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İnşaat planlamasında Monte Carlo iş programının değerlendirilmesi

Başlık çevirisi mevcut değil.

  1. Tez No: 47992
  2. Yazar: SAVAŞ BEK
  3. Danışmanlar: PROF. DR. DOĞAN SORGUÇ
  4. Tez Türü: Yüksek Lisans
  5. Konular: İnşaat Mühendisliği, Civil Engineering
  6. Anahtar Kelimeler: Belirtilmemiş.
  7. Yıl: 1995
  8. Dil: Türkçe
  9. Üniversite: İstanbul Teknik Üniversitesi
  10. Enstitü: Fen Bilimleri Enstitüsü
  11. Ana Bilim Dalı: Belirtilmemiş.
  12. Bilim Dalı: Belirtilmemiş.
  13. Sayfa Sayısı: 129

Özet

Özet Bir inşaat yatırımının planlanmasında en önemli unsur süre ve maliyettir. Zira yatırımın gerçekleşmesi için öngörülen süre ve maliyette riskler nedeniyle sapmalar ortaya çıkar. Bu sapmaları en aza indirmek Proje Yönetimi ile sağlanır. Burada, bir projenin çeşitli riskler altında süre ve maliyet değişikliklerini izleyen Primavera Systems Inc.'ın geliştirdiği, Monte Carlo yazılımı incelenmiştir. Tezin giriş bölümünde ilk olarak Monte Carlo programı diğer deterministik sistemlerle karşılaştırılmış ve bazı genel özelliklerinden bahsedilmiştir. Ortaya çıkan farkların hangi grafiklerle ve ne şekilde bastınlabildiği örnekler yardımıyla belirtilmiştir. Daha sonra probabilistik sistemlerden Monte Carlo'ya en yakın olan PERT sistemi ile bir karşılaştırma yapılmış ve bir örnek üzerinde yapılan karşılaştırmanın sonuçları gösterilmiştir. Son olarak bu tezde kullanılan uygulama projesi hakkında bilgi sunulmuştur. 2. bölüm olan Monte Carlo Programı kısmına, bu programın donanım ve teknik özellikleri, yazılımın yüklenmesi, ekran ve komut bilgileri hakkında kısa bilgiler verilerek başlanıp, her menü ve her ekranın detaylı tanıtımlanna yer verilmiştir. Rapor ve grafikler bölümünde her rapor ve grafiğin bir örneği verilmiş ve bu örnekler üzerinde açıklamalar yapılmıştır. 3. bölüm, uygulama projesine aynlmıştır. Bu bölümde, uygulama projesi için istenen rapor ve grafiklerin basımında kullanılan ekranlar, ne şekilde doldurulduğu da gösterilerek basılmıştır. 4. bölümde uygulama projesinde yapılan güncelleştirmeler karşılaştırılmış ve farklılıkların nedenleri açıklanmıştır. Bu bölümde son olarak Monte Carlo programı hakkında yorumlara yer verilmiştir. Ekler bölümünde programın kullanılmasında yardımcı olması için hazırlanan akış diyagramı, 1 Nisan 1995 ve 1 Haziran 1995 tarihli güncelleştirmelerin rapor ve grafikleri bulunmaktadır. vm

Özet (Çeviri)

