Browsing by Author "Dengiz, B."

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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
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.
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
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.
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.
Using The Six Sigma To Redesign PCB Production Line
(2015) Dengiz, B.; Gulsen, M.; Pakdil, F.
Six Sigma is a methodology that includes set of analytical tools for process and product improvement. In this study we present a successful implementation of this methodology in a printed circuit board (PCB) manufacturing company. A simulation model of the production is built and the system performance of the current PCB line is analyzed for improvement. Taguchi design is used to find optimum levels of the design factors in redesigning of the PCB production system. Operation at optimum levels provides 46.6% decline in total costs.
Using The Six Sigma to Redesign Pcb Production Line
(2014) Dengiz, B.; Gulsen, M.; Pakdil, F.
Six Sigma is a methodology that includes set of analytical tools for process and product improvement. In this study we present a successful implementation of this methodology in a printed circuit board (PCB) manufacturing company. A simulation model of the production is built and the system performance of the current PCB line is analyzed for improvement. Taguchi design is used to find optimum levels of the design factors in redesigning of the PCB production system. Operation at optimum levels provides 46.6% decline in total costs.