated annealing algorithm (m_SAA) is proposed for single-model, stochastic assembly line balancing problem with the aim of minimizing of smoothness index and total design cost. To obtain Pareto-optimal solutions, m_SAA implements tabu list and a multinomial probability mass function approach. The effectiveness of the proposed m_SAA is comparatively investigated using another SA using weight-sum approach on the test problems. Computational results show that m_SAA with multinomial probability mass function approach is more effective than SA with weight-sum approach in terms of quality of Pareto-optimal solutions.
The Efficiency of Variance Reduction in Manufacturing and Service Systems: The Comparison of the Control Variates and Stratified Sampling
(2009) Eraslan, Erguen; Dengiz, Berna; 0000-0002-5667-0391; AAE-7165-2019
There has been a great interest in the use of variance reduction techniques (VRTs) in simulation output analysis for the purpose of improving accuracy when the performance measurements of complex production and service systems are estimated. Therefore, a simulation output analysis to improve the accuracy and reliability of the output is required. The performance measurements are required to have a narrow and strong confidence interval. For a given confidence level, a smaller confidence interval is supposed to be better than the larger one. The wide of confidence interval, determined by the half length, will depend on the variance. Generally, increased replication of the simulation model appears to have been the easiest way to reduce variance but this increases the simulation costs in complex-structured and large-sized manufacturing and service systems. Thus, VRTs are used in experiments to avoid computational cost of decision-making processes for more precise results. In this study, the effect of Control Variates (CVs) and Stratified Sampling (SS) techniques in reducing variance of the performance measurements of M/M/1 and GI/G/1 queue models is investigated considering four probability distributions utilizing randomly generated parameters for arrival and service processes. Copyright (C) 2009 E. Eraslan and B. Dengiz.
A General Neural Network Model for Estimating Telecommunications Network Reliability
(2009) Altiparmak, Fulya; Dengiz, Berna; Smith, Alice E.; 0000-0003-1730-4214; 0000-0001-8808-0663; AAF-7020-2021; AAK-2318-2021
This paper puts forth a new encoding method for using neural network models to estimate the reliability of telecommunications networks with identical link reliabilities. Neural estimation is computationally speedy, and can be used during network design optimization by an iterative algorithm such as tabu search, or simulated annealing. Two significant drawbacks of previous approaches to using neural networks to model system reliability are the long vector length of the inputs required to represent the network link architecture, and the specificity of the neural network model to a certain system size. Our encoding method overcomes both of these drawbacks with a compact, general set of inputs that adequately describe the likely network reliability. We computationally demonstrate both the precision of the neural network estimate of reliability, and the ability of the neural network model to generalize to a variety of network sizes, including application to three actual large scale communications networks.
Optimization of Manufacturing Systems Using A Neural Network Metamodel with A New Training Approach
(2009) Dengiz, B.; Alabas-Uslu, C.; Dengiz, O.
In this study, two manufacturing systems, a kanban-controlled system and a multi-stage, multi-server production line in a diamond tool production system, are optimized utilizing neural network metamodels (tst_NNM) trained via tabu search (TS) which was developed previously by the authors. The most widely used training algorithm for neural networks has been back propagation which is based on a gradient technique that requires significant computational effort. To deal with the major shortcomings of back propagation (BP) such as the tendency to converge to a local optimal and a slow convergence rate, the TS metaheuristic method is used for the training of artificial neural networks to improve the performance of the metamodelling approach. The metamodels are analysed based on their ability to predict simulation results versus traditional neural network metamodels that have been trained by BP algorithm (bp NNM). Computational results show that tst NNM is superior to bp NNM for both of the manufacturing systems. Journal of the Operational Research Society (2009) 60, 1191-1197. doi:10.1057/palgrave.jors.2602620 Published online 30 July 2008
Redesign Of PCB Production Lıine With Simulation And Taguchi Design
(2009) Dengiz, Berna
This paper presents the problem of determining the optimum condition of printed circuit board (PCB) manufacturing process in an electronic company in Ankara,. Turkey. In the optimization stage of the study Taguchi method is integrated with simulation model considering minimum total cost under stochastic breakdowns. Using this methodology we investigate the system performance of the current PCB line and determine the optimum working conditions with reduced cost, time and effort.
