Modeling methodologies The two main modeling methodologies are: Continuous and Discreteevent. Continuous modeling is used to describe a flow of values. Discreteevent models track unique entities. The two main modeling methodologies are: Continuous and Discrete event. In addition to these main modeling methodologies, other modeling approaches are useful and usually based on one of the two main methods: Monte Carlo, Agentbased and StateAction (Imagine That Inc., 2007). To model different aspects of the real system it is possible to use different methods. However, it should be understood that there is no such thing as “the” model of a system. A system can be modeled in a number of different ways depending on what it is one want to accomplish. How the system is modeled depends on the purpose of the model and what kind of information is needed (Imagine That Inc)
Mô Phỏng Sự Kiện Rời Rạc Discrete Event Simulation LUONG DUC LONG (Ph.D, NUT JAPAN) LUONG DUC LONG (Ph.D) • Modeling methodologies The two main modeling methodologies are: Continuous and Discrete-event Continuous modeling is used to describe a flow of values Discrete-event models track unique entities The two main modeling methodologies are: Continuous and Discrete event In addition to these main modeling methodologies, other modeling approaches are useful and usually based on one of the two main methods: Monte Carlo, Agent-based and State/Action (Imagine That Inc., 2007) To model different aspects of the real system it is possible to use different methods However, it should be understood that there is no such thing as “the” model of a system A system can be modeled in a number of different ways depending on what it is one want to accomplish How the system is modeled depends on the purpose of the model and what kind of information is LUONG DUC LONG (Ph.D) needed (Imagine That Inc., 2007) • Continuous Models • In continuous models, the time step is fixed at the beginning of the simulation Time advances in equal increments and values change based directly on changes in time In this type of model, values reflect the state of the modeled system at any particular time and simulated time advances evenly from one time step to the next, see figure 3.1 Continuous simulations are comparable to a constant stream of fluid passing through a pipe The volume may increase or decrease at each time step, but the flow is continuous (Imagine That Inc., 2007) LUONG DUC LONG (Ph.D) Discrete event In discrete-event models, the system changes state as events occur and only when those events occur The mere passing of time has no direct effect on the model Unlike a continuous model, simulated time advances from one event to the next and it is unlikely that the time between events will be equal, see figure 3.2 A factory that assembles parts is a good example of a discrete event system The individual entities (parts) are assembled based on events (Imagine That Inc., 2007) LUONG DUC LONG (Ph.D) • System state variables • The system state variables are the collection of all information needed to define what is happening within the system to a sufficient level at a given point in time The determination of system state variables is a function of the purposes of the investigation So what may be the system state variables in one case may not be the same in another case even though the physical system is the same Determining the system state variables is as much an art as a science (Banks, 2000) • Having defined system state variables, a contrast can be made between discrete-event models and continuous models based on the variables needed to track the system state The system state variables in a discreteevent model