An ACO-based method including its corresponding framework is proposed for solving the project scheduling problem in terms of minimizing project duration and resource leveling index.
More than that, the proposed ACO method will be developed into a user-friendly system so that the practitioners can utilize it in solving the project scheduling problems.
The proposed framework is robust and easy to implement since it is based on highly accurate geodesic distance computation and solving the projected geometric constraints.
This study develops a two-phase fuzzy goal programming (FGP) method for solving the project management (PM) decision problems with multiple goals in uncertain environments.
Secondly, an improved Sakurai Sugiura algorithm is proposed to solve the projected NEPs with enhancements on reliability and accuracy.
According to some management goals, three types of fuzzy models are built to solve the project scheduling problem.
From the implicit function theorem, we solve the projected equations P F ( y 1 ) + ε P H 1 ( y 1, y 2, η, ε ) = 0, P F ( y 2 ) + ε P H 2 ( y 1, y 2, η, ε ) = 0. for unique y 1, 2 = Y 1, 2 ∈ Y such that Y 1, 2 ( η, 0 ) = 0, provided | ε | ≤ ε 0 is sufficiently small and η in a fixed closed ball Ξ ⊂ R 2 d with η ¯ ∈ Ξ ∘.
Parshu and Dion created NextLesson to solve the project tracking problem and a couple more problems along the way.
Creating flexibility by solving the true project planning complexity is difficult due to the involved uncertainties and dynamics, and classical models lack the flexibility necessary to adequately handle uncertainty.
In this paper, a multi-agent optimization algorithm (MAOA) is proposed for solving the resource-constrained project scheduling problem (RCPSP).
