A simulation approach to PERT network analysis

This paper presents simulation as a useful analytical tool for project network analysis. Simulation is a powerful tool for evaluating many of the decision parameters involved in project management. A computer program, named STARC, is used to illustrate the effectiveness of computer simulation for project planning. STATGRAPHICS software is used to illustrate some of the post-simulation statistical analyses that can be conducted.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

The Application of Memetic Algorithms for Forearm Crutch Design: A Case Study

Product design has normally been performed by teams, each with expertise in a specific discipline such as material, structural, and electrical systems. Traditionally, each team would use its member\u27s experience and knowledge to develop the design sequentially. Collaborative design decisions explore the use of optimization methods to solve the design problem incorporating a number of disciplines simultaneously. It is known that such optimized product design is superior to the design found by optimizing each discipline sequentially due to the fact that it enables the exploitation of the interactions between the disciplines. In this paper, a bi-level decentralized framework based on Memetic Algorithm (MA) is proposed for collaborative design decision making using forearm crutch as the case. Two major decisions are considered: the weight and the strength. We introduce two design agents for each of the decisions. At the system level, one additional agent termed facilitator agent is created. Its main function is to locate the optimal solution for the system objective function which is derived from the Pareto concept. Thus to Pareto optimum for both weight and strength is obtained. It is demonstrated that the proposed model can converge to Pareto solutions

Acquisition Challenge: The Importance of Incompressibility in Comparing Learning Curve Models

The Department of Defense (DoD) cost estimating methodology currently employs T. P. Wrights 75-plus-year-old learning curve formula. The goal of this research was to examine alternative learning curve models and determine if a more reliable and valid cost estimation method exists, which could be incorporated within the DoD acquisition environment. This study tested three alternative learning models (the Stanford-B model, DeJong\u27s learning formula, and the S-Curve model) to compare predicted against actual costs for the F-15 A-E jet fighter platform. The results indicate that the S-Curve and DeJong models offer improvement over current estimation techniques, but more importantly and unexpectedly highlight the importance of incompressibility (the amount of a process that is automated) in learning curve estimating

A Learning Curve Model Accounting for the Flattening Effect in Production Cycles

We investigate production cost estimates to identify and model modifications to a prescribed learning curve. Our new model examines the learning rate as a decreasing function over time as opposed to a constant rate that is frequently used. The purpose of this research is to determine whether a new learning curve model could be implemented to reduce the error in cost estimates for production processes. A new model was created that mathematically allows for a “flattening effect,” which typically occurs later in the production process. This model was then compared to Wright’s learning curve, which is a popular method used by many organizations today. The results showed a statistically significant reduction in error through the measurement of the two error terms, Sum of Squared Errors and Mean Absolute Percentage Error

Cost Estimating Using a New Learning Curve Theory for Non-Constant Production Rates

Traditional learning curve theory assumes a constant learning rate regardless of the number of units produced. However, a collection of theoretical and empirical evidence indicates that learning rates decrease as more units are produced in some cases. These diminishing learning rates cause traditional learning curves to underestimate required resources, potentially resulting in cost overruns. A diminishing learning rate model, namely Boone’s learning curve, was recently developed to model this phenomenon. This research confirms that Boone’s learning curve systematically reduced error in modeling observed learning curves using production data from 169 Department of Defense end-items. However, high amounts of variability in error reduction precluded concluding the degree to which Boone’s learning curve reduced error on average. This research further justifies the necessity of a diminishing learning rate forecasting model and assesses a potential solution to model diminishing learning rates

Project Management for Research

vii,224 hlm;24,,8x16,5 cm

Visualising the Cost of Quality Investment Using Equity Breakeven Point

The fully-burdened cost of quality management programs must be viewed from a systems perspective with respect to the value impacted on the organisation. What appears to be cost-effective or cheap in the present scenario may proof to be costly (or even disastrous) in the long run of market share with respect to customer perception. This paper presents an application of the equity breakeven point technique for graphical analysis of quality investments. The mathematical derivation of the equity breakeven point indicates the time when the unpaid balance on a capital investment is equal to the cumulative equity in the investment, thereby providing an a-priori insight into how long it might take to retire an investment loan on the strength of the accrued equity. This type of knowledge is useful for negotiating the terms of acquiring and/or managing large quality-oriented projects, with expected long-term impacts. Abstract (c) 2016 Inderscience

Expert Systems Applications in Engineering and Manufacturing

429hl

Comprehensive Project Management: Intergrating Optimization Models,Management Principles,and Computers

xvii, 548 hlm.; 26 c

Project Management For Research : A Guide For Engineering And Science

224 hlm.; biblio.; indeks