• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.3
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• pp.8-15
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• 2014

For zero inventory and mixed assembly production, JIT (Just In Time) production system in Toyota and JIS (Just-In-Sequence) production system in Hyundai motor co. have been proposed in automobile production areas. Even though the production systems are popular in the areas, many subcontract companies producing part-modules for final production at a parent company suffers from excessive or shortage amount of inventory due to the time gap of production and delivery to the parent company. In this study, we propose an efficient real-time assembly sequence system applying a well-known Pareto method using Paint-In information in painting process and daily production planning information. Based on this system, a production line can estimate the shortage amount of UPH (Units Per Hour) at production line and recovers the amount before operating assembly production in the line. The proposed system provides efficiency on productivity compared with the previous system.

• International Journal of Fluid Machinery and Systems
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• v.9 no.3
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• pp.265-276
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• 2016

Energy systems working coherently in different conditions may not have a specific design which can provide optimal performance. A system working for a longer period at lower efficiency implies higher energy consumption. In this effort, a methodology demonstrated by a jet pump design and optimization via numerical modeling for fluid dynamics and implementation of an evolutionary algorithm for the optimization shows a reduction in computational costs. The jet pump inherently has a low efficiency because of improper mixing of primary and secondary fluids, and multiple momentum and energy transfer phenomena associated with it. The high fidelity solutions were obtained through a validated numerical model to construct an approximate function through surrogate analysis. Pareto-optimal solutions for two objective functions, i.e., secondary fluid pressure head and primary fluid pressure-drop, were generated through a multi-objective genetic algorithm. For the jet pump geometry, a design space of several design variables was discretized using the Latin hypercube sampling method for the optimization. The performance analysis of the surrogate models shows that the combined surrogates perform better than a single surrogate and the optimized jet pump shows a higher performance. The approach can be implemented in other energy systems to find a better design.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.1
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• pp.66-71
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• 2007

To adapt environment changing high speed and improve rapidly response ability for variation of environment and reduce delay time of decision making inlet agents, the derivation of user's preference and alternative are required. In this paper, we propose an efficient coordination method of multi-agents based on fuzzy decision making with the solution proposed by agents in the view of Pareto optimality. Our method generates the optimal alternative by using weighted value. We compute importance of attributes of winner agent, then can obtain the priorities lot attributes. The result of our method is analyzed that of Yager's method.

• International Journal of Aeronautical and Space Sciences
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• v.14 no.4
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• pp.324-333
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• 2013

The purpose of this paper is to improve the efficiency of multi-objective topology optimization using a genetic algorithm (GA) with bar-system representation. We proposed a new GA using an elite initial population obtained from a Solid Isotropic Material with Penalization (SIMP) using a weighted sum method. SIMP with a weighted sum method is one of the most established methods using sensitivity analysis. Although the implementation of the SIMP method is straightforward and computationally effective, it may be difficult to find a complete Pareto-optimal set in a multi-objective optimization problem. In this study, to build a more convergent and diverse global Pareto-optimal set and reduce the GA computational cost, some individuals, with similar topology to the local optimum solution obtained from the SIMP using the weighted sum method, were introduced for the initial population of the GA. The proposed method was applied to a structural topology optimization example and the results of the proposed method were compared with those of the traditional method using standard random initialization for the initial population of the GA.

• Journal of Electrical Engineering and Technology
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• v.5 no.1
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• pp.70-78
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• 2010

Elitist nondominated sorting genetic algorithm (NSGA-II) is adopted and improved for multiobjective optimal reactive power flow (ORPF) problem. Multiobjective ORPF, formulated as a multiobjective mixed integer nonlinear optimization problem, minimizes real power loss and improves voltage profile of power grid by determining reactive power control variables. NSGA-II-based ORPF is tested on standard IEEE 30-bus test system and compared with four other state-of-the-art multiobjective evolutionary algorithms (MOEAs). Pareto front and outer solutions achieved by the five MOEAs are analyzed and compared. NSGA-II obtains the best control strategy for ORPF, but it suffers from the lower convergence speed at the early stage of the optimization. Several problem-specific local search strategies (LSSs) are incorporated into NSGA-II to promote algorithm's exploiting capability and then to speed up its convergence. This enhanced version of NSGA-II (ENSGA) is examined on IEEE 30 system. Experimental results show that the use of LSSs clearly improved the performance of NSGA-II. ENSGA shows the best search efficiency and is proved to be one of the efficient potential candidates in solving reactive power optimization in the real-time operation systems.

