Towards a model of integration between Risk Management and Lesson Learning system for Project Management

Manotas-Nino, Vanessa; Clermont, Philippe; Geneste, Laurent; Halabi, Ana X.

doi:10.1109/iesm.2015.7380302

Cited by 4 publications

(1 citation statement)

References 18 publications

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“…Project management, as a well-defined approach dealing with strategic change, contributes to create a strategic value chain with which company respond to change and gain a competitive edge [1]. To carry out project, various parameters and often, various stakeholders must be considered by the managers so as to better study the potential impacts of their decisions and assess the risks as precisely as possible [2]. Whenever Project Risk (PR) exists, there is complexity.…”

Section: Introductionmentioning

confidence: 99%

A Smart Ontology for Project Risk Management Based on PMI’s Framework

Zaouga

Rabai

2020

Proceedings of Fifth International Congress on Information and Communication Technology

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Whenever Project Risk (PR) exists, there is complexity. The difficulty to make concerned decisions related to Project Risk Management (PRM) increases project complexity and even its failure. In order to assist practitioners and professionals to better study the potential impacts of their decisions and assess the PR as precisely as possible, this chapter put forward an ontological approach based on OWL ontology with SWRl rules, that provides the project team clear guidelines to effectively manage PR, and then make the appropriate decisions based on the right recommendations. This approach takes advantages of ontology semantic strengths as it represents a unified PRM knowledge relying on PMI's frameworks. As well, through SWRL reasoning rules, the proposed ontology generates recommendations by which a team member ask for risk-related request more targeted. The proposed ontological approach was evaluated, in term of content and structure, achieving promising results based on the F-measure metric.

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Section: Introductionmentioning

confidence: 99%

A Smart Ontology for Project Risk Management Based on PMI’s Framework

Zaouga

Rabai

2020

Proceedings of Fifth International Congress on Information and Communication Technology

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Scenario selection optimization in system engineering projects under uncertainty: A multi-objective Ant Colony method based on a learning mechanism

Lachhab¹,

Coudert²,

Béler³

2016

2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

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This paper presents a multi-objective Ant Colony Optimization (MOACO) algorithm based on a learning mechanism (named MOACO-L) for the optimization of project scenario selection under uncertainty in a system engineering (SE) process. The objectives to minimize are the total cost of the project, its total duration and the global risk. Risk is considered as an uncertainty about task costs and task durations in the project graph. The learning mechanism aims to improve the MOACO algorithm for the selection of optimal project scenarios in a SE project by considering the uncertainties on the project objectives. The MOACO-L algorithm is then developed by taking into account ants' past experiences. The learning mechanism allows a better exploration of the search space and an improvement of the MOACO algorithm performance. To validate our approach, some experimental results are presented.

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