A Local Search Heuristic with Self-tuning Parameter for Permutation Flow-Shop Scheduling Problem
(2009) Dengiz, Berna; Alabas-Uslu, Cigdem; Sabuncuoglu, Ihsan
In this paper, a new local search metaheuristic is proposed for the permutation flow-shop scheduling problem. In general, metaheuristics are widely used to solve this problem due to its NP-completeness. Although these heuristics are quite effective to solve the problem, they suffer from the need to optimize parameters. The proposed heuristic, named STLS, has a single self-tuning parameter which is calculated and updated dynamically based on both the response surface information of the problem field and the performance measure of the method throughout the search process. Especially, application simplicity of the algorithm is attractive for the users. Results of the experimental study show that STLS generates high quality solutions and outperforms the basic tabu search, simulated annealing, and record-to-record travel algorithms which are well-known local search based metaheuristics.
A cross entropy approach to design of reliable networks
(2009) Dengiz, Berna; Altiparmak, Fulya; 0000-0003-1730-4214; AAF-7020-2021
One of the most important parameters determining the performance of communication networks is network reliability. The network reliability strongly depends on not only topological layout of the communication networks but also reliability and availability of the communication facilities. The selection of optimal network topology is an NP-hard problem so that computation time of enumeration-based methods grows exponentially with network size. This paper presents a new solution approach based on cross-entropy method, called NCE, to design of communication networks. The design problem is to find a network topology with minimum cost such that all-terminal reliability is not less than a given level of reliability. To investigate the effectiveness of the proposed NCE, comparisons with other heuristic approaches given in the literature for the design problem are carried out in a three-stage experimental study. Computational results show that NCE is an effective heuristic approach to design of reliable networks. (C) 2008 Elsevier B.V. All rights reserved.
A Tabu Search Algorithm for the Training of Neural Networks
(2009) Dengiz, B.; Alabas-Uslu, C.; Dengiz, O.
The most widely used training algorithm of neural networks (NNs) is back propagation ( BP), a gradient-based technique that requires significant computational effort. Metaheuristic search techniques such as genetic algorithms, tabu search (TS) and simulated annealing have been recently used to cope with major shortcomings of BP such as the tendency to converge to a local optimal and a slow convergence rate. In this paper, an efficient TS algorithm employing different strategies to provide a balance between intensification and diversification is proposed for the training of NNs. The proposed algorithm is compared with other metaheuristic techniques found in literature using published test problems, and found to outperform them in the majority of the test cases.
A Systematic Approach to Reduce Human and System-related Errors Causing Customer Dissatisfaction in a Production Environment
(2009) Pakdil, Fatma; Oezkoek, Onur; Dengiz, Berna; Kara, Imdat; Selvi, Nilay; Karg, Alper; ABH-1078-2021
In this study, a systematic methodology for business process improvement, which aims to eliminate human and system-related errors resulting in customer dissatisfaction in a production environment, is presented. The proposed methodology consists of problem identification and analysis, preventing human-related errors and system-related error steps respectively. The methodology was also implemented in a real-life organisation. Current and proposed systems are compared via a simulation model to examine the results of process improvements. The case study shows that the proposed methodology works exceedingly well and yields considerable improvement in the process under study. The most important and impressive difference of this paper from the previous literature is that process improvement needs are derived directly from customer dissatisfaction reasons and solved by the proposed systematic methodology. In this way human-related and system-related errors were perceived opportunities for improvement.
