A new computational approach for project management networks
ICC&IE '94 Proceedings of the 17th international conference on Computers and industrial engineering
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy priority heuristics for project scheduling
Fuzzy Sets and Systems
Network design techniques using adapted genetic algorithms
Advances in Engineering Software
Theory and Practice of Uncertain Programming
Theory and Practice of Uncertain Programming
An exact algorithm for the robust shortest path problem with interval data
Computers and Operations Research
Fuzzy statistics: hypothesis testing
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Fuzzy Probability and Statistics (Studies in Fuzziness and Soft Computing)
Fuzzy Probability and Statistics (Studies in Fuzziness and Soft Computing)
Applying fuzzy method for measuring criticality in project network
Information Sciences: an International Journal
Computers & Mathematics with Applications
A two-stage-priority-rule-based algorithm for robust resource-constrained project scheduling
Computers and Industrial Engineering
Some optimal models for facility location-allocation problem with random fuzzy demands
Applied Soft Computing
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In this article, we consider the project critical path problem in an environment with hybrid uncertainty. In this environment, the duration of activities are considered as random fuzzy variables that have probability and fuzzy natures, simultaneously. To obtain a robust critical path with this kind of uncertainty a chance constraints programming model is used. This model is converted to a deterministic model in two stages. In the first stage, the uncertain model is converted to a model with interval parameters by alpha-cut method and distribution function concepts. In the second stage, the interval model is converted to a deterministic model by robust optimization and min-max regret criterion and ultimately a genetic algorithm with a proposed exact algorithm are applied to solve the final model. Finally, some numerical examples are given to show the efficiency of the solution procedure.