Top PDF DP: Critical-Path Planning and Scheduling

DP: Critical-Path Planning and Scheduling

DP: Critical-Path Planning and Scheduling

In particular, the earliest project completion time for this schedule is A = tn 0 • By the nature of the job cost functions this schedule is also a minimum cost schedule for A = tn 0 • W[r]

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DP-FAIR: A Simple Model for Understanding Optimal Multiprocessor Scheduling

DP-FAIR: A Simple Model for Understanding Optimal Multiprocessor Scheduling

Subsequent improvements to LLREF have all done away with some of that algorithm’s unnecessary schedul- ing overhead. Funaoka et al. present the E-TNPA [13] and TRPA [12] algorithms which, for task sets with ∆(τ ) < m, fill the idle time in a slice with work from future slices; that is, they are work conserving (they never allow an idle processor when there’s a job available to run on it). Thus, only their slice scheduling (not their allocations) are DP- F AIR . Like LLREF, neither gives a prescription for as- signing tasks to processors, so it is difficult to gauge their real overheads. Chen et al. [8] extend the T-L Plane model to handle the extended problem of uniform multiprocessors (where processors run at different speeds, but treat all tasks uniformly). Based on this, they develop PCG, the first opti- mal scheduler for uniform multiprocessors. Funk et al. [14] extend LLREF with LRE-TL to handle sporadic as well as periodic tasks, and remove the sorting overhead from each scheduler invocation. They also extend their work to uni- form multiprocessors.
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DP: A Guide to Scheduling Good Practice

DP: A Guide to Scheduling Good Practice

• Once the float values have been calculated resource smoothing and/or levelling may be carried out to minimise resource over allocations or reduce the fluctuations in resource demand. If this process is to be done automatically, the scheduler needs to determine the processes and algorithms to be used. Most project management software packages have multiple options and settings that can have a significant impact on the resulting ‘resource levelled’ schedule. Some practitioners may be tempted to do the resource levelling manually by adjusting the logic or adding constraints to delay the start of certain activities. However, this is not a good practice as it distorts the normal scheduling calculation and can ‘build up’ problems as artificial constraints and logic are added over time.
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DP: Dynamic Scheduling

DP: Dynamic Scheduling

A properly developed CPM schedule is a ‘dynamic schedule’. However, a schedule is only dynamic if it can be easily kept up-to-date when you are busy during project execution – this is not just a fashionable term, it is critical to effective project management. So whilst by definition a CPM schedule is a dynamic model, this can only be achieved if the schedule meets the following requirements:

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Project Planning and Scheduling, the Critical Path Method Approach. BUI Power Project as a Case Study

Project Planning and Scheduling, the Critical Path Method Approach. BUI Power Project as a Case Study

planning. It is a systematic procedure for detailed project planning and control. Critical Path Method is a widely used technique for analyzing and managing sequential tasks in large projects for which the Bui dam is of no exception. Based on calculating how long it takes to complete essential steps of a process and analyzing how those steps interrelate, CPM is a visual and mathematical algorithm that gives managers the ability to effectively plan, schedule, and evaluate their projects.

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Fuzzy Critical Path With Centroidmeasure

Fuzzy Critical Path With Centroidmeasure

Time management play more significant role in project management compares to scheduling control, resource and cost management. Critical path method(CPM) is a powerful tool that is most useful in practice and is applied in the planning and control of complicated projects in real world applications. The main purpose of critical path method is to evaluating project performance and to identifying the critical activities on the critical path so that the available resources could be utilized on these activities in the project network in order to reduce project completion time. With the help of the critical path, the decision maker can adopt a better strategy of optimizing the time and the available resources to ensure the earlier completion and the quality of the project.
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INDOOR GLOBAL PATH PLANNING BASED ON CRITICAL CELLS USING DIJKSTRA ALGORITHM

INDOOR GLOBAL PATH PLANNING BASED ON CRITICAL CELLS USING DIJKSTRA ALGORITHM

2.1 A Brief Background of Load Forecasting Short-term load forecasts (STLF) are required for the control and scheduling of power systems [4]. STLF is basically aimed at predicting system load with a leading time of one hour to seven days, which is necessary for adequate scheduling and operation of power systems. STLF traditionally has been an essential component of Energy Management Systems (EMS) as it provides the input data for load flow and contingency analysis [5]. There are a large variety method have been developed for STLF load forecasting techniques grouped broadly in three major groups, traditional forecasting technique, modified traditional technique and soft computing technique [6].
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INDOOR GLOBAL PATH PLANNING BASED ON CRITICAL CELLS USING DIJKSTRA ALGORITHM