Design Of Reliable Communication Networks: A Hybrid Ant Colony Optimization Algorithm
(2010) Dengiz, Berna; Altiparmak, Fulya; Belgin, Onder; 0000-0003-1730-4214; 0000-0001-6702-2608; AAF-7020-2021; K-1080-2019
This article proposes a hybrid approach based on Ant Colony Optimization (ACO) and Simulated Annealing (SA), called ACO_SA, for the design of communication networks. The design problem is to find the optimal network topology for which the total cost is a minimum and the all-terminal reliability is not less than a given level of reliability. The proposed ACO_SA has the advantages of the ability to find higher performance solutions, created by the ACO, and the ability to jump out of local minima to find better solutions, created by the SA. The effectiveness of ACO_SA is investigated by comparing its results with those obtained by individual application of SA and ACO, which are basic forms of ACO_SA, two different genetic algorithms and a probabilistic solution discovery algorithm given in the literature for the design problem. Computational results show that ACO_SA has a better performance than its basic forms and the investigated heuristic approaches.
Women in Engineering in Turkey - A Large Scale Quantitative and Qualitative Examination
(2010) Smith, Alice; Dengiz, Berna; 0000-0001-8808-0663; AAK-2318-2021
The underrepresentation of women in engineering is well known and unresolved. However, Turkey has witnessed a shift in trend from virtually no female participation in engineering to across-the-board proportions that dominate other industrialised countries within the 76 years of the founding of the Turkish Republic. This paper describes the largest known direct cross-sectional study of women in engineering in Turkey with over 800 participants. The methods include survey and facilitated focus groups. The study shows that women in Turkey choose engineering mainly because they enjoy the underlying mathematics and science. There is no gender bias on the part of teachers or fellow students; however, women students believe that they have fewer opportunities than male peers and acutely feel the lack of role models. Working professionals in industry or government perceive that women assume a more indirect, supporting role; however, women overall strongly affirm their selection of engineering despite some negative factors.
A Self-adaptive Local Search Algorithm for the Classical Vehicle Routing Problem
(2011) Alabas-Uslu, Cigdem; Dengiz, Berna
The purpose of this study is introduction of a local search heuristic free from parameter tuning to solve classical vehicle routing problem (VRP). The VRP can be described as the problem of designing optimal delivery of routes from one depot to a number of customers under the limitations of side constraints to minimize the total traveling cost. The importance of this problem comes from practical as well as theoretical point of view. The proposed heuristic, self-adaptive local search (SALS), has one generic parameter which is learnt throughout the search process. Computational experiments confirm that SALS gives high qualified solutions to the VRP and ensures at least an average performance, in terms of efficiency and effectiveness, on the problem when compared with the recent and sophisticated approaches from the literature. The most important advantage of the proposed heuristic is the application convenience for the end-users. SALS also is flexible that can be easily applied to variations of VRP. (C) 2011 Elsevier Ltd. All rights reserved.
Multi-Objective Optimization of A Stochastic Assembly Line Balancing: A Hybrid Simulated Annealing Algorithm
(2011) Cakir, Burcin; Altiparmak, Fulya; Dengiz, Berna; 0000-0003-1730-4214; AAF-7020-2021
This paper deals with multi-objective optimization of a single-model stochastic assembly line balancing problem with parallel stations. The objectives are as follows: (1) minimization of the smoothness index and (2) minimization of the design cost. To obtain Pareto-optimal solutions for the problem, we propose a new solution algorithm, based on simulated annealing (SA), called m_SAA, m_SAA implements a multinomial probability mass function approach, tabu list, repair algorithms and a diversification strategy. The effectiveness of m_SAA is investigated comparing its results with those obtained by another SA (using a weight-sum approach) on a suite of 24 test problems. Computational results show that m_SAA with a multinomial probability mass function approach is more effective than SA with weight-sum approach in terms of the quality of Pareto-optimal solutions. Moreover, we investigate the effects of properties (i.e., the tabu list, repair algorithms and diversification strategy) on the performance of m_SAA. (C) 2010 Elsevier Ltd. All rights reserved.