remain constant over intervals of time and change value only at certain well-defined points called event times Continuous models have system state variables defined by differential or difference equations giving rise to variables that may change continuously over time (Banks, 2000) Some models are mixed discrete-event and continuous There are also continuous models that are treated as discrete-event models after some reinterpretation of system state variables, and vice versa (Banks, 2000) LUONG DUC LONG (Ph.D) LUONG DUC LONG (Ph.D) SIMULATION SYSTEMS • Several simulation systems have been designed specifically for construction (e.g., Halpin 1992, Martinez 1996) These systems use some form of network based on Activity Cycle Diagrams to represent the essentials of a model • These systems are designed for both simple (e.g., CYCLONE) and very advanced (e.g., STROBOSCOPE) modeling tasks but not satisfy the need for a very easy to learn and simple tool capable of modeling moderately complex problems with little effort EZStrobe is designed to fill this void in currently existing simulation tools and to facilitate the transition to more advanced tools (e.g STROBOSCOPE) as the system is outgrown LUONG DUC LONG (Ph.D) Các loại mô • Mô XD có nhiều loại (liên tục, rời rạc) bao gồm nhiều cấp bậc, từ Very basic đến very advance • Ví dụ: Crystal Ball dùng cho mô liên tục (chức overlay cho nhiều hình chồng lên nhau)- Bài toán nâng cao_ NETCOR_wang 200o • Ví dụ: Strobocope/ EZstrobe ngôn ngữ mô rời rạc (bài học này) LUONG DUC LONG (Ph.D) Ý nghĩa mô Mô kiện rời rạc hỗ trợ thấy loại phí phạm LUONG DUC LONG (Ph.D) • Từ đó: Cần phân biệt Khả mức độ áp dụng tiến độ: - Tiến độ phương pháp đường găng- CPM (MP 2007) - Tiến độ thi công dựa theo Mô (Strobocope), tiến độ công trường (LAST PLANNER==Lean Construction- Khai thác vấn đề DÒNG CÔNG ViỆCWORKFLOW) LUONG DUC LONG (Ph.D) 10 LUONG DUC LONG (Ph.D) 88 LUONG DUC LONG (Ph.D) 89 LUONG DUC LONG (Ph.D) 90 LUONG DUC LONG (Ph.D) 91 Red and Blue Color • Mau xanh : Bat dau • Mau do: thuc hien xong • Mau se kich hoat mau xanh LUONG DUC LONG (Ph.D) 92 LUONG DUC LONG (Ph.D) 93 LUONG DUC LONG (Ph.D) 94 LUONG DUC LONG (Ph.D) 95 QUEUE Chữ: Count nghĩa Đếm=> nói số lượng Content Queue Wait nghĩa chờ => nói Time (thời gian) tài nguyên vào Queue chờ giải phóng Dòng 2_ AveCount: Trung bình số lượng Queue theo trọng số thời gian Vd: Xe_banh_xich= 0.86 Xe_tai=0.0 752 đơn vị thời gian Số xe tải Queue thường =0 (do xe tải sử dụng), trường hợp xe Bánh xích thường có =1 ///GHI CHÚ TH dang xem xet la: Xe Tai+ Xe banh xich Dòng 3_AveWait: Thời gian chờ đợi trung bình cho Tài nguyên vào đợi (have entered) Queue (vd: Xe_tai vào Queue không Đợi nên có AveWait=0; Xe bánh xích thường phải đợi nên có Ave.Wait cao TH dang xem xet la: Xe Tai+ Xe banh xichLUONG DUC LONG (Ph.D) 96 LUONG DUC LONG (Ph.D) 97 LUONG DUC LONG (Ph.D) 98 Activity- Variable Dòng 3_AveInt: Thời gian trung bình lần thực thi công tác (thời gian trung bình lần công tác Chuyển màu xanh) LUONG DUC LONG (Ph.D) 99 LUONG DUC LONG (Ph.D) 100 LUONG DUC LONG (Ph.D) 101 LUONG DUC LONG (Ph.D) 102 ... nâng cao_ NETCOR_wang 200o • Ví dụ: Strobocope/ EZstrobe ngôn ngữ mô rời rạc (bài học này) LUONG DUC LONG (Ph.D) Ý nghĩa mô Mô kiện rời rạc hỗ trợ thấy loại phí phạm LUONG DUC LONG (Ph.D) • Từ đó:... LUONG DUC LONG (Ph.D) Các loại mô • Mô XD có nhiều loại (liên tục, rời rạc) bao gồm nhiều cấp bậc, từ Very basic đến very advance • Ví dụ: Crystal Ball dùng cho mô liên tục (chức overlay cho nhiều... theo Mô (Strobocope), tiến độ công trường (LAST PLANNER==Lean Construction- Khai thác vấn đề DÒNG CÔNG ViỆCWORKFLOW) LUONG DUC LONG (Ph.D) 10 LUONG DUC LONG (Ph.D) 11 CÁC THÀNH PHẦN MÔ PHỎNG