• The KSFM Journal of Fluid Machinery
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• v.16 no.1
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• pp.11-16
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• 2013

A numerical study was conducted to improve the performance of an impeller of centrifugal compressor. Nine design variables were chosen with constraints. Only meridional contours and blade profile were adjusted. ANN (Artificial Neural Net) was adopted as a main optimization algorithm with PSO (Particle Swarm Optimization) in order to reduce the optimization time. At first, ANN was learned and trained with the design variable sets which were obtained using DOE (Design of Experiment). This ANN was continuously improved its accuracy for each generation of which population was one hundred. New design variable set in each generation was selected using a non-gradient based method of PSO in order to obtain the global optimized result. After $7^{th}$ generation, the prediction difference of efficiency and pressure ratio between ANN and CFD was less than 0.6%. From more than 1,200 design variable sets, a pareto of efficiency versus pressure ratio was obtained and an optimized result was selected based on the multi-objective function. On this optimized impeller, the efficiency and pressure ratio were improved by 1% and 9.3%, respectively.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.5
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• pp.433-441
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• 2011

The optimization of a centrifugal compressor was conducted. The ANN (Artificial Neural Network) was adopted as an optimization algorithm, and it was learned and trained with the DOE (Design of Experiment). In the DOE, it was predicted the main effect and the interaction effect of design variables to the objective function. The ANN was improved in the optimization process using the GA (Genetic Algorithm). When any output at each generation was reached a standard level, it was re-calculated by the CFD (Computational Fluid Dynamics) and it was applied to develop a new ANN. After 6th generation, the prediction difference between ANN and CFD was less than 1%. A pareto of the efficiency versus the pressure ratio was obtained through the 21th generation. Using this method, the computational time for the optimization was equivalent to the time consumed by the gradient method, and the optimized results of multi-objective function were obtained.

• Journal of Distribution Research
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• v.2 no.1
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• pp.143-174
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• 1997

The model applied in this paper is based on the theory of economic order quantity (EOQ). EOQ model is introduced to explain the improvement of the transaction efficiency through the cooperative purchase. We examine explicitly how horizontal cooperation affects vertical transactions. A result of the analysis is that a seller can prefer transacting with a cooperative rather than with each buyer separately, even if he reduces the selling price of the product. Without increasing the demand for the product, this result is that dealing with a cooperative, rather than separately with each buyer, decreases the transaction cost for the seller-buyers system, the cost reduction more than off-setting the effect of price decrease on the sellers profit. For a coopative consisting of any number of buyers, Pareto efficient ordering policies that maximize the joint cost saving for the seller-buyers system are identified. We then discuss the conditions under which a cooperative under consideration can be modified to increase efficiency gain. Next, we relax the assumption that all buyers participate in a single cooperative and examine the issue of how many cooperatives, each consisting of a subset of the buyers, should be formed to maximize the total cost saving for the seller-buyers system. Finally, the issue of shapley value to divide the cooperatives gain among its members is discussed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.611-627
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• 2017

For enhancing the coverage of wireless networks and increasing the spectrum efficiency, small cell networks (SCNs) are considered to be one of the most prospective schemes. Most of the existing literature on resource allocation among non-cooperative small cell base stations (SBSs) has widely drawn close attention and there are only a small number of the cooperative ideas in SCNs. Based on the motivation, we further investigate the cooperative approach, which is formulated as a coalition formation game with power control algorithm (CFG-PC). First, we formulate the downlink sub-channel resource allocation problem in an SCN as a coalition formation game. Pareto order and utilitarian order are applied to form coalitions respectively. Second, to achieve more availability and efficiency power assignment, we expand and solve the power control using particle swarm optimization (PSO). Finally, with our proposed algorithm, each SBS can cooperatively work and eventually converge to a stable SBS partition. As far as the transmit rate of per SBS and the system rate are concerned respectively, simulation results indicate that our proposed CFG-PC has a significant advantage, relative to a classical coalition formation algorithm and the non-cooperative case.

• Smart Structures and Systems
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• v.16 no.1
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• pp.1-26
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• 2015

Minimizing construction cost and reducing seismic damage are two conflicting objectives in the design of any new structure. In the present work, we try to develop a framework in order to solve the optimum performance-based design problem considering the construction cost and the seismic damage of steel moment-frame structures. The Park-Ang damage index is selected as the seismic damage measure because it is one of the most realistic measures of structural damage. The non-dominated sorting genetic algorithm (NSGA-II) is employed as the optimization algorithm to search the Pareto optimal solutions. To improve the time efficiency of the proposed framework, three simplifying strategies are adopted: first, simplified nonlinear modeling investigating minimum level of structural modeling sophistication; second, fitness approximation decreasing the number of fitness function evaluations; third, wavelet decomposition of earthquake record decreasing the number of acceleration points involved in time-history loading. The constraints of the optimization problem are considered in accordance with Federal Emergency Management Agency's (FEMA) recommended seismic design specifications. The results from numerical application of the proposed framework demonstrate the efficiency of the framework in solving the present multi-objective optimization problem.

