In this paper, a novel risk-based, two-objective (technical and economical) OPTIMAL reactive power dispatch method in a wind-integrated power system is proposed which is more consistent with operational criteria.  The technical objective includes the minimization of the new voltage instability risk index. The economical objective includes cost minimization of reactive power generation and active power loss. The proposed voltage instability risk employs a hybrid possibilistic (Delphi-Fuzzy)-probabilistic approach that takes into consideration the operator’s experience, the wind speed and demand forecast uncertainties when quantifying the risk index. The decision variables are the reactive power resources of the system. To solve the problem, the modified multi-objective particle swarm optimization algorithm with sine and cosine acceleration coefficients is utilized. The method is implemented on the modified IEEE 30-bus system. The proposed method is compared with those in the previously published literature, and the results confirm that the proposed risk index is better at estimating the voltage instability risk of the system, especially in cases with severe impact and low probability. In addition, according to the simulation results compared to typical security-based planning, the proposed risk-based planning may increase the security and economy of the system due to better utilization of system resources.

Purpose: The purpose of this applied research is to study the effectiveness of gamification on corporate training.  Methodology: A gamified course was designed and implemented to train the location of Fire Hosing Cabinet for 24 employees of a firefighting maintenance company in Iran Mall shopping Center in Tehran. Using a quantitative quasi-experimental research plan (post-test only control group design) the participants of the study were randomly assigned to treatment (12) and control (12) groups and trained for a week.  Conclusion: The descriptive and interpretive result of the posttest analyses indicated the effectiveness of gamification of the training performed for the employees of the firefighting maintenance company in Iran Mall shopping Center in Tehran. Moreover, the descriptive result of Gamification Acceptance Questionnaire answered by the members of the experimental group after gamified training indicated that all the participants in the experimental group were satisfied with the gamified training course.

In order to study the spatial structure of oriental beech (Fagus orientalis Lipsky) stand at OPTIMAL STAGE, this research was carried out in the Caspian forests of Iran. The studied site consisted of a natural stand located at Sangdeh district (Mers-e-se) with three hectare area. It is located between 1900 and1950 m.a.s.l. Six sample plots each with 0.36 ha area, were systematically selected and all trees (1244 stem) within the plots were measured and recorded. Using increment borer, the mean age of the stand was estimated as 137 years; the age difference of trees was 40 years. Results showed that the stand has a closed canopy cover and distribution of stem number per diameter class was more or less homogenous (Bell shape) with a semi even-aged structure. Using Ripley's K function, the distribution of trees within the stand was random.

In this paper, OPTIMAL bridge management system models have been presented. These optimization models are capable of allocating limited resources to the bridge preservation schemes in order to establish the OPTIMAL time of completing the activities. Bridge-based activities are divided into two main groups: repair projects, and maintenance activities and both models are presented in this paper. Particular attention has been made to optimize the management of the two system activities. The dynamic programming approach was utilized to formulate and analyze the two models. The developed models are found to be more accurate and faster than the previous ones.

Objective Flexible Manufacturing Systems (FMS) are integrated workstations that utilize computer-controlled equipment components for production. These systems are managed by a central computer, which significantly enhances the efficiency and productivity of the production process. Accordingly, a case study is conducted on an FMS electrical manufacturing system with complex manufacturing processes, where automation on the production line is crucial. Selecting the OPTIMAL number of advanced equipment is a challenging and vital issue for managers aiming to boost productivity and efficiently fulfill customer orders. It is a hard-to-change model, and replacing equipment incurs substantial costs.   Methods This study employs the two-STAGE D-OPTIMAL method to optimize the combination of FMS elements and equipment. The D-OPTIMAL response level input is derived from discrete-event simulation results. Depending on the conditions, various FMS equipment is allocated to each process. Each simulation result for element combinations serves as input for the experimental design. Additionally, the response level (y) of experiments from various FMS indexes is calculated using a weighting method. To reduce the number of experiments and increase data accuracy in a case study with hard-to-change parameters, all combinations are categorized based on the number of automated and manual equipment. The two STAGEs of the D-OPTIMAL design are defined as follows: In the first STAGE, all combinations within these categories are investigated. In the second STAGE, the optimized combination from the first STAGE is analyzed to determine the best configuration. Experiments in the top category from the first STAGE are simulated and further evaluated in the second STAGE of the D-OPTIMAL method.   Results In the first phase, all advanced production equipment and FMS elements were considered. After selecting the best-calculated “y” value, which was 147,133.09 in this category, another D-OPTIMAL design was optimized in the second phase to determine the best combination. This combination yielded a “y” value of 151,317.88, representing an improvement over the best category in the first phase of the D-OPTIMAL design. Consequently, the optimized combination from the first phase was further refined. The results from the developed D-OPTIMAL method and the second phase indicate that the OPTIMAL combination of equipment involves using automated and FMS equipment for approximately 92.8% of the total components. Finally, a list of recommended FMS equipment is provided, and its productivity is compared with the productivity at the current level and a higher degree of automation for this production line.   Conclusion In summary, the results of the experimental design show that using advanced production systems does not necessarily improve system efficiency, and determining OPTIMAL combinations requires accurate calculations.

In this paper the problem of OPTIMAL multiple-burn injection of a satellite into geostationary orbit using an upper STAGE with a limited thrust and restart capability, and comparison with sub-OPTIMAL case is considered. The goal is finding thrust vector angle, times of the engine firings and OPTIMAL duration of active phases of the upper STAGE so as to minimize fuel consumption and to meet the desired boundary conditions. The contribution of this research is developing an accurate and rapid convergence algorithm for solving multiple-burn trajectory for satellite injection into geostationary orbit. To solve the multipoint boundary value problem, an improved indirect shooting method with high performance and modified Newton’s methodis presented and used for OPTIMAL solution. Moreover, the novel method presented for multi burn problem not only has very good accuracy, but also, it converges very fast to the desired end conditions. Various flight sequences with multiple burns are considered and the OPTIMAL trajectory with minimum fuel consumption criteria, for each flight sequence is derived. The verification and validation of the proposed algorithm is made via comparison with references. Finally, the results of OPTIMAL solutions are compared with the results of sub-OPTIMAL solution whose thrust direction is aligned to the velocity vector direction.