• ETRI Journal
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• v.44 no.5
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• pp.805-815
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• 2022

The model-based evolutionary algorithms are divided into three groups: estimation of distribution algorithms, inverse modeling, and surrogate modeling. Existing inverse modeling is mainly applied to solve multi-objective optimization problems and is not suitable for many-objective optimization problems. Some inversed-model techniques, such as the inversed-model of multi-objective evolutionary algorithm, constructed from the Pareto front (PF) to the Pareto solution on nondominated solutions using a random grouping method and Gaussian process, were introduced. However, some of the most efficient inverse models might be eliminated during this procedure. Also, there are challenges, such as the presence of many local PFs and developing poor solutions when the population has no evident regularity. This paper proposes inverse modeling using random forest regression and uniform reference points that map all nondominated solutions from the objective space to the decision space to solve many-objective optimization problems. The proposed algorithm is evaluated using the benchmark test suite for evolutionary algorithms. The results show an improvement in diversity and convergence performance (quality indicators).

• Structural Engineering and Mechanics
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• v.88 no.4
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• pp.323-334
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• 2023

The submerged floating tunnel (SFT) infrastructure has been regarded as an emerging technology that efficiently and safely connects land and islands. The SFT route problem is an essential part of the SFT planning and design phase, with significant impacts on the surrounding environment. This study aims to develop an optimization model considering transportation and structure factors. The SFT routing problem was optimized based on two objective functions, i.e., minimizing total travel time and cumulative strains, using NSGA-II. The proposed model was applied to the section from Mokpo to Jeju Island using road network and wave observation data. As a result of the proposed model, a Pareto optimum curve was obtained, showing a negative correlation between the total travel time and cumulative strain. Based on the inflection points on the Pareto optimum curve, four optimal SFT routes were selected and compared to identify the pros and cons. The travel time savings of the four selected alternatives were estimated to range from 9.9% to 10.5% compared to the non-implemented scenario. In terms of demand, there was a substantial shift in the number of travel and freight trips from airways to railways and roadways. Cumulative strain, calculated based on SFT distance, support structure, and wave energy, was found to be low when the route passed through small islands. The proposed model helps decision-making in the planning and design phases of SFT projects, ultimately contributing to the progress of a safe, efficient, and sustainable SFT infrastructure.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.7B
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• pp.1034-1042
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• 2010

As many researchers have been interested in Green IT, Energy Efficient Ethernet(EEE) with rate adaptation has recently begun to receive many attention. However, the rate adaptation scheme can have different energy efficiency and delay according to the characteristics of various traffic patterns. Therefore, in this paper, we analyze the impact of different traffic patterns on the energy efficiency and delay in Ethernet with rate adaptation. To do this, firstly we design a rate adaptation simulator which consists of Poisson based traffic generator, Pareto distribution based ON-OFF generator and Ethernet node with rate adaptation by using OPNET Modeler. Using this simulator, we perform the simulation in view of the total number of switching, transmission rate reduction, energy saving ratio and average queueing delay. Simulation results show that IP traffic patterns with high self-similarity affect the number of switching, rate reduction and energy saving ratio. Additionally, the transition overhead is caused due to the high self-similar traffic.

• The Journal of Information Technology and Database
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• v.4 no.2
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• pp.21-32
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• 1998

A primary concern of physical database design has been efficient retrieval and update of a record because predictable performance of a DBMS is indispensable to time-critical missions. To maintain such phenomenal performance, database manages often spends more than or as much as the goal of an organization can warrant. The motivation of this research stems from the fact that even predictable performance of a physical database can be hampered by stochastic query processing time, physical configurations of a database, and random arrival processes of queries. They all together affect the overall performance of a DBMS. In particular, if there are queuing delays due to limited capacity or during on-peak congestion, this paper suggest to prioritize database services. A surprising finding of this paper is that such a transition from a non-priority system to a corresponding priority-based system can be Pareto-improving in the sense that no users in the system will be worse off after the transition. Thus prioritizing database services can be a viable option for efficient database management.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1998.04a
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• pp.245-253
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• 1998

Recently, the importance of multi-objective optimization techniques and stochastic search methods is increasing. The stochastic search methods have the concepts of the survival of the fittest and natural selection such as genetic algorithms(GA), simulated annealing(SA) and evolution strategies (ES). As many accidents of oil tankers cause marine pollution, oil tankers of double hull or mid deck structure are being built to minimize the marine pollution. For the improvement of oil tanker design technique, an efficient optimization technique is proposed in this study. Multi-objective optimization problem of weight and cost of double hull and mid deck tanker is formulated. Discrete design variables are used considering real manufacturing, and the concept of relative production cost is also introduced. The ES method is used as an optimization technique, and the ES algorithm was developed to generate a more efficient Pareto optimal set.