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Bazı Temel İnşaat Faaliyetlerinin Olasılık Dağılımlarının Alınmasına İlişkin Örnek Olay

Yıl 2020, Ejosat Özel Sayı 2020 (ARACONF), 137 - 143, 01.04.2020
https://doi.org/10.31590/ejosat.araconf18

Öz

‘Yönetim’ başlığı altında, zaman, para, insan gücü, makine ve ekipman ve bunların etkileşimleri gibi birçok şey eşzamanlı olarak ele alınmalıdır. Bu değerlendirmeden sonra, iş akışı, zamanlama, kaynak kullanımını gibi yönetimi etkileyen faktörleri geliştirmek amacıyla bahsedilen yönetim başlığı altındaki bileşenlerin iyileştirilmesi gerekmektedir. Diğer taraftan bakıldığında pek çok paydaşı olan, uzun tamamlanma süreleri sözkonusu olan ve yüksek oranda insan emeğine bağımlı olan inşaat sektörünün belirsiz ortamında bunu gerçekleştirmek kolay değildir. Bu belirsizliği modellemek gerekir. Bu bağlamdaki en önemli faktör, bu modeller için olasılık verilerine ihtiyacımız vardır. Olasılık verilerimize en uygun dağılıma göre, bu verilerden gerçek zamanlı veriler elde ederiz. Bu çalışmada 13 kattan oluşan bir konut projesi analiz edilmiş, bu katların her biri için 20 faaliyetin tamamlanma süresi verilmiş ve her katta gerçekleştirilen her faaliyetin süresinin dağılımı elde edilmiştir. Bu faaliyetler aynı ekip tarafından ve aynı ekipmanla 13 kez tekrarlanır. Sonuçlara göre; faaliyetler çoğunlukla Lojistik, Lojistik Lojistik, Lognormal ve Weibull dağılımına uygun sonuç vermiştir. Faaliyetler için hangi dağılımın verilerimize en uygun olduğunu belirledikten sonra, gerçek zamanlı veriler bu dağılıma göre ayarlanır ve bunun sonucunda ilgilli binada bu faaliyetinin ortalama tamamlanma süresi elde edilir. Her faaliyetin ortalama tamamlanma süresine bağlı olarak, tüm binanın tamamlanma süresi elde edilmiştir. Amaç, gerçek bir projeden alınan stokastik verileri gelecekte farklı yönetim aşamasında kullanmaktır. Çünkü çok katlı binalardan oluşan karmaşık yapılar üzerinde çalışmalar yapıldığında, proje yönetiminin tüm faaliyetlerin zaman verilerini kullanarak projenin tamamlanma süresini elde etmesi çok yararlı olacaktır. Özetle; zaman verilerine parametrik testler uygulanacaktır. Her kat için tüm katsayıların zamanlarının dağılımları incelenecek ve sonuçlar belirlenecek ve elde edilen sonuçlar hakkında gerekli yorumlar yapılacaktır. Ayrıca, örnek sayısı artırılacak ve ortaya çıkan dağılımlardaki sapmalar ve nedenleri incelenecektir.

