A computer-based systematic approach for optimizing material cost in reinforced concrete elements is presented. This approach considers the current material cost in the trade-off and selection of member dimensions and reinforcement design of concrete elements such as Beams and columns. The theoretical framework is to design reinforced concrete structure based on the cost of available materials and not just on the availability of materials. The windows-oriented computer program developed for this purpose would also be beneficial to owners, designers and contractors interested in cost optimization of reinforced concrete elements.      

The optimal selection of portfolio is one the most important decisions in managing investment funds. Several approaches have been proposed to determine what is the best trading strategy. Most of these common approaches are based on single period optimization, however, as investment is a long-term concept, such short term profit maximization cannot fully exploit the opportunities that an investor might get if he/she looks into a longer term. To this end, in this paper, we intend to extend the single-period optimization into a multi-period optimization and also, to make it more realistic, we will incorporate transaction cost in our model. To investigate the validity of the proposed scheme, we have analyzed several examples using which we have presented the steps of our approach and also statistically compared the performance of the single and multi- period optimization using Mann-Whitney U test. Based on the results of this paper, we can conclude that multi-period and single-period optimization might have similar performance if we look at short time span of the system. However the superiority of multi-period optimization becomes more evident as we extend the time span of the system which gives multi-period scheme more freedom to suggest better portfolio selections.

The optimal selection of portfolio is one the most important decisions in managing investment funds. Several approaches have been proposed to determine what is the best trading strategy. Most of these common approaches are based on single period optimization, however, as investment is a long-term concept, such short term profit maximization cannot fully exploit the opportunities that an investor might get if he/she looks into a longer term. To this end, in this paper, we intend to extend the single-period optimization into a multi-period optimization and also, to make it more realistic, we will incorporate transaction cost in our model. To investigate the validity of the proposed scheme, we have analyzed several examples using which we have presented the steps of our approach and also statistically compared the performance of the single and multi- period optimization using Mann-Whitney U test. Based on the results of this paper, we can conclude that multi-period and singleperiod optimization might have similar performance if we look at short time span of the system. However the superiority of multi-period optimization becomes more evident as we extend the time span of the system which gives multi-period scheme more freedom to suggest better portfolio selections.

In this paper, recommended spiral passive micromixer was designed and simulated. spiral design has the potential to create and strengthen the centrifugal force and the secondary flow. A series of simulations were carried out to evaluate the effects of channel width, channel depth, the gap between loops, and flowrate on the micromixer performance. These features impact the contact area of the two fluids and ultimately lead to an increment in the quality of the mixture. In this study, for the flow rate of 25 μl/min and molecular diffusion coefficient of 1×10-10 m2/s, mixing efficiency of more than 90% is achieved after 30 (approximately one-third of the total channel length). Finally, the optimized design fabricated using proposed 3D printing method.

Application of the network equivalent concept for external system representation for power system transient analysis is well known. However, the challenge to utilize an equivalent network, approximated by a rational function, is to guarantee the passivity of the corresponding model. In this regard, special techniques are required to enforce the passivity of the equivalent model through a post processing approach that minimizes its impact on the original model characteristics. In this paper, the passivity is enforced by expressing the problem in terms of a convex optimization problem that guarantees the global optimal solution. The convex optimization problem is efficiently solved by recently developed numerical interior–point methods. This passivity enforcement is also global which indicates that the passivity enforcement in one region does not lead to passivity violation in other regions.