Summary Uncertainty and risk are intrinsic parts of our daily life. We often deal with risk by allowing a margin of time, resources, labor, materials or funds to cover this uncertainty. For example, if you have an important meeting in the morning you leave the house earlier to reach the meeting on time in every conditions. This must be the same in project management. You develop plan and manage networks consisting of activities that occur in a world subject to unforeseen events that might affect your schedule. Recent years have seen significant overruns on large, complex projects, despite the proliferation of project management software packages. Both government and industry are defining the challenge as the effective management of cost, schedule, and technical uncertainty or risk. The advanced project risk management system softwares was developed to meet industry and government need. With these softwares, management can accomplish the following:. Develop realistic project plan, schedule, and budget targets. Determine the level of risk associated with a specific project plan, schedule, and budget. Evaluate different risk reduction measures and exercise risk control on the project. Here we will deal about Monte Carlo, which is an advanced project risk analysis and management system that enables you model many factors that may cause uncertainty and risk. Rather than except a single-point estimate of the amount of time required to perform a particular task, you want to consider the range of possible durations, each activity in the project has its own range and pattern of duration probabilities. When you compute the early and late dates of activities using the single point estimate of time, you can identify which activities are critical to the project completion schedule. Monte Carlo simulates the range estimates, identifying the activities that appear on the critical path in nearly every instance and those that appear on the critical path less often. The probabilistic planning technique is, in essence, the same as any conventional approach, in that it constructs a network of project activities and their associated logic. It differs in that during the data collection phase, great attention is paid to the quantification of the range of possible activity parameters and the factors which can influence these parameters. This variability is expressed as distribution of durations, resources, and cost ranging from the most ixoptimistic to the most pessimistic values, as well as probabilities and conditions associated with alternate logic paths. The results produced from such a model can be likened to a summary of perhaps 100 to 1000 separate CPM analyses using the random combinations of possible activity durations, resources, costs, project logic, and other external influences. Each separate analyses of the project is called an iteration. By dealing with a range of probable durations, you can estimate overall completion time for the project more accurately and with more confidence. This information enables you to predict, with a high degree of confidence, that a project will be finished by a certain date. If you consider the graphs produced by Monte Carlo, you can say, for example, that you are 93 percent certain that the project will be finished by December 1. The same considerations can be made for the costs and durations. Time OMCTCQOATt TOCTM TMOST OATf St OCC9* Engineering, Inc. Project CompteMn Oate ProbabDtfes In conventional techniques like CPM or PERT, the beginning time of an activity is assumed as the expected completion time of the latest activity. The example below shows the difference between Monte Carlo and PERT^} -0-33 - 3 Activities 1,2 and 3 are predecessors of activity 4. Activity 4 may begin only when activities 1,2 and 3 are completed. Completion Date (Workday) Act.1 Act 2.Act 3. PERT concludes that the expected start date of Activity 4 is Day 60 (the expected date of activity 2) PERT does not consider the possibility that activity 1 or 3 may complete later than activity 2. But the start date of activity 4 based on probabilistic network simulation (Monte Carlo) is illustrated in the table below. Iteration Completion Date (Workday) Latest Predecessor's Monte Carlo concludes that the expected start date of Activity 4 is day 63, the expected date of completion of the latest predecessor, which is not necessarily XIactivity 2. Monte Carlo analysis shows that activity 2 has, in fact, only a 50% probability of being critical, whereas activity 1 and 3 have a 30% probability and a 20% probability, respectively, of being critical. The failure of PERT to recognize these lesser critical paths is a major error. Such errors may occur at many nodes in a large network, adding up to a very significant overall total. Monte Carlo can model different actions at important decision points in the project. At these points, called probabilistic and conditional branches, Monte Carlo models problems and other difficult decisions. At these points the work to be performed differs according to the outcome of a test or decision. In typical critical path scheduling each activity is only performed once. In Monte Carlo, work elements can be performed always, sometimes, or almost never, depending on the outcome of some decision veriable. This modeling is not suitable for construction companies but it may be very useful for research and development projects which have decision points affecting the end of the project. One of the more misleading aspects of conventional deterministic methods is the assumption that a single critical path exists which determines the length of the project. The designated critical path then becomes the focal point for management's attempts to control the project. However, when uncertainty and activity performance variability are accounted for, a number of different network paths have some probability of becoming critical during the life of the project. Probabilistic planning departs from the deterministic concept of a single critical path and focuses on activity criticality, which is defined as the probability that a particular activity will lie on the critical path during the project. The higher the criticality value, the more important the activity with regard to overall project schedule performance. In the intruduction of the theses, Monte Carlo and other deterministic systems are compared. The differences are shown by graphics and samples. The second chapter is about the hardware and the installation of the program. Menus, screens and commands are also the subjects of the chapter. In the third chapter, there is an example case of a simulation produced by Monte Carlo. This case is the Çiğli Pomp Station Project of İdil İnşaat İn izmir. The Modeling is created by transferring the April 1st update project produced in Primavera Project Planner (P3), to Monte Carlo. The reports and graphics are produced after entering the duration range estimates of necessary activities. In order to compare the progress of the project, an other update project is produced in June 1.st- After comparing the updates, we saw that, 36 days of a gap is closed between these two updates. In the result the project cannot reach the target date yet but, the gap is decreasing. The data entry of the program which is difficult to learn and use, must be prepared by experienced project managers. Only the managers who know the capacity and the equipment of their company can calculate the pessimistic, most likely and optimistic durations which Monte Carlo needs for simulation. xiiAnd also the updates of the project must be simulated by the project management sections formed by project managers in the company, not the adviser companies. The most important problem of Monte Carlo is about the relationships of activities. All the relationships except the Finish-to-Start relationship needs calculations for data entry. When transferring a project from Primavera the relationships are transferred with a special format by using temporary activities. Thus, using only the Monte Carlo alone is not efficient, projects must be transferred from Primavera. The future versions of Primavera, can be expectedly consist the Monte Carlo as a section. xiu

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