Time cost trade off
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The document discusses the time cost trade off curve. It shows the relationship between time and cost on a project, where reducing time leads to increased costs due to inefficiencies like crashing. There is a minimum cost point where further time reductions increase costs more than the time saved.
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This chapter discusses the time-cost trade-off analysis in project management. The objective is to reduce the project duration determined by critical path analysis to meet deadlines with the least cost. Activity duration can be reduced through actions like overtime work or additional resources, but this increases direct costs from labor, equipment, and materials. There is generally a relationship between time and cost, where crashing an activity's duration increases its costs.
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Project cost and project duration are main factors in construction management. In real-life projects, both the trade-off between the project cost and the Project completion time, and the uncertainty of the environment are considerable aspects for decision-makers. Moreover, in some projects, activity durations show their complexity with time-dependence as well as randomness. Time cost trade off problem have been solved by using vast of methods such as genetic algorithms, particle swarm optimization. Most of these studies considered deterministic project values and includes CPM calculations for fitness value calculation. However most of construction projects are stochastic processes. In this paper, a stochastic time–cost trade-off problem is introduced. This study takes into account that activity duration and cost are uncertain variables. this study differs from other studies that for each fitness value calculation. The proposed model is dealt with an intelligent algorithm combining stochastic simulation and Harmony search, where stochastic simulation technique is employed to estimate random functions and Harmony search is designed to search optimal schedules under different decision-making criteria. Beside this Monte Carlo simulation is made for fitness value calculation in order to make more realistic optimization. Finally, some numerical experiments are given to illustrate the algorithm effectiveness.
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This document provides steps for crashing a project schedule to reduce its duration. It begins by explaining the two types of activity durations: normal and crash. It then outlines 9 steps to determine the maximum possible time reduction for a project: 1) draw the network diagram, 2) find the critical path using normal durations, 3) find the critical path using crash durations, 4) calculate the difference in critical paths, 5) calculate the slope for each activity, 6) select the activity with the lowest slope for crashing, 7) calculate the possible crash amount, 8) redraw the network and recalculate duration and cost, 9) compare to crashed critical path and repeat steps if different. It provides two notes: the critical path may change between
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The PMI Lebanon is glad to announce that Akram Najjar is the speaker for the a lecture titled “Using Monte Carlo Simulation in Project Estimates” delivered on Thursday, 28 July 2016
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Monte Carlo Simulation for project estimates v1.0
Using Monte Carlo Simulation in Project Estimates by Akram Najjar
The PMI Lebanon is glad to announce that Akram Najjar is the speaker for the a lecture titled “Using Monte Carlo Simulation in Project Estimates” delivered on Thursday, 28 July 2016
Lecture Outline
* Why are single point estimates unreliable and what is the alternative?
* What are distributions and how do we extract random samples from them (using Excel)? Two costing examples.
* How to setup a Monte Carlo Simulation model in a spreadsheet?
* Two PM examples (in detail)
* How to statistically analyze the thousands of runs to reach reliable estimates?
Lecture Objectives
* A Project Manager usually knows how certain parameters (such as duration, resource rates or quantities) behave. However, the PM can almost never define reliable single point estimates for these parameters. The result: many projects fail due to unreliable estimates. The alternative? The PM has to use his/her knowledge of how specific parameters behave statistically. For example, the PM knows that a specific task’s duration is distributed according to the bell shaped curve OR that another is uniformly distributed (flat variation), or triangular, or Beta-PERT, etc. The PM can then use Monte Carlo Simulation (MCS) to arrive at statistically significant and robust results. Monte Carlo Simulation (MCS) is a technique that relies on two processes. Process 1 aims at developing a spreadsheet model that calculates the critical path or the total cost, etc. The calculation is setup in a single row (or Run). This row is then duplicated a large number of times (thousands). Process 2 aims at inserting Excel functions in each of the parameters (durations, costs). In each row (or Run), such functions will provide a sample drawn from a statistical distribution that properly describes the behavior of that parameter. For example, a specific duration follows a Normal (Bell) distribution with an Average A and a Standard Deviation S. The model will then generate for each run and for that duration a different value that conforms with the bell shaped curve as defined (A and S). Each of these thousands of runs will provide the PM with a different “simulation” of the duration or the total cost, etc. By statistically analyzing the thousands of results, the PM can arrive at a robust and reliable estimate. Proprietary Add On’s for Monte Carlo Simulation in Microsoft Project are available. However, it is easy, free and more flexible to use native Microsoft functions to carry out the full simulation. The talk covered all the steps needed for such simulations giving several examples
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This document provides an overview and introduction to transportation, assignment, and transshipment models in quantitative methods and linear programming. It discusses learning objectives, outlines the topics that will be covered, and provides examples to illustrate each type of model. Specifically, it presents examples of transportation problems involving distributing goods from suppliers to warehouses, an assignment problem assigning workers to repair jobs, and a transshipment problem shipping snow blowers through distribution centers. It also introduces the transportation and assignment algorithms for solving these types of linear programs.