Background and Objectives: Surface irrigation is the most common method of irrigation. over 80% of agricultural lands in Iran are irrigated by this method. Generally, this technique has lower investment and energy requirements than pressurized irrigation methods. Many efforts are applied to improve the economic output of water use and to preserve the environment in Iran. Modifying the design and management parameters at the farm level can improve the performance of irrigation systems. The main objective of this study is to optimize surface irrigation efficiency, with low-cost tools, using a simulation model. Materials and Methods: The study areas were selected fields of the Molla-Sani region in Khuzestan province, located southwest of Iran. Field experiments were carried out in two fields, irrigated using a surface irrigation system. Three irrigation events and three plots (as repeats) were applied per field. Experiments were conducted on the three borders of 150 m in length, 7 m in width, and 0. 125 % slope, in Field 1, and on three borders of 200 m in length, 7 m in width, and 0. 1 % slope, in Field 2. The inflow rates of 25 and 35 L/s were applied in fields 1 and 2. The Inflow rate was checked using a W. S. C flume. The borders were divided into parts of 10 m distances to measure the advance and recession times. The best combination of parameters was determined with the simulation model. The objective function (OF) including the application efficiency and the distribution uniformity was applied to optimize the irrigation performance. Results: This study showed that, based on the simulation model, changing the inflow rate, does not affect the best value of an objective function. The optimal inflow rate and cut-off time are recommended as 35 L/s and 30 min in a border with a length of 50 m, in Field 1, and, the best performance in Field 2, is obtained from the inflow rate of 20 L/s and the cut-off time of 48 min and length of 50 m. Field experiments showed that the difference in infiltration rates, was not significant, during this study. Based on the data obtained from three events, in both fields, and analyzed via the simulation model, the average NRMSE (Normalized Root Mean Square Error) index values for the evaluation of the advance curves were 12. 7, 12. 5, and 11. 6%, while the recession curves were 6. 9, 6. 8, and 6. 6%. Conclusion: Pressurized irrigation has the high investment and energy requirements than surface irrigation. Furthermore, the evaporation rate is much, in the research region. Because the inflow rate and cutoff time are the most effective parameters in improving irrigation, thus, in this region, prediction and selection of the optimum combination of cut-off time and inflow rate are the low-cost tools to improve the surface irrigation performance compared to modifying length and slope in border irrigation or transform of surface to the pressurized system, in the farm level.

The purpose of this paper is to present the cost estimation and optimization of space propulsion systems. Thus, choosing optimal propulsion system (from fuel and oxidizer aspect) is done in order to increase the efficiency and decrease the cost. Also, human resource cost and technology development time based on the consideration of labor cost effect on the personals motivation have been optimized. To this end, cost estimation and optimization algorithm has been drawn and suggested. The suggested algorithm has two steps. The first step in the algorithm is concern to cost estimation for seven fuel and oxidizer components. In the second step, labor cost and project implementation time is estimated and optimized based on the optimal space propulsion system derived from the previous step. Here, the objective functions are propulsion system technology development cost and time. On the other hand, the purpose is to consider the salary enhancement and consequently efficiency enhancement, time decrease and cost decrease.

Economic analysis is an important tool in evaluating the performances of microgrid (MG) operations and sizing. Optimization techniques are required for operating and sizing an MG as economically as possible. Various optimization approaches are applied to MGs, which include classic and artificial intelligence techniques. Particle swarm optimization (PSO) is one of the most frequently used methods for cost optimization due to its high performance and flexibility. PSO has various versions and can be combined with other intelligent methods to realize improved performance optimization. This paper reviews the cost minimization performances of various economic models that are based on PSO with regard to MG operations and sizing. First, PSO is described, and its performance is analyzed. Second, various objective functions, constraints and cost functions that are used in MG optimizations are presented. Then, various applications of PSO for MG sizing and operations are reviewed. Additionally, optimal operation costs that are related to the energy management strategy, unit commitment, economic dispatch and optimal power flow are investigated.

Safety risk management has a considerable effect on disproportionate injury rate of construction industry, project cost and both labor and public morale. On the other hand timecost optimization (TCO) may earn a big profit for project stakeholders. This paper has addressed these issues to present a multi-objective optimization model to simultaneously optimize total time, total cost and overall safety risk (OSR). The present GA-based optimization model possesses significant features of Pareto ranking as selection criterion, elite archiving and adaptive mutation rate. In order to facilitate safety risk assessment in the planning phase, a qualitative activity-based safety risk (QASR) method is also developed. An automated system is codded as an Excel add-in program to facilitate the use of the model for practitioners and researchers. The model has been implemented and verified on a case study successfully. Results indicate that integration of safety risk assessment methods into multi-objective TCO problem improves OSR of nondominated solutions. The robustness of the present optimization model has also been proved by its great ability to prevent genetic drift as well as the improvement in the bicriteria among generations.