• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2005.11a
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• pp.306-309
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• 2005

We consider the multi-objective optimization of a multi-service arrayed-waveguide grating-based single-hop metro WDM network with the two conflicting objectives of maximizing throughput while minimizing delay. We develop and evaluate a genetic algorithm based methodology for finding the optimal throughput-delay tradeoff curve, the so-called Pareto-optimal frontier. Our methodology provides the network architecture and the Medium Access Control protocol parameters that achieve the Pareto-optima in a computationally efficient manner. The numerical results obtained with our methodology provide the Pareto-optimal network planning and operation solution for a wide range of traffic scenarios. The presented methodology is applicable to other networks with a similar throughput-delay tradeoff.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2000.04b
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• pp.168-173
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• 2000

Design of experiments is utilized for exploring the design space and for building response surface models in order to facilitate the effective solution of multi-objective optimization problems. Response surface models provide an efficient means to rapidly model the trade-off among many conflicting goals. In robust design, it is important not only to achieve robust design objectives but also to maintain the robustness of design feasibility under the effects of variations, called uncertainties. However, the evaluation of feasibility robustness often needs a computationally intensive process. To reduce the computational burden associated with the probabilistic feasibility evaluation, the first-order Taylor series expansions are used to derive individual mean and variance of constraints. For robust design applications, these constraint response surface models are used efficiently and effectively to calculate variances of constraints due to uncertainties. Robust optimization of automotive seat is used to illustrate the approach.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.354-359
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• 2006

The analysis of large-scale water resources systems is often complicated by the presence of multiple reservoirs and diversions, the uncertainty of unregulated inflows and demands, and conflicting objectives. Reinforcement learning is presented herein as a new approach to solving the challenging problem of stochastic optimization of multi-reservoir systems. The Q-Learning method, one of the reinforcement learning algorithms, is used for generating integrated monthly operation rules for the Keum River basin in Korea. The Q-Learning model is evaluated by comparing with implicit stochastic dynamic programming and sampling stochastic dynamic programming approaches. Evaluation of the stochastic basin-wide operational models considered several options relating to the choice of hydrologic state and discount factors as well as various stochastic dynamic programming models. The performance of Q-Learning model outperforms the other models in handling of uncertainty of inflows.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.5
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• pp.325-331
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• 2004

The multiobjective optimization (MO) problem usually includes the conflicting objectives and the use of conventional optimization algorithms for MO problem does not so good approach to obtain an effective optimal solution. In this paper, genetic algorithm (GA) as an effective method is used to solve such MO problem of brushless DC motor (BLDCM). 3D equivalent magnetic circuit network (EMCN) method which enables us to reduce the computational burden is also used to consider the 3D structure of BLDCM. In order to effectively obtain a set of Pareto optimal solutions in MO problem, ranking method proposed by Fonseca is applied. The objective functions are decrease of cogging torque and increase of torque respectively. The airgap length, teeth width and magnetization angle of PM are selected for the design variables. The experimental results are also shown to confirm the validity of the optimization results.

• Korean Management Science Review
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• v.15 no.2
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• pp.201-210
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• 1998

Truck dispatching is one of the most commonly occurring problems of transprot management. We developed an interactive decision support system named IDSSTD, for the truck dispatching problem where two conflicting objectives are treated and travel speed varies depending on the passing areas and time of day. The IDSSTD aids the decision-making process by allowing the user to interact directly with the database, to direct data to a decision model, and to portray results in a convenient form. The IDSSTD is consisted of two major interactive phases. The pre-scheduling interactive phase is to reduce the complexity of a given problem before applying the BC-saving heuristic algorithm and the post-scheduling interactive phase is to improve practically the algorithmic solution. The IDSSTD has the capabilities of its own manipulation(analysis and recommendation) and diverse graphic features in order to facilitate a user's interaction.

• Journal of the Korean Operations Research and Management Science Society
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• v.24 no.2
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• pp.121-134
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• 1999

In this paper, I propose a hybrid genetic algorithm(HGAM) incorporating a greedy interchange local optimization procedure for the multiobjective vehicle scheduling problems with service due times where three conflicting objectives of the minimization of total vehicle travel time, total weighted tardiness, and fleet size are explicitly treated. The vehicle is allowed to visit a node exceeding its due time with a penalty, but within the latest allowable time. The HGAM applies a mixed farming and migration strategy in the evolution process. The strategy splits the population into sub-populations, all of them evolving independently, and applys a local optimization procedure periodically to some best entities in sub-populations which are then substituted by the newly improved solutions. A solution of the HCAM is represented by a diploid structure. The HGAM uses a molified PMX operator for crossover and new types of mutation operator. The performance of the HGAM is extensively evaluated using the Solomons test problems. The results show that the HGAM attains better solutions than the BC-saving algorithm, but with a much longer computation time.

• Management Science and Financial Engineering
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• v.13 no.2
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• pp.29-46
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• 2007

This paper makes an attempt to compare the two important methods for finding solutions of multi-item inventory problem with more than one conflicting objectives. Panda et al.[9] discusses a distance-based method to find the best possible compromise solution with variation of priority under the given weight structure. In this paper, the problem in [9] is revisited through the Pareto-optimal front of genetic algorithm with the help of a situation of retail stocking of FMCG business. The advantages of using the solutions from the perspective of the decision maker obtained through multi-objective optimization are highlighted in terms of population search, weighted goals and priority structure, cost, set of compromise solutions along with prevention of stock-out situation.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.233-235
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• 2000

SVC(Static Var Compensator) is commonly installed with conventional mechanically switched existing reactor or capacitor banks for wide range voltage control. The frequencies of switching of external banks have a great impact on the quality of voltage, but is limited since the life time of the external banks depends severely on the number of switching. So it is a complete multi-objective nonlinear optimization problem with conflicting objectives. This paper presents a method to determine the optimal coordination of SVC and external banks using genetic algorithm based on the multi-objective criteria. Optimal dead band and delay time of external banks is sought for reliable and efficient operation

• Journal of Construction Engineering and Project Management
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• v.2 no.1
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• pp.20-26
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• 2012

Urban regeneration is a large-scale program that must address challenges such as a significant number of involved projects, diverse participants and their conflicting objectives, and a long life span. In an effort to address these issues, the Interactive Electronic Technical Manual (IETM) is applied to urban regeneration in order to provide a guideline to enhance urban regeneration program management. Through a survey, three major functionalities required in the IETM are identified: process map provision, customized information delivery, and communication ability with other information systems. To achieve these functionalities, the development framework of the IETM and the associated technologies are examined.Its characteristics can be sum up ontology-driven metadata, user-oriented process map composition system and so on. Finally, the usage scenarios of the IETM are discussed.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1279-1281
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• 1999

The learning automaton is an automaton to update systematically the strategy for enhancing the performance in response to the output results, and several schemes of learning automata have been presented. In this paper, S-model learning automata are applied to achieving a best compromise solution between an optimal solution for economic operation and an optimal solution for stable operation of the power system under the circumstance that the loads vary randomly. It is shown that learning automata are applied satisfactorily to the multiobjective optimization problem for obtaining the best tradeoff among the conflicting economy and stability objectives of power systems.