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.

ABSTRACT: This study concerns the scope to improve water flooding in heterogeneous reservoirs. We used an existing, in-house developed, Optimization program consisting of a reservoir simulator in combination with an adjoint-based optimal control algorithm. In particular we aimed to examine the scope for Optimization in a two-dimensional horizontal reservoir containing a single high permeable streak, as a function of reservoir and fluid parameters, which we combined in the form of 10 dimensionless parameters. We defined the parameter NPV improvement to indicate the improvement in net present value (NPV) that can be achieved through Optimization. For initial screening of the effect of the dimensionless parameters, a two-level D-optimal design of experiments (DOE) technique was used to obtain a linear response surface model with the aid of 11 water-flooding simulations. As a result 8 dimensionless groups were selected for more detailed analysis, and a full quadratic NPV improvement model was constructed using a three-level D-optimal design using 50 simulations. It should be reminded that all the D-optimal matrix designs were generated by using commands of statistics Toolbox of MATLAB software. Finally, Pareto charts were plotted to visualize the sensitivity of the model as a function of the dimensionless parameters. Based on the present model we can draw the conclusion that the parameters L s/L (relative streak length), k max/k max streak (relative streak permeability) and the ratio of water cost and oil price have the largest effect on the scope for obtaining a high value of NPVimprovement .

In this paper a Dynamic layout Optimization of a construction site is performed using genetic algorithm (DSL-GA). In this method considering the time table and the possibility of replacing the facilities in different time periods, layout design is performed. The formation of a proper and logical chromosome matrix in relation with Dynamic layout is one of the main characteristics of the present model.For comparison of the performance of the present model, the problem of the Dynamic site layout of the Rosenblatt which is a special case of the DSL-GA is considered. The results of the present method are compared to those of the Rosenblatt model. These results show that the high capability of the present model in performing the Dynamic layout Optimization.

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.

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.

Transportation plays an important role in excessive costs and the price of final goods. For this reason, constructing an economic transportation model enables us to optimize various activities in transportation systems for agriculture products. This brings lower costs for consumers of products. The main objective of this study is to construct a Dynamic linear programming transportation model in order to minimize the transportation costs of sunflower shipped from different supply regions to different pressing oil factories. There are 40 production centers as supply origins, and 7 factories as demanding centers for sunflower seeds in Iran. This study used the monthly data of year 2004 based on the capacities of origin supplies, warehouse (and related monthly stock available in previous period and the amount of transferred shipment to next period), monthly consumption of demanding centers and related transportation costs. LINGO computer package was applied to solve the transportation model. Based on the comparison of optimal solutions of the model and the actual transformation costs, the transformation cost reductions for sunflower seeds was 436 million Rials which was 11.8 percent of the total transportation costs over the year.

The purpose of this study is to modeling the factors affecting supply chain optimality based on the system Dynamics technique in the power industry. The present study uses a hybrid approach to develop a qualitative-quantitative approach to formulate and validate the factors influencing the supply chain optimality. To elaborate and identify the optimal factors, from content analysis and thematic analysis using Novio software to construct themes, a qualitative model of factors influencing supply chain optimality was designed. On the optimality of the supply chain, 23 experts and experts in the electricity industry with purposive sampling method were selected. In the quantitative part, the system Dynamics method was used for modeling, and then the data collected was analyzed by Venice software. This simulation was carried out over a one year period and represents a long-term horizon. Then the results of this simulation were presented and interpreted on the variables studied. Determining the relationships between variables and the types of variables can lead to a better understanding of the issue and to making appropriate decisions on the supply chain optimality problem.

The optimal harvesting can be a function of the stocking volume, stumpage price, harvesting cost, and rate of interest in the capital market. This research was done to determine the optimal harvest decision. First, a growth function was estimated for Kheyrud Forest, North of Iran, then the stumpage price was estimated via an autoregressive model. The optimal harvest was computed based on annual growth and stumpage price functions. The optimal adaptive harvesting which maximizes the expected present value of all profits over time are made conditional on the latest available price and stock level information. In order to determine the optimal harvest level, the optimal harvest decisions were made via stochastic Dynamic programming,as well as with deterministic Optimization method. The results show that if the stochastic Dynamic programming technique is used instead of deterministic Optimization method, the expected present value will increase by more than 26% via optimal adaptive decisions.

NP-hard Optimization problems are computationally challenging tasks that require significant resources to solve, particularly as problem sizes increase. In this paper, we introduce a novel framework, GiraDP, which converts Dynamic programming problems into graphs and solves these large-scale graphs efficiently. GiraDP addresses NP-hard Optimization problems by leveraging advanced graph processing techniques. The framework utilizes Giraph and Hadoop in its architecture to manage extensive datasets and complex computations. It generates input data for Giraph and identifies active vertices that correspond to sub-problems within the larger problem. Our experiments include scenarios with 20, 40, and 60 items, across varying knapsack capacities. The results indicate that while the serial algorithm performs best at low capacities, it fails to handle larger instances due to memory limitations, resulting in heap space errors. In contrast, GiraDP demonstrates superior efficiency and scalability for high capacities and large item sets. The MPI-based approach also shows improved performance over the serial algorithm for larger problems, although it does not match the efficiency of GiraDP. These findings underscore the importance of distributed computing solutions for large-scale Optimization problems