Time-Cost Tradeoff Analysis in Project Management: An Ant System Approach

2011 ◽  
Vol 58 (1) ◽  
pp. 36-43 ◽  
Author(s):  
Hadi Mokhtari ◽  
Reza Baradaran Kazemzadeh ◽  
Ali Salmasnia
Keyword(s):  
Project Management ◽  
System Approach ◽  
Time Cost ◽  
Ant System ◽  
Tradeoff Analysis

Enhancing the Effect on Project Success through Information Presentation

2015 ◽  
Vol 764-765 ◽  
pp. 895-899
Author(s):  
Shiow Luan Wang ◽  
Thi Hoa Vu
Keyword(s):  
Project Management ◽  
Construction Projects ◽  
Project Success ◽  
Project Managers ◽  
Management Process ◽  
Time Cost ◽  
Information Presentation ◽  
Web Based ◽  
Project Participants ◽  
Project Data

Construction projects are becoming ever more complex and time driven, especially as the amount of project data and active project participants’ increase. For achieving a project success, project management not only must to meet time, cost, quality objectives, but also satisfies the project stakeholders needs related to the project management process. Project managers were difficult to effectively seizing, collecting and handling information which are generated from different systems. The elements of information presentation in web-based was contributed an important role to project management success. The purpose of this study is to provide a background to denote the enhancing project management via information presentation based on effective information technology/information systems which are emphasized in web-based.

Comprehensive Project Risk Management Methodology

2021 ◽  
Vol 8 (1) ◽  
pp. 94-115
Author(s):  
Luis F. Copertari
Keyword(s):  
Risk Management ◽  
Project Management ◽  
Critical Path ◽  
Three Dimensions ◽  
Project Risk Management ◽  
Time Cost ◽  
Due Date ◽  
Project Risk ◽  
Computer Screen ◽  
Optimal Cost

The objective of this paper is to introduce and discuss the basics of a methodology called the Probabilistic Critical Path Method (PCPM) for managing the previously identified risks (uncertainty) of the three project management dimensions: time, cost and return (performance). An interactive Graphic User Interface (GUI) has been designed for visualizing the tradeoffs among these three dimensions as well as their uncertainties on a flat computer screen. The user can choose to visualize the probability of failure (exceeding some user given due date, budget or not exceeding a given Minimally Attractive Rate of Return – MARR) or the probability of success (not exceeding the due date and the budget and exceeding the MARR). PCPM allows for comprehensive project risk management and it constitutes a new integrative project risk management framework. This paper shows that it is possible to integrate all three project management dimensions (time, cost and return) and show their known risks as well as determining the optimal cost and the associated time and return for such optimal cost. Finally, it is possible to interactively show all this multidimensional information on a flat computer screen.

scholarly journals A Dynamic Knowledge Model of Project Time-Cost Analysis Based on Trend Modelling

2019 ◽  
Vol 28 (1) ◽  
pp. 18-28
Author(s):  
Karel Doubravský ◽  
Radek Doskočil ◽  
Mirko Dohnal
Keyword(s):  
Project Management ◽  
Differential Equations ◽  
Cost Analysis ◽  
Trend Analysis ◽  
Linear Equations ◽  
Linear Differential Equations ◽  
Time Cost ◽  
Personnel Costs ◽  
Past Behavior ◽  
Linear Differential

This paper investigates the application of trend quantifiers of project time-cost analysis as a tool for decision-making support in the project management. Practical project management-related problems are solved under information shortages. It means that methods of statistical analysis cannot be easily used as they are based on the law of large numbers of observations. Numbers are information intensive quantifiers. The least information intensive quantifier is a trend; its values are increasing, constant, decreasing. If a derivative cannot be quantified by a trend, then nothing is known and therefore nothing can be analyzed/predicted. For this reason, the trend model M was created. The model M is based on a degraded set of differential equations or heuristics. A trend analysis of the model M is an evaluation of the relevant discrete set of solutions/scenarios S. A trend reconstruction is an evaluation of the model M if a (sub)set of scenarios S is given. The paper studies linear reconstruction, i.e. the model M is a set of linear differential equations. The trend reconstruction is partially reverse process to trend analysis. A case study has 7 variables (e.g. Project duration, Direct personnel costs, Indirect personal costs etc.) and the reconstructed set of linear differential equations has 7 equations. The set of 243 scenarios is obtained if this reconstructed set of trend linear equations is solved. Any future or past behavior of the model M can be described by a sequence of obtained scenarios.

Systematic approach to project efficiency management on the base of genetic algorithms

10.12737/99 ◽  
2012 ◽  
Vol 1 (1) ◽  
pp. 0-0
Author(s):  
������� ◽  
Olga Makeeva ◽  
������� ◽  
Igor Tsarkov
Keyword(s):  
Genetic Algorithms ◽  
Project Management ◽  
Time Management ◽  
Systematic Approach ◽  
Critical Path ◽  
Universal Model ◽  
Time Cost ◽  
Different Types ◽  
Management Project

The discovery of Critical Path Method (CPM) made breakdown of project management possible. In the result, project management consists of some knowledge areas like project time management, project cost management etc. But there is a problem: CPM couldn�t take into account resource constrains and costs. So there are a lot of models which include different types of constraints. But the vast majority of such models have serious problem: it�s impossible to scale them to universal model which could take into account time, cost and resources together. We suppose that this universal model could be developed on the base of genetic algorithms and it allow increasing efficiency and quality of project management. In this article we discuss the base principals and tools of such model and specific genetic algorithm is proposed

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