• Advances in aircraft and spacecraft science
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• v.10 no.3
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• pp.245-256
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• 2023

In order to improve aerodynamic performance of multi-stage axial flow turbines used in aircraft engines, a one-dimensional aerodynamic design and optimization framework is constructed. In the method, flow path is generated by solving mass continuation and energy conservation with loss computed by the Craig & Cox model; Also real gas properties has been taken into consideration. To obtain an optimal result, a multi-objective genetic algorithm is used to optimize the efficiencies and determine values of various design variables; Final design can be selected from obtained Pareto optimal solution sets. A three-stage axial turbine is used to verify the effectiveness of the developed optimization framework, and designs are checked by three-dimensional CFD simulation. Results show that the aerodynamic performance of the optimized turbine has been significantly improved at design point, with the total-to-total efficiency increased by 1.17% and the total-to-static efficiency increased by 1.48%. As for the off-design performance, the optimized one is improved at all working points except those at small mass flow.

• Journal of Korean Institute of Industrial Engineers
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• v.25 no.3
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• pp.310-320
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• 1999

A mixed model assembly line (MMAL) is a special type of production line where a variety of product models similar in product characteristics are assembled. Determining the model sequence is an important problem for the efficient use of MMALs. This paper considers interactive multiobjective decision making problems for MMAL sequencing. Evolution program is employed as an underlying framework. In this study, a way of approximating the linear utility function is first studied. To improve its search efficiency to the solution space preferred by a decision maker, some modifications of a standard evolution program are made: operating several subpopulations instead of a single population and merging two or more subpopulations to a single subpopulation, and using a Pareto pool. Extensive computational experiments are carried out to verify the performance of the proposed approach. The computational results show that our approach is promising in solution quality.

• Industrial Engineering and Management Systems
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• v.3 no.2
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• pp.116-122
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• 2004

This paper presents a process plan selection model with multiple objectives. The process plans for all parts should be selected under multiple objective environment as follows: (1) minimizing the sum of machine processing and material handling time of all the parts considering realistic shop factors such as production volume, processing time, machine capacity, and capacity of transfer device. (2) balancing the load between machines. A multiple objective mathematical model is proposed and an evolutionary algorithm with the adaptive recombination strategy is developed to solve the model. To illustrate the efficiency of proposed approach, numerical examples are presented. The proposed approach is found to be effective in offering a set of satisfactory Pareto solutions within a satisfactory CPU time in a multiple objective environment.

• Journal of the Korean Regional Science Association
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• v.10 no.2
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• pp.45-51
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• 1994

The purpose of this paper is to present the optimal investment conditions of SOC facilities for maximizing regional social welfare in implementing the urban development project in the theoretical fashion. Particularily, SOC facilities are divided into both supply-side($P_s$) and demand-side SOC ($P_d$) in the paper. General equilibrium analysis from the intra-regional viewpoint by utilizing Pareto's Optimal Conditions and by revising Samuleson's Conditions for public goods($P_s$ and $P_d$) results in the optimum pattern of SOC investment. The following are important implications from the analysis. First, rather than the pursue social equity, SOC investment is to resolve the issue of efficiency to activate the regional economy. Second, the marginal rate of transformation (MRT) between $P_s$ and $P_d$ in the region is to play a significant role in structuring SOC investment plant of local government for social welfare maximization. Third, the optimal SOC investment policy based on this regional economy but also to generate the enhancement of soical amenities of the residents.

• International Journal of Precision Engineering and Manufacturing
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• v.7 no.4
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• pp.57-62
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• 2006

The quality of precision spindle units (S/Us) running on rolling bearings depends strongly on their structural parameters, such as the configuration and geometry of the S/U elements and bearing preloads. When S/Us are designed, their parameters should be optimized to improve the performance characteristics. However, it is practically impossible to state perfectly a general criterion function for S/U quality. Therefore, we propose to use a multicriteria optimization based on the parameter space investigation (PSI) method We demonstrate the efficiency of the proposed method using the optimization results of high-speed S/Us.