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The critical path method (CPM), or critical path analysis (CPA), is an algorithm for scheduling a set of project activities. [1] A critical path is determined by identifying the longest stretch of dependent activities and measuring the time [ 2 ] required to complete them from start to finish.
The program evaluation and review technique (PERT) is a statistical tool used in project management, which was designed to analyze and represent the tasks involved in completing a given project. PERT was originally developed by Charles E. Clark for the United States Navy in 1958; it is commonly used in conjunction with the Critical Path Method ...
PERT network chart for a seven-month project with five milestones (10 through 50) and six activities (A through F). work breakdown structure, A work breakdown structure (WBS), in project management is a deliverable oriented decomposition of a project into smaller components. A Gantt chart is a type of bar chart, that illustrates a project schedule.
In computer science, the analysis of algorithms is the process of finding the computational complexity of algorithms—the amount of time, storage, or other resources needed to execute them. Usually, this involves determining a function that relates the size of an algorithm's input to the number of steps it takes (its time complexity ) or the ...
There are several broadly recognized algorithmic techniques that offer a proven method or process for designing and constructing algorithms. Different techniques may be used depending on the objective, which may include searching, sorting, mathematical optimization, constraint satisfaction, categorization, analysis, and prediction.
Critical chain project management is based on methods and algorithms derived from Theory of Constraints. The idea of CCPM was introduced in 1997 in Eliyahu M. Goldratt's book, Critical Chain . The application of CCPM has been credited with achieving projects 10% to 50% faster and/or cheaper than the traditional methods (i.e., CPM, PERT, Gantt ...
In real-time computing, the worst-case execution time is often of particular concern since it is important to know how much time might be needed in the worst case to guarantee that the algorithm will always finish on time. Average performance and worst-case performance are the most used in algorithm analysis.
Predictive analytics statistical techniques include data modeling, machine learning, AI, deep learning algorithms and data mining. Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown whether it be in the past, present or future.
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