A new method for critical path method with fuzzy processing time

Critical path method plays an important role on managing medium to large-scale problems. It is often difficult to determine the critical path for different reasons such as the existing uncertainties in processing tasks. One alternative to handle the uncertainty associated with the processing time is to use fuzzy techniques. We present a new method to calculate the critical path method when the processing times follow trapezoidal fuzzy numbers. The proposed model of this paper does not use any defuzzification technique to find the final processing time. The implementation of the proposed model is compared with other techniques using a well-known example from the literature

Exact Optimal Solution of Fuzzy Critical Path Problems

In this paper, a fuzzy critical path problem is chosen to show that the results, obtained by using the existing method [Liu, S.T.: Fuzzy activity times in critical path and project crashing problems. Cybernetics and Systems 34 (2), 161-172 (2003)], could be improved to reflect, more appropriate real life situations. To obtain more accurate results of fuzzy critical path problems, a new method that modifies the existing one is proposed here. To demonstrate the advantages of the proposed method it is used to solve a specific fuzzy critical path problem

A New Method for Fuzzy Critical Path Analysis in Project Networks with a New Representation of Triangular Fuzzy Numbers

The method for finding fuzzy optimal solution of fully fuzzy critical path (FFCP) problems i.e., critical path problems in which all the parameters are represented by fuzzy numbers, is at best scant; possibly non-existent. In this paper, a method is proposed to find the fuzzy optimal solution of FFCP problems, together with a new representation of triangular fuzzy numbers. This paper will show the advantages of using, the proposed representation over the existing representations of triangular fuzzy numbers and will present with great clarity the proposed method and illustrate its application to FFCP problems occurring in real life situations

Proposing an Optimum Model for Time Estimation of Construction Projects in Iranian Gas Refineries

Time management can be effective in a project when the project schedule is based on comprehensive time scheduling. In the industries with complicated processes, many uncertainties and risks affect the timing of projects. Considering the very low reliability of the project planning in certainty-based approach, using more secure models for control and interact with uncertainty should be placed on the agenda. Iranian Gas Company has been using risk management to manage probable uncertainties in construction projects but in the field of possible uncertainties, actions are very scarce. This article aims to propose an optimum model based on the integrated risk management and fuzzy expert systems in order to provide comprehensive project time estimation and in this regard, reviews the results of the implementation of this model in construction projects of Iranian gas refineries. The results show that the proposed model increases the accuracy of time estimation about 8 to 24 percent.Time management can be effective in a project when the project schedule is based on comprehensive time scheduling. In the industries with complicated processes, many uncertainties and risks affect the timing of projects. Considering the very low reliability of the project planning in certainty-based approach, using more secure models for control and interact with uncertainty should be placed on the agenda. Iranian Gas Company has been using risk management to manage probable uncertainties in construction projects but in the field of possible uncertainties, actions are very scarce. This article aims to propose an optimum model based on the integrated risk management and fuzzy expert systems in order to provide comprehensive project time estimation and in this regard, reviews the results of the implementation of this model in construction projects of Iranian gas refineries. The results show that the proposed model increases the accuracy of time estimation about 8 to 24 percent

Design and pilot run of fuzzy synthetic model (FSM) for risk evaluation in civil engineering

Most of the current construction risk assessment tools deliver unsatisfactory results because the prerequisite for their effective applications rely on the availability of high quality data especially during the early stage of a project. Unfortunately, such data are limited, ambiguous or even not exist due to the great uncertainty inherent in construction projects. Based on Fuzzy Synthetic Analysis (FSA), a model development team was formed among construction engineers, IT professionals, and Mathematicians in developing a holistic risk assessment model to estimate the construction risks especially for the situations with incomplete data and vague environments. Through qualitative scales defined by triangular fuzzy numbers used in pairwise comparisons to capture the vagueness in the linguistic variables, a risk assessment model using Analytic Hierarchy Process (AHP) was developed. The Pilot Run revealed the developed Fuzzy Synthetic Model (FSM) could accelerate the decision-making process and provide optimal allocation of project resources to mitigate possible risks detrimental to the success of a project in terms of time, cost, and quality

Ordonnancement sous incertitude

Tout projet est soumis à de l'incertitude et ce, à plusieurs aspects, comme la disponibilité des ressources et la durée certaine des tâches. Jusqu'à ce jour, pour appréhender l'incertitude, l'attention s'est portée sur l'ordonnancement des projets pour lesquels nous pouvons attribuer une durée probable aux différentes tâches. Mais souvent, le gestionnaire de projet ne peut recourir aux méthodes stochastiques usuelles parce que les données historiques ou la connaissance des temps d'exécution des nouvelles tâches sont manquantes. Ce mémoire propose un modèle d'ordonnancement qui traite de l'incertitude reliée à la durée des tâches, grâce à une formulation définie à l'aide d'un intervalle de temps borné par une durée minimale et une durée maximale. Cette nouvelle formulation de l'incertitude conduit à une nouvelle forme de problème. Trois méthodes sont ici proposées afin de calculer ce nouveau problème. Premièrement un calcul brut de tous les scénarios, deuxièmement l'utilisation de la méthode Monte Carlo et finalement l'utilisation d'une métaheuristique et plus précisément, il s'agit d'un algorithme d'optimisation de la colonie de fourmis. Ces différentes méthodes conduisent à des résultats différents en termes de précision, de rapidité et de niveau d'information de gestion fournie au gestionnaire de projet. La comparaison de ces différentes méthodes a été effectuée à partir de quatre ensembles de problèmes tests comprenant 25, 60, 90 et 120 tâches, respectivement et provenant de la librairie PSPLIB disponible sur lnternet : (http ://129.187.106.23l/psplib/)

Structuring NPD processes: advancements in test scheduling and activity sequencing

Ph.DDOCTOR OF PHILOSOPH