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Yayın A DBN based reactive maintenance model for a complex system in thermal power plants(Elsevier Sci Ltd, 2019-10) Özgür Ünlüakın, Demet; Türkali, Busenur; Karacaörenli, Ayşe; Aksezer, Sezgin ÇağlarThermal power plants consist of several complex systems having many interacting hidden components. Any unexpected failure may lead to prolonged downtime and serious lost profits. Therefore, implementing an effective maintenance policy is crucial for this sector. Although preventive maintenance has become a more popular strategy, it does not completely prevent the need for corrective maintenance. Our aim in this study is to tackle the corrective maintenance implementation problem of a multi-component partially observable dynamic system based on a regenerative air heater in a thermal power plant. We propose eight methods having different efficiency measures with respect to time, effect and probability criteria to minimize the total number of maintenance activities in a given planning horizon. Performances of these methods are evaluated under corrective maintenance strategy using dynamic Bayesian networks. The results show that fault effect methods with best working state probability measure perform better than the others considering both the total amount of maintenance activities and also the solution time. We also point out how the methods can be implemented in real-life and how the results can be used for requirements planning. Furthermore, the proposed methods can be used for the corrective maintenance of all systems having hidden interacting components.Yayın A mathematical model for perishable products with price- and displayed-stock-dependent demand(Elsevier Ltd, 2016-12) Önal, Mehmet; Yenipazarlı, Arda; Kundakçıoğlu, Ömer ErhunWe introduce an economic order quantity model that incorporates product assortment, pricing and space-allocation decisions for a group of perishable products. The goal is to maximize the retailer's profit under shelf-space and backroom storage capacity constraints. We assume that the demand rate of a product is a function of the selling prices and the displayed stock levels of all the products in the assortment. We propose a Tabu Search based heuristic method to solve this complex problem.Yayın Power control for fading cooperative multiple access channels(IEEE, 2007-08) Kaya, Onur; Ulukuş, ŞennurFor a fading Gaussian multiple access channel with user cooperation, we obtain the power allocation policies that maximize the average rates achievable by block Markov superposition coding, subject to average power constraints. The optimal policies result in a coding scheme that is simpler than the one for a general multiple access channel with generalized feedback. This simpler coding scheme also leads to the possibility of formulating an otherwise non-concave optimization problem as a concave one. Using the perfect channel state information available at the transmitters to adapt the powers, we demonstrate gains over the achievable rates for existing cooperative systems.Yayın Hybrid high dimensional model representation (HHDMR) on the partitioned data(Elsevier B.V., 2006-01-01) Tunga, Mehmet Alper; Demiralp, MetinA multivariate interpolation problem is generally constructed for appropriate determination of a multivariate function whose values are given at a finite number of nodes of a multivariate grid. One way to construct the solution of this problem is to partition the given multivariate data into low-variate data. High dimensional model representation (HDMR) and generalized high dimensional model representation (GHDMR) methods are used to make this partitioning. Using the components of the HDMR or the GHDMR expansions the multivariate data can be partitioned. When a cartesian product set in the space of the independent variables is given, the HDMR expansion is used. On the other band, if the nodes are the elements of a random discrete data the GHDMR expansion is used instead of HDMR. These two expansions work well for the multivariate data that have the additive nature. If the data have multiplicative nature then factorized high dimensional model representation (FHDMR) is used. But in most cases the nature of the given multivariate data and the sought multivariate function have neither additive nor multiplicative nature. They have a hybrid nature. So, a new method is developed to obtain better results and it is called hybrid high dimensional model representation (HHDMR). This new expansion includes both the HDMR (or GHDMR) and the FHDMR expansions through a hybridity parameter. In this work, the general structure of this hybrid expansion is given. It has tried to obtain the best value for the hybridity parameter. According to this value the analytical structure of the sought multivariate function can be determined via HHDMR.Yayın On the performance of West's bubble test: A simulation approach(Elsevier science inc, 2010-12-01) Yüksel, Sadettin Aydın; Akdeniz, Levent; Altay Salih, AslıhanIn this research we examine the ability of West's bubble test [1] in detecting speculative bubbles using Brock's (1982) [2] intertemporal general equilibrium model of asset pricing as the basis for a simulation study. In this setting, (1) the economy, by construction is efficient and produces the maximally possible amount of welfare for society, and (2) asset prices reflect the utility-maximizing behavior of consumers and the profit-maximizing behavior of firms. We find that the West's bubble test flag as "bubbles" in the simulated data yet the data is produced from an economy in which markets are efficient in welfare production.Yayın BinBRO: Binary Battle Royale Optimizer algorithm(Elsevier Ltd, 2022-02-04) (Rahkar Farshi), Taymaz Akan; Agahian, Saeid; Dehkharghani, RahimStochastic methods attempt to solve problems that cannot be solved by deterministic methods with reasonable time complexity. Optimization algorithms benefit from stochastic methods; however, they do not guarantee to obtain the optimal solution. Many optimization algorithms have been proposed for solving problems with continuous nature; nevertheless, they are unable to solve discrete or binary problems. Adaptation and use of continuous optimization algorithms for solving