Monte carlo
Monte Carlo methods use random sampling to solve problems numerically. They work by setting up probabilistic models and running simulations using random numbers. This allows approximating solutions to problems in physics, finance, optimization, and other fields. Examples include estimating pi by simulating dart throws, and using a "drunken wino" random walk simulation to approximate the solution to a partial differential equation on a grid. The accuracy of Monte Carlo methods increases with more simulation iterations, requiring truly random numbers for best results.
Duality in Linear Programming
The document discusses duality theory in linear programming (LP). It explains that for every LP primal problem, there exists an associated dual problem. The primal problem aims to optimize resource allocation, while the dual problem aims to determine the appropriate valuation of resources. The relationship between primal and dual problems is fundamental to duality theory. The document provides examples of primal and dual problems and their formulations. It also outlines some general rules for constructing the dual problem from the primal, as well as relations between optimal solutions of primal and dual problems.
Human Resource Planning
Human resource planning is the process of anticipating and carrying out the movement of people into, within, and out of an organization. It ensures a continuous and proper staffing that avoids occupational imbalances. The key elements are forecasting future labor demand, analyzing current labor supply, and balancing supply and demand. The goals are to achieve optimal use of human resources and have the correct number and types of employees needed to meet organizational goals. It is a systematic and flexible continuous process that includes forecasting requirements, assessing current resources, anticipating problems, and planning programs for recruitment, selection, training, development, and utilization.
Improving Forecasts with Monte Carlo Simulations
This Slideshow contains a brief description of Monte Carlo simulation and how it can benefit financial forecasting and other business modeling.
Factors Affecting Human Resource Planning
This document discusses factors that affect human resource planning. It identifies key factors such as the type and strategy of the organization, organizational growth cycles, environmental uncertainties, the type and quality of information used, time horizons, the labor market, and outsourcing. It provides details on each factor and how they influence determining the right number and type of employees needed at different stages of an organization.
Transportation and Assignment
The document discusses transportation and assignment models in operations research. The transportation model aims to minimize the cost of distributing a product from multiple sources to multiple destinations, while satisfying supply and demand constraints. The assignment model finds optimal one-to-one matching between sources and destinations to minimize costs. Some solution methods for transportation problems include the northwest corner method, row minima method, column minima method, and least cost method. The Hungarian method is commonly used to solve assignment problems by finding the minimum cost matching.
Human resource planning & development
Human Resource Planning & Development discusses the process of human resource planning. It involves determining current and future human resource needs to achieve organizational objectives. The key aspects covered include:
- Defining human resource planning as a process of forecasting future needs and balancing supply and demand.
- The importance of aligning HR plans with organizational goals, policies, and environmental factors.
- Forecasting human resource demand and supply through various quantitative and qualitative techniques.
- Developing HR programs to address surpluses or shortages based on demand vs. supply forecasts.
- Implementing plans through recruitment, training, and other HR activities and controlling/evaluating outcomes.
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This document outlines the various elements and costs associated with planning a party including guests, name tags, seatings, supervision, coordination, communication, budget, refreshments, menu, caterer, reception, guest book, gift table, thank yous, and subcontractors with responsible budgets ranging from 1,000 to 8,000 euros.
How far down ?
The work breakdown structure (WBS) is decomposed into work packages that can be used to track project performance, with progressive elaboration allowing refinement over time as outcomes become defined and only actions remain unspecified. The document recommends decomposing the WBS to no more than 4 levels deep or 60 work packages, following the 40 hour rule where work packages should take no more than 40 hours per week to complete and remain manageable.
Different types of wbs structures
This document discusses different types of work breakdown structures that can be used for project planning and management. These include task-oriented WBS that focus on verbs and deliverables, time-phased WBS that track work in phases, and organizational or geographically-focused WBS. It also mentions cost-breakdown structures.
Kano
This document discusses Kano's model of customer satisfaction which categorizes customer needs into three types: basic needs, performance needs, and delighters. It suggests that products or services fully meeting basic needs will lead to satisfaction while not implementing them can cause dissatisfaction, and that addressing performance needs is important to keep customers satisfied but delighters are what drive excitement.
Why use a wbs?
A work breakdown structure (WBS) helps visualize and organize all tasks in a project, communicate the scope to the team and stakeholders, and identify any overlapping responsibilities to allow for precise estimation and decision making on project management.
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