A common problem arising in project management is the fact that the baseline schedule is often disrupted during the project execution because of Uncertain parameters. As a result, project managers are often unable to meet the deadline time of the milestones. Robust project scheduling is an effective approach in case of Uncertainty. Upon adopting this approach, schedules are protected against possible disruptions that may occur during project execution. In order to apply robust scheduling principles to real projects, one should make assumptions close to the actual conditions of the project as much as possible. In this paper, in terms of Uncertainty in both activities duration and resources availability, some methods are proposed to construct the robust schedules. In addition, various numerical experiments are applied to different problem types with the aid of simulation. The main purpose of those is to assess the performance of robust scheduling methods under different conditions. Finally, we formulate recommendations regarding the best method of robust scheduling based on the results of these experiments.

In this paper, the stability of time-delay control systems subject to parameter perturbation is investigated. The maximum allowable perturbation in the parameters of the system that maintains stability is determined. The parameters are allowed to perturb inside an lp-hypersolid (such as a hyperellipsoid, a box or a diamond in a multi-dimensional space) around their nominal values, and the size of the largest hypersolid that preserves the stability conditions is computed. The stability is characterized as the location of the closed-loop poles inside the symmetric regions in the complex plane (so-called “D-stability”). A direct numerical method is developed for the calculation of these stability margins. The method requires plotting a distance function over the contour of the stability region and finding its minimum. To illustrate the method, a numerical example is provided.

Given that disasters are unavoidable, and many people are suffering from them each year, we should manage the emergencies and plan for them well to reduce mortality and financial losses. One of the measures that organizations must take after the disaster is the assessment of the conditions and needs of the people. We consider some characteristics for sites and roads and two teams for assessment as well as the Uncertain assessment time to modeling. A multi-objective model is proposed in this study. The first objective function maximizes the gain from the assessment of areas and roads. The second and third objective functions maximize total coverage at damaged areas and roads. We use the LP-metric technique to solve small size problems in the GAMS software and the Grasshopper Optimization Algorithm (GOA) as a Meta-heuristic algorithm to solve a case study.  Numerical results are presented to prove the credibility and efficiency of our model.

In this work a robust optimum design for mechanical parameters of linear Tuned Mass Damper devices is proposed. In this field, standard approaches are based on the implicit assumption that all system parameters are deterministically known quantities. When this hypothesis is removed, a robust optimum design criterion for Tuned Mass Damper should be developed, where robustness is obtained by finding solutions which are less sensitive to variation of system parameters, originated by the Uncertainty.In this study the load condition for the analysed system is represented by a stationary stochastic process which models the base acceleration, and here modelled by the Kanai- Tajimi stochastic process. The main system, which is equipped by a single Tuned Mass Damper, is described by a system with a single degree of freedom: system mass and stiffness are assumed to be affected by Uncertainty, and then are represented by random bounded variables. The ratio between the protected and unprotected main system covariance displacement is assumed as Objective Function, and then its mean and standard deviation are evaluated. Robust optimum design is formulated as a multi-objective optimization problem, in which both the first and the second statistical moments are minimized simultaneously, with different weights. In this way, optimal Pareto fronts are obtained: after that a sensitivity analysis is carried out in order to assess the variation of robust solution with respect to some parameters, and moreover in order to evaluate the differences with respect to conventional deterministic solution...

Fractional programming is a significant nonlinear planning tool within operation research‎. ‎It finds applications in diverse domains such as resource allocation‎, ‎transportation‎, ‎production programming‎, ‎performance evaluation‎, ‎and finance‎. ‎In practical scenarios‎, ‎Uncertainties often make it challenging to determine precise coefficients for mathematical models‎. ‎Consequently‎, ‎utilizing indefinite coefficients instead of definite ones is recommended in such cases‎. ‎Grey systems theory‎, ‎along with probability theory‎, ‎randomness‎, ‎fuzzy logic‎, ‎and rough sets‎, ‎is an approach that addresses Uncertainty‎. ‎In this study‎, ‎we address the problem of linear fractional programming with grey coefficients in the objective function‎. ‎To tackle this problem‎, ‎a novel approach based on the variable change technique proposed by Charnes and Cooper‎, ‎along with the convex combination of intervals‎, ‎is employed‎. ‎The article presents an algorithm that determines the solution to the grey fractional programming problem using grey numbers‎, ‎thus capturing the Uncertainty inherent in the objective function‎. ‎To demonstrate the effectiveness of the proposed method‎, ‎an example is solved using the suggested approach‎. ‎The result is compared with solutions obtained using the whitening method‎, ‎employing Hu and Wong's technique and the Center and Greyness Degree Ranking method‎. ‎The comparison confirms the superiority of the proposed method over the whitening method‎, ‎thus suggesting adopting the grey system approach in such situations‎.