discrete problems have gained growing popularity in recent decades. In this paper, the binary version of a recently proposed optimization algorithm, Battle Royale Optimization, which we named BinBRO, has been proposed. The proposed algorithm has been applied to two benchmark datasets: the uncapacitated facility location problem, and the maximum-cut graph problem, and has been compared with 6 other binary optimization algorithms, namely, Particle Swarm Optimization, different versions of Genetic Algorithm, and different versions of Artificial Bee Colony algorithm. The BinBRO-based algorithms could rank first among those algorithms when applying on all benchmark datasets of both problems, UFLP and Max-Cut.Yayın On the identification of microstretch elastic moduli of materials by using vibration data of plates(Pergamon-Elsevier Science LTD, 2008-06) Kırış, Ahmet; İnan, EsinIn the present work, the vibration problems of rectangular plates modeled by Eringen's microstretch theory are investigated for the identification of the upper bounds of the microstretch moduli of the plate material. The calculated frequencies of the plates are obtained by extending the Ritz method to the microstretch plates. The three dimensional (3D) vibration analysis of the plates shows that some additional frequencies occur among the classical frequencies as characterizing the microstretch effects. Then it is also observed that these additional frequencies disappear and only the classical frequencies remain with the increasing values of microstretch constants. The inverse problem is established for the identification of the upper bounds of the microstretch elastic constants as an optimization problem where an error function is minimized.Yayın Rate-distortion and complexity optimized motion estimation for H.264 video coding(IEEE-INST Electrical Electronics Engineers Inc, 2008-02) Ateş, Hasan Fehmi; Altunbaşak, Yücel11.264 video coding standard supports several inter-prediction coding modes that use macroblock (MB) partitions with variable block sizes. Rate-distortion (R-D) optimal selection of both the motion vectors (MVs) and the coding mode of each MB is essential for an H.264 encoder to achieve superior coding efficiency. Unfortunately, searching for optimal MVs of each possible subblock incurs a heavy computational cost. In this paper, in order to reduce the computational burden of integer-pel motion estimation (ME) without sacrificing from the coding performance, we propose a R-D and complexity joint optimization framework. Within this framework, we develop a simple method that determines for each MB which partitions are likely to be optimal. MV search is carried out for only the selected partitions, thus reducing the complexity of the ME step. The mode selection criteria is based on a measure of spatiotemporal activity within the MB. The procedure minimizes the coding loss at a given level of computational complexity either for the full video sequence or for each single frame. For the latter case, the algorithm provides a tight upper bound on the worst case complexity/execution time of the ME module. Simulation results show that the algorithm speeds up integer-pel ME by a factor of up to 40 with less than 0.2 dB loss in coding efficiency.Yayın Evaluation of proactive maintenance policies on a stochastically dependent hidden multi-component system using DBNs(Elsevier Ltd, 2021-07) Özgür Ünlüakın, Demet; Türkali, BusenurIn complex systems with stochastically dependent components which are not observed directly, determining an effective maintenance policy is a difficult task. In this paper, a dynamic Bayesian network based maintenance decision framework is proposed to evaluate proactive maintenance policies for such systems. Two preventive and one predictive maintenance strategies from a cost perspective are designed for multi-component dependable systems which aim to reduce maintenance cost while increasing system reliability at the same time. Tabu procedure is employed to avoid repetitive similar actions. The performances of the policies are compared with a reactive maintenance strategy and also with each other using different strategy parameters on a real life system confronted in thermal power plants for six different scenarios. The scenarios are designed considering different structures of system dependability and reactive cost. The results show that the threshold based maintenance which is the predictive strategy gives the minimum cost and maintenance number in almost all scenarios.Yayın Optimal project duration for resource leveling(Elsevier Science BV, 2018-04-16) Atan, Sabri Tankut; Eren, ElifResource leveling is important in project management as resource fluctuations are costly and undesired. Typically, schedules with better resource profiles are obtained by shifting the activities within their float times using the schedule of fixed duration found by Critical Path Method. However, if the project duration can be extended, it is plausible to find a schedule with enhanced resource leveling since a longer duration allows for more float time for all activities. In this work, we relax the assumption of fixed durations in resource leveling formulations and investigate what the minimal project duration for the best leveled schedule should be. We provide mixed-integer linear models for several leveling objectives including the Release and Rehire metric. We show that not all metrics used for leveling under fixed durations may be appropriate when the project duration becomes a decision variable. Optimal solutions from smaller problems are used to find the magnitude of the extension needed and benefits obtained thereby. Since the problem is a NP-hard problem for which exact solutions cannot be obtained for large networks in reasonable time, we provide a greedy heuristic to be used with the Release and Rehire metric. Using an iterative framework, we also test the performance of a state-of-the-art heuristic algorithm from the literature on our problem. Computational experiments indicate that the more the number of resources is increased, the less leveling benefits are gained from extending the project. The optimal project durations and extension benefits can also be significantly different for different metrics.