Kaynakça

  • Avenal, A. (2014). Why Construction Project Management Should be included as a separate undergraduate Program and what topics should cover?. 4.PYYK, Eskişehir, 20.
  • Chen, H., Ding, G. and Zhang, J. & Qin, S. (2019). Research on priority rules for the stochastic resource constrained multi-project scheduling problem with new project arrival. Computers & Industrial Engineering, 137.
  • Gonzalez-Ruiz, J., Peña, A., Duque, E., Patiño, A., Chiclana, F. and Góngora, M. (2019). Stochastic logistic fuzzy maps for the construction of integrated multirates scenarios in the financing of infrastructure projects. Applied Soft Computing Journal, 85.
  • Ma, Z., Demeulemeester, E., He, Z. and Wang, N. (2019). A computational experiment to explore better robustness measures for project scheduling under two types of uncertain environments. Computers & Industrial Engineering, 131, 382–390.
  • Creemers, S. (2019). The preemptive stochastic resource-constrained project scheduling problem. European Journal of Operational Research, 277, 238-247.
  • Tao, S., Wu, C., Sheng, Z. and Wang, X. (2018). Stochastic Project Scheduling with Hierarchical Alternatives. Applied Mathematical Modelling, 58, 181-202.
  • Chen, Z., Demeulemeester, E., Bai, S. and Guo Y. (2018). Efficient priority rules for the stochastic resource-constrained project scheduling problem. European Journal of Operational Research, 270, 957-967.
  • Wanga, Y., He, Z., Kerkhove, L. and Vanhoucke, M. (2017). On the performance of priority rules for the stochastic resource constrained multi-project scheduling problem. Computers & Industrial Engineering,114, 223-234.
  • Ning, M., He, Z., Jia, T. and Wang, N. (2017). Metaheuristics for multi-mode cash flow balanced project scheduling with stochastic duration of activities. Automation in Construction, 81, 224–233.
  • Wood, D. A. (2017). High-level integrated deterministic, stochastic and fuzzy costduration analysis aids project planning and monitoring, focusing on uncertainties and earned value metrics. Journal of Natural Gas Science and Engineering, 37, 303-326.
  • Ke, H., Maa, W. and Chen, X. (2012). Modeling stochastic project time-cost trade-offs with time-dependent activity durations. Applied Mathematics and Computation, 218, 9462-9469.
  • Rabbani, M., Ghomi, F., Jolai, F. and Lahiji, N. S. (2007). A new heuristic for resource-constrained project scheduling in stochastic networks using critical chain concept. European Journal of Operational Research, 176, 794-808.
  • Ke, H. and Liu, B. (2005). Project scheduling problem with stochastic activity duration times. Applied Mathematics and Computation, 168, 342-353.
  • Salimi, S., Mawlana, M. and Hammad, A. (2018). Performance Analysis of Simulation-Based Optimization of Construction Projects Using High-Performance Computing. Automation in Construction, 8, 158-172.
  • Ballesteros-Pérez, P., Larsen, G. and González-Cruz, M. (2018). Do Projects Really End Late? On The Shortcomings Of The Classical Scheduling Techniques. Journal of Technology and Science Education, 8, 17-33.
  • Durucasu, H., Karamaşa, Ç., İcan, Ö., Yeşilaydın G. and Gülcan, B. (2015). Project Scheduling by means of Fuzzy CPM Method: An Implementation in Construction Sector. Ege Academic Review, 15, 449-466.
  • Bayhan, H., Kanra, Y., Demir, M., Kar, H. and Gürcanlı, G. (2016). Estimation of Production Efficiency of Work Machines at Site with Stochastic Simulation Model.4. PYYK, Eskişehir.

Case Study for Getting Probability Distributions of Some Basic Construction Activities

Yıl 2020, Ejosat Özel Sayı 2020 (ARACONF), 137 - 143, 01.04.2020
https://doi.org/10.31590/ejosat.araconf18

Öz

Under the title ‘Management’, it should be considered many things simultaneously such as time, money, manpower, machinery and equipment and their interactions. After that consideration, it should have also optimized them to improve the workflow, scheduling, resource usage etc. Well, it is not easy in the uncertain environment of construction sector, which has a many stakeholder, has long completion times and is highly dependent on the human labor. It is necessary to model this uncertainty. Most importantly, we need the probability data for these models. In this study a housing project consisting of 13 floors was analyzed, the completion times of 20 activities are given for each of these floors and the distribution of the duration of each activity implemented on each floor was obtained. Those activities are repeated 13 times by the same team and with the same equipment. According to that results, activities mainly fit the Logistic, Log Logistic, Lognormal and Weibull distribution. After determining that which distribution for the activities is best suited to our data, the real time data is adjusted to this distribution and the average completion time of this activity for this building is obtained. Based on the average completion time of each activity, the completion time of the entire building was obtained.
The aim was getting the stochastic data from a real project to use in different management stage in future. Besides, the number of samples will also be increased and the deviations in the resulting distributions and their causes will be examined.

Kaynakça

  • Avenal, A. (2014). Why Construction Project Management Should be included as a separate undergraduate Program and what topics should cover?. 4.PYYK, Eskişehir, 20.
  • Chen, H., Ding, G. and Zhang, J. & Qin, S. (2019). Research on priority rules for the stochastic resource constrained multi-project scheduling problem with new project arrival. Computers & Industrial Engineering, 137.
  • Gonzalez-Ruiz, J., Peña, A., Duque, E., Patiño, A., Chiclana, F. and Góngora, M. (2019). Stochastic logistic fuzzy maps for the construction of integrated multirates scenarios in the financing of infrastructure projects. Applied Soft Computing Journal, 85.
  • Ma, Z., Demeulemeester, E., He, Z. and Wang, N. (2019). A computational experiment to explore better robustness measures for project scheduling under two types of uncertain environments. Computers & Industrial Engineering, 131, 382–390.
  • Creemers, S. (2019). The preemptive stochastic resource-constrained project scheduling problem. European Journal of Operational Research, 277, 238-247.
  • Tao, S., Wu, C., Sheng, Z. and Wang, X. (2018). Stochastic Project Scheduling with Hierarchical Alternatives. Applied Mathematical Modelling, 58, 181-202.
  • Chen, Z., Demeulemeester, E., Bai, S. and Guo Y. (2018). Efficient priority rules for the stochastic resource-constrained project scheduling problem. European Journal of Operational Research, 270, 957-967.
  • Wanga, Y., He, Z., Kerkhove, L. and Vanhoucke, M. (2017). On the performance of priority rules for the stochastic resource constrained multi-project scheduling problem. Computers & Industrial Engineering,114, 223-234.
  • Ning, M., He, Z., Jia, T. and Wang, N. (2017). Metaheuristics for multi-mode cash flow balanced project scheduling with stochastic duration of activities. Automation in Construction, 81, 224–233.
  • Wood, D. A. (2017). High-level integrated deterministic, stochastic and fuzzy costduration analysis aids project planning and monitoring, focusing on uncertainties and earned value metrics. Journal of Natural Gas Science and Engineering, 37, 303-326.
  • Ke, H., Maa, W. and Chen, X. (2012). Modeling stochastic project time-cost trade-offs with time-dependent activity durations. Applied Mathematics and Computation, 218, 9462-9469.
  • Rabbani, M., Ghomi, F., Jolai, F. and Lahiji, N. S. (2007). A new heuristic for resource-constrained project scheduling in stochastic networks using critical chain concept. European Journal of Operational Research, 176, 794-808.
  • Ke, H. and Liu, B. (2005). Project scheduling problem with stochastic activity duration times. Applied Mathematics and Computation, 168, 342-353.
  • Salimi, S., Mawlana, M. and Hammad, A. (2018). Performance Analysis of Simulation-Based Optimization of Construction Projects Using High-Performance Computing. Automation in Construction, 8, 158-172.
  • Ballesteros-Pérez, P., Larsen, G. and González-Cruz, M. (2018). Do Projects Really End Late? On The Shortcomings Of The Classical Scheduling Techniques. Journal of Technology and Science Education, 8, 17-33.
  • Durucasu, H., Karamaşa, Ç., İcan, Ö., Yeşilaydın G. and Gülcan, B. (2015). Project Scheduling by means of Fuzzy CPM Method: An Implementation in Construction Sector. Ege Academic Review, 15, 449-466.
  • Bayhan, H., Kanra, Y., Demir, M., Kar, H. and Gürcanlı, G. (2016). Estimation of Production Efficiency of Work Machines at Site with Stochastic Simulation Model.4. PYYK, Eskişehir.
Toplam 17 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Feyza Gürbüz 0000-0002-6327-8232

Merve Dinç Bu kişi benim 0000-0002-9873-9879

Gizem Erdinç Bu kişi benim 0000-0001-5563-7963

Yayımlanma Tarihi 1 Nisan 2020
Yayımlandığı Sayı Yıl 2020 Ejosat Özel Sayı 2020 (ARACONF)

Kaynak Göster

APA Gürbüz, F., Dinç, M., & Erdinç, G. (2020). Case Study for Getting Probability Distributions of Some Basic Construction Activities. Avrupa Bilim Ve Teknoloji Dergisi137-143. https://doi.org/10.31590/ejosat.araconf18