INDOOR GLOBAL PATH PLANNING BASED ON CRITICAL CELLS USING DIJKSTRA ALGORITHM

Wang et al. [S21] present an optimization model approach to reduce the cost of migration service composition. The proposed approach validation shows effectiveness and efficiency. Similarly, Ardagna et al. [S25] present a model of mixed- integer linear programming to reduce the cost and improve the performance of cloud applications during deployment. The proposed model lacks an evaluation in real cloud environments. In addition, Rack et al. [S17] propose a model to validate cost and performance (response time) to allow customers to choose migration options (resources and service providers) regarding their applications in the cloud. Perez-Palacin et al. [S14] present a model based on queuing network theory to predict and reduce the cost and response time of cloud deployment based on application logs. Gao et al. [S16] present two scheduling task models: the first model operates under deadline constraints to reduce the total cost, while the second model is based on resource constraints to minimize the completion time of application deployment. The experimental findings indicate that the proposed models can
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PP: Why Critical Path Scheduling (CPM) is Wildly Optimistic

PP: Why Critical Path Scheduling (CPM) is Wildly Optimistic

The newest idea ‘Schedule Density’ takes a different approach and can be used with any of the foregoing ideas. The key concept in schedule density is that there is no point in developing a detailed schedule that is guaranteed to be wrong. You can’t create detailed schedules for work you do not have detailed knowledge of, including knowing who will be doing the work and how effective they are. Schedule Density sets an overall time budget at ‘Low Density’ and re-plans the work in greater detail as the density is increased. The re-planning takes into account the actual situation, actual production rates and the objectives set by the Low Density schedule to encourage the proactive and effective management of time (Mosaic White Papers #6) 11 .
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CONSTRUCTION SCHEDULING PROJECT WITH TIME USING CRITICAL PATH METHOD

CONSTRUCTION SCHEDULING PROJECT WITH TIME USING CRITICAL PATH METHOD

Practice and Understanding of total project management provides better result, product, and service to the project organization. Effective project management provides balance between Scope, Time, Cost, Quality, Resource and Communication. Project management Software MS Project provides better planning, scheduling, monitoring and controlling of small as well as large projects. During the execution of a project, software is helpful for promoting effective coordination. Research and Study state that implementation of MS Project software for Time and Cost Management is proven technique. Implementation of MS Project software for construction of Buildings such as Villas, for Time and Cost Management provides effective monitor and control. For infrastructure project like construction of Buildings, implementation of MS Project software gives better schedule to control the project. Time management which gives accurate planning and scheduling of project and Cost management which gives earned value management of project. Earned Value Management gives better financial control of overall cost of the project. It can be used for measuring project performance and progress in an objective manner. Software provides effective Monitoring and Controlling through various Reports.
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Fuzzy Critical Path With  Centroidmeasure

Fuzzy Critical Path With Centroidmeasure

Time management play more significant role in project management compares to scheduling control, resource and cost management. Critical path method(CPM) is a powerful tool that is most useful in practice and is applied in the planning and control of complicated projects in real world applications. The main purpose of critical path method is to evaluating project performance and to identifying the critical activities on the critical path so that the available resources could be utilized on these activities in the project network in order to reduce project completion time. With the help of the critical path, the decision maker can adopt a better strategy of optimizing the time and the available resources to ensure the earlier completion and the quality of the project.
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Planning and Scheduling in Additive Manufacturing

Planning and Scheduling in Additive Manufacturing

Additive manufacturing has been an emerging technology for several decades, and each advancement has pro- vided new alternatives to traditional manufacturing methods for candidate geometries and applications. Mass production via additive manufacturing is now viable, yet the operations research techniques designed for tradi- tional manufacturing are not directly transferable to AM. A number of cost models for AM have been recently developed in [10] and [15], a survey of cost models was performed in [4], and various AM products have been mapped in [3]. However, only a few models for production planning of AM have been proposed. Notably, [14] has proposed a constraint optimization model on top of CPLEX, which does not take into account deadlines and approximates nesting by the total surface area. In [2], the authors implemented a model in MATLAB which takes into account nesting and scheduling metrics of earliness and tardiness, however they only consider a single machine.
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Online Budget Path Planning

Online Budget Path Planning

selected tour, we develop an application which predicts the Pre-analysis of tour budget through the mobile computing with complete requirement’s and need in real time location. App module determines the effect of personalized information and provision through smartphone on user ability to plan for any destination trips. It helps to understand the traveller perception for each and every PV system’s by real-time. The destination of trip-planning can be accessed. Through Mobile Application for pre-analysis operation over PV system’s (like train, flight, etc..) are envisioned to be analysing the budget approach for the selected tour. The data is collected through multiple set data analysis systems and compared with the exhaustive search method under the same service quality performance. In case of emergency SOS helpline are mentioned for particular places in India.
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Planning and scheduling in the 21st century

Planning and scheduling in the 21st century

Traditional planning and scheduling systems originated in the 1960s with the advent of Material Requirements Planning (MRP) which evolved into Manufacturing Resource Planning (MRP II) and finally into Enterprise Resource Planning (ERP), where a financial component was introduced. In those days the demands placed on manufacturers were very different from today. The principle objective of planning was to synchronize all levels of production backwards from the customer due date, aligning all work orders according to date order and providing a target date for bought-in purchased parts. The primary inputs to the planning process were bills of material, bills of routing orders, inventory and work-in-process.
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Planning and scheduling in the process industry

Planning and scheduling in the process industry

it was shown by various authors that LP-based list-scheduling, combined with the concept of so-called α-completion times, can lead to schedules with constant worst-case performance guarantees. The rationale behind this ap- proach is that a solution of a relaxed problem also holds valuable information to compute feasible solutions to the original, resource-constrained problem. [49] use simple list scheduling algorithms which are based on priority lists according to α-completion times of jobs in the solutions of the Lagrangian subproblems. Feasible solutions are computed in each iteration of the sub- gradient optimization. The computational experiments with this approach are very promising in terms of both computation time and solution quality. In fact, on a set of well established benchmark instances, the Lagrangian based approach provides solutions which are comparable to those of state- of-the-art algorithms from the literature. Moreover, for instances close to real-world scenarios, the results are clearly favorable to those obtained with a constraint propagation approach by [34], or a branch-and-bound algorithm by [27]. Note that the Lagrangian approach can handle many regular, and also non-regular objective functions.
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Performance comparison of path planning methods

Performance comparison of path planning methods

The paper has demonstrated the performance of a number of established and popular methods used in path planning i.e., cell decomposition (CD), voronoi diagram (VD), probabilistic roadmaps (PRMs) and visibility graph (VG). It was found that VG produced shortest path consistently. However, the computation time of VG was exponentially increased with respect to the number of obstacles. With the above-mentioned advantage, it is worth researching on VG to improve its computation time. On the other hand, VD had a consistent increment in computation time as the number of obstacles was increased. As for CD and both PRMs, the computation time were almost identical in slightly-, normally- and highly-cluttered environments. All four methods showed a minimal increase in path length as the number of obstacles increased.
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C – THETA* : PATH PLANNING ON GRIDS

C – THETA* : PATH PLANNING ON GRIDS

Sven Koenig et al. proposed Lazy Theta*[8]. In a 3D environment the average shortest paths found between a start and goal node are 13% longer than paths found in a continuous environment, as per the authors, there is a need for a smart path planning algorithm for a 3D environment [8]. Theta* an any angle path planning algorithm can be tweaked to be adapted in a 3D environment, but since Theta* will perform a line of sight check for every unexpanded neighbor of a node under consideration and in a 3D grid map environment each node will have 26 neighbors under evaluation for a line of sight check before expanding the lowest cost neighbor which would make Theta* undesirable in a 3D environment.
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Performance comparison of path planning methods

Performance comparison of path planning methods

such that the robot is free from collisions with surrounding obstacles and its planned motion satisfies the robot’s physical/kinematic constraints (Hasircioglu et al., 2008) Typically, path planning of a robot A consists of two phases. The first phase is called the pre-processing phase in which nodes and edges (lines) are built within an environment/workspace W with A and obstacles O. In this phase, it is common to apply the concept of a configuration space ( C -space) to represent A and O in W (Lozano- Perez, 1979), (Giesbrecht, 2004). In C -space, the robot’s size is reduced to a point, and the obstacles’ sizes are enlarged according to the size of A . Next, representation techniques are used to generate maps of graphs. Each technique differs in the way the nodes and edges are defined.
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PROJECT MANAGEMENT WITH SIMULATION A critical view on the critical path

PROJECT MANAGEMENT WITH SIMULATION A critical view on the critical path

Management of the project will concentrate on those activities that may still cause a late finish. These “contract critical” activities are not necessarily restricted to the critical path activities of the CPM analysis, but may be activities that were not initially critical at all or only slighty critical. The simulation detects these potentially critical activities providing the means to monitor the right activities during real project- execution.

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Autonomous robot motion path planning using shortest path planning algorithms

Autonomous robot motion path planning using shortest path planning algorithms

Autonomous robot navigation[3] has long been a goal of researchers for applications ranging from military supply convoys, to space exploration, to autonomous highway driving. A critical requirement of higher level navigation applications is that the robot has some reasonable knowledge of its current position with respect to a fixed reference frame. For example, in navigation applications that entail motion to a target position, the robot needs an accurate estimate of its current position to plan a path to the goal and to conform success. Similarly, for exploration applications, position information can be used and recorded to avoid redundant coverage. The process of position estimation with respect to a fixed reference frame is defined as the localization of the robot.
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