• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.101-101
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• 2017

근래에 대한민국 담수계에 조류 대발생으로 인한 수질악화 문재가 대두되고 있다. 또한 독성물질을 생성하는 남조류종이 우점하는 현상으로인해 수질문제와더불에 생태계와 인간의 건강도 잠재적인 위험을 받고있는 실정이다. 이와같은 조류 대발생으로인한 피해를 최소화하기위해 효과적인 수질관리가 필수적이다. 원격탐사기술은 조류의 공간적인 분포를 해석하고 농도를 정량화하기위해 이용되고 있다. 현재까지 많은 분광알고리즘들이 개발되어 담수유역에 적용이 되고 있다. 수체마다 다른 분광특성 때문에 알고리즘내의 파라미터 및 분광밴드 조정이 필수적이다. 하지만 대부분의 연구에선 파라미터와 밴드의 변경에 따른 결과향상에만 초점이 맞춰지고 있어 분광알고리즘내의 파라미터와 분광밴드사이의 관계 이해 뿐만아니라 알고리즘 최종 산출물에 대한 영향에 관한 설명이 전무한 실정이다. 본 연구에선, 대한민국 백제보를 대상으로 현장모니터링 및 조류추출 실험을 진행하였고, 이를 기반으로 5가지 클로로필a 알고리즘과 2가지 피코시아닌 알고리즘을 구축하였다. 알고리즘내에서 변수들의 관계와 영향을 알아보기위해 민감도 분석을 실시하였다. 민감도 분석 조건을 기반으로 one-objective 최적화 및 multi-objective 최적화를 실시하여 백제보수계를 대표할 수 있는 최적 변수들을 모의하였다. 민감도 분석결과 후방산란계수에 영향을 미치는 파라미터와 조류 생체량에 영향을 미치는 파라미터가 다른 변수들 및 알고리즘 농도산정결과에 가장 민감한 것으로 나타났다. multi-objective 최적화 결과가 one-objective 결과 및 reference 결과보다 대부분 정확도가 향상되었고 흡광도 계수를 함께 고려할 수 있기 때문에 백제보 수계의 분광특성을 함께 고려하여 대표할 수 있는 장점을 가지는 것으로 나타났다. 따라서, 본 연구는 민감도 분석을 활용하여 분광알고리즘 내의 변수들의 이해를 도모하였고, 최적화 기법 중, multi-objective 최적화 기법이 백제보의 분광특성을 대변하는 최적변수를 제시할 수 있음과 동시에 보다 나은 정확성을 제고할 수 있음을 확인하였다.

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.1
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• pp.73-80
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• 2010

The optimal design of water distribution system have started with the least cost design of single objective function using fixed hydraulic variables, eg. fixed water demand and pipe roughness. However, more adequate design is accomplished with considering uncertainties laid on water distribution system such as uncertain future water demands, resulting in successful estimation of real network's behaviors. So, many researchers have suggested a variety of approaches to consider uncertainties in water distribution system using uncertainties quantification methods and the optimal design of multi-objective function is also studied. This paper suggests the new approach of a multi-objective optimization seeking the minimum cost and maximum robustness of the network based on two uncertain variables, nodal demands and pipe roughness uncertainties. Total design procedure consists of two folds: least cost design and final optimal design under uncertainties. The uncertainties of demands and roughness are considered with Latin Hypercube sampling technique with beta probability density functions and multi-objective genetic algorithms (MOGA) is used for the optimization process. The suggested approach is tested in a case study of real network named the New York Tunnels and the applicability of new approach is checked. As the computation time passes, we can check that initial populations, one solution of solutions of multi-objective genetic algorithm, spread to lower right section on the solution space and yield Pareto Optimum solutions building Pareto Front.

• Journal of Mechanical Science and Technology
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• v.18 no.8
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• pp.1327-1337
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• 2004

An efficient multi-objective optimization method is presented making use of neural network and a systematic satisficing trade-off method (STOM), in order to simultaneously improve both maneuverability and durability of tire. Objective functions are defined as follows: the sidewall-carcass tension distribution for the former performance while the belt-edge strain energy density for the latter. A back-propagation neural network model approximates the objective functions to reduce the total CPU time required for the sensitivity analysis using finite difference scheme. The satisficing trade-off process between the objective functions showing the remarkably conflicting trends each other is systematically carried out according to our aspiration-level adjustment procedure. The optimization procedure presented is illustrated through the optimum design simulation of a representative automobile tire. The assessment of its numerical merit as well as the optimization results is also presented.

• Korean Management Science Review
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• v.29 no.1
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• pp.1-14
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• 2012

The goal of the multi-reservoir operation planning is to provide an optimal release plan that maximize the reservoir storage and hydropower generation while minimizing the spillages. However, the reservoir operation is difficult due to the uncertainty associated with inflows. In order to consider the uncertain inflows in the reservoir operating problem, we present a Stochastic Dynamic Programming (SDP) model based on the markov decision process (MDP). The objective of the model is to maximize the expected value of the system performance that is the weighted sum of all expected objective values. With the SDP model, multi-reservoir operating rule can be derived, and it also generates the steady state probabilities of reservoir storage and inflow as output. We applied the model to the Geum-river basin in Korea and could generate a multi-reservoir monthly operating plan that can consider the uncertainty of inflow.

• International Journal of Control, Automation, and Systems
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• v.6 no.3
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• pp.401-412
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• 2008

This paper discusses a design methodology of cooperative path planning for dynamical multi-agent systems with spatial and temporal constraints. The cooperative behavior of the multi-agent systems is specified in terms of the objective function in an optimization formulation. The path of achieving cooperative tasks is then generated by the optimization formulation constructed based on a differential flatness approach. Three scenarios of multi-agent tasking are proposed at the cooperative task planning framework. Given agent dynamics, both spatial and temporal constraints are considered in the path planning. The path planning algorithm first finds trajectory curves in a lower-dimensional space and then parameterizes the curves by a set of B-spline representations. The coefficients of the B-spline curves are further solved by a sequential quadratic programming solver to achieve the optimization objective and satisfy these constraints. Finally, several illustrative examples of cooperative path/task planning are presented.

• Industrial Engineering and Management Systems
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• v.13 no.1
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• pp.29-42
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• 2014

Extensive research has been conducted on supplier evaluation and selection as a strategic and crucial component of supply chain management in recent years. However, few articles in the previous literature have been dedicated to the use of fuzzy inference systems as an aid in decision-making. Therefore, this essay attempts to demonstrate the application of this method in evaluating suppliers, based on a comprehensive framework of qualitative and quantitative factors besides the effect of gradual coverage distance. The purpose of this study is to investigate the applicability of the numerous measures and metrics in a multi-objective optimization problem of the supply chain network design with the aim of managing the allocation of orders by coordinating the production lines to satisfy customers' demand. This work presents a dynamic non-linear programming model that examines the important aspects of the strategic planning of the manufacturing in supply chain. The effectiveness of the configured network is illustrated using a sample, following which an exact method is used to solve this multi-objective problem and confirm the validity of the model, and finally the results will be discussed and analyzed.

• Journal of Electrical Engineering and Technology
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• v.4 no.4
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• pp.443-450
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• 2009

This paper deals with the design of decentralized controller for load-frequency control of interconnected power systems with superconducting magnetic energy storage units and Governor Dead Band Nonlinearity using Multi-Objective Evolutionary Algorithm. The superconducting magnetic energy storage unit exhibits favourable damping effects by suppressing the frequency oscillations as well as stabilizing the inter-area oscillations effectively. The proposed control strategy is mainly based on a compromise between Integral Squared Error and Maximum Stability Margin criteria. Analysis on a two-area interconnected thermal power system reveals that the proposed controller improves the dynamic performance of the system and guarantees good closed-loop stability even in the presence of nonlinearities and with parameter changes.

• Industrial Engineering and Management Systems
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• v.14 no.1
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• pp.11-21
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• 2015

The purpose of this research is to develop algorithms using the Differential Evolution Algorithm (DE) to solve a multi-objective, sources and stages location-allocation problem. The development process starts from the design of a standard DE, then modifies the recombination process of the DE in order improve the efficiency of the standard DE. The modified algorithm is called modified DE. The proposed algorithms have been tested with one real case study (large size problem) and 2 randomly selected data sets (small and medium size problems). The computational results show that the modified DE gives better solutions and uses less computational time than the standard DE. The proposed heuristics can find solutions 0 to 3.56% different from the optimal solution in small test instances, while differences are 1.4-3.5% higher than that of the lower bound generated by optimization software in medium and large test instances, while using more than 99% less computational time than the optimization software.

• Structural Engineering and Mechanics
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• v.43 no.6
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• pp.771-794
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• 2012

This paper presents a multi-objective evolutionary algorithm for combinatorial optimisation and applied for design optimisation of fiber reinforced composite structures. The proposed algorithm closely follows the implementation of Pareto Archive Evolutionary strategy (PAES) proposed in the literature. The modifications suggested include a customized neighbourhood search algorithm in place of mutation operator to improve intensification mechanism and a cross over operator to improve diversification mechanism. Further, an external archive is maintained to collect the historical Pareto optimal solutions. The design constraints are handled in this paper by treating them as additional objectives. Numