Makale Koleksiyonu
https://hdl.handle.net/11491/3174
Article Colleciton2024-03-28T09:42:27ZShortest Confidence Intervals of Weibull Modulus for Small Samples in Materials Reliability Analysis
https://hdl.handle.net/11491/8469
Shortest Confidence Intervals of Weibull Modulus for Small Samples in Materials Reliability Analysis
Yalçınkaya, Meryem; Birgören, Burak
The Weibull distribution has been widely used to model strength properties of brittle materials. Estimation of confidence intervals for Weibull shape parameter has been an important concern, since small sample sizes in materials science experiments bring about large intervals. Many methods have been proposed in the literature for constructing shorter intervals; the methods of maximum likelihood, least square, and Menon are among the most extensively studied methods. However, they all use an equal-tails approach. The pivotal quantities used for constructing confidence intervals have right-skewed and unimodal distributions, thus, they clearly do not produce the shortest intervals for a given confidence level in equal tail form. This study constructs the shortest confidence intervals for the three aforementioned methods and compares their performances by their equal-tails counterparts. To this end, a comprehensive simulation study has been conducted for the shape parameter values between 1 to 80 and the sample sizes between 3 to 20. The comparison criterion is chosen as the expected interval length. The results show that the shortest confidence intervals in each of three methods have yielded considerably narrower intervals. Further, the unknown parameter values are more centered in these intervals.
2023-01-01T00:00:00ZDeep Learning Approach to Technician Routing and Scheduling Problem
https://hdl.handle.net/11491/8418
Deep Learning Approach to Technician Routing and Scheduling Problem
Pekel, Engin
This paper proposes a hybrid algorithm including the Adam algorithm and body change operator (BCO). Feasible solutions to technician routing and scheduling problems (TRSP) are investigated by performing deep learning based on the Adam algorithm and the hybridization of Adam-BCO. TRSP is a problem where all tasks are routed, and technicians are scheduled. In the deep learning method based on the Adam algorithm and Adam-BCO algorithm, the weights of the network are updated, and these weights are evaluated as Greedy approach, and routing and scheduling are performed. The performance of the Adam-BCO algorithm is experimentally compared with the Adam and BCO algorithm by solving the TRSP on the instances developed from the literature. The numerical results evidence that Adam-BCO offers faster and better solutions considering Adam and BCO algorithm. The average solution time increases from 0.14 minutes to 4.03 minutes, but in return, Gap decreases from 9.99% to 5.71%. The hybridization of both algorithms through deep learning provides an effective and feasible solution, as evidenced by the results.
2022-01-01T00:00:00ZKarma modelli tip-2 montaj hattı dengeleme problemi için bir kısıt programlama modeli
https://hdl.handle.net/11491/4974
Karma modelli tip-2 montaj hattı dengeleme problemi için bir kısıt programlama modeli
Alağaş, Hacı Mehmet; Pınarbaşı, Mehmet; Yüzükırmızı, Mustafa; Toklu, Bilal
Bu çalışmada karma modelli montaj hattı dengeleme problemleri için yeni bir kısıt programlama modeli sunulmuştur. Önerilen model verilen bir istasyon sayısı ile çevrim zamanını en küçüklemektedir. Önerilen model literatürdeki örnek problemler ile test edilmiştir ve modelin performansı karma modelli montaj hattı dengeleme problemlerinin matematiksel modeli ile karşılaştırmalı olarak değerlendirilmiştir. Performans kriterleri olarak ulaşılan en iyi çözüm değeri ve CPU süresi kullanılmıştır. Deneysel sonuçlar önerilen kısıt programlama modelinin problemin çözümünde iyi performans gösteren bir alternatif modelleme tekniği olduğunu göstermiştir.; This paper presents a new constraint programming model for mixed-model assembly line balancing problem. The proposed model minimizes the cycle time for a given number of stations. The proposed model is tested with literature problems and its performance is evaluated by comparing to mathematical model. Best obtained solution and elapsed CPU time are used as performance criteria. The experimental results show that the proposed constraint programming model performs well and can be used as an alternative modeling technique to solve the problem.
research
2016-01-01T00:00:00ZAnalysis of bowl effects on assembly line using queueing networks and constraint programming procedure
https://hdl.handle.net/11491/2123
Analysis of bowl effects on assembly line using queueing networks and constraint programming procedure
Pınarbaşı, Mehmet; Alağaş, Hacı Mehmet; Yüzükırmızı, Mustafa; Toklu, Bilal
In this study, a new solution procedure based on queueing networks and constraint programming is proposed to model and solve the Assembly Line Balancing Problem (ALBP). Variation of the task and the station times, and precedence relation effects are considered to evaluate the line performance. Station utilization, total average number of jobs and smoothness index are used as performance measures. Bowl effect, inverted bowl effect and variability imbalance which are seen in balanced lines are examined by using proposed procedure. Also effects of the variability on the line performance are reviewed. Literature data sets are utilized to assess the effectiveness of the procedure.
Joint International Symposium on "The Social Impacts of Developments in Information, Manufacturing and Service Systems" 44th International Conference on Computers and Industrial Engineering, CIE 2014 and 9th International Symposium on Intelligent Manufacturing and Service Systems, IMSS 2014, 14 October 2014 through 16 October 2014,
2014-01-01T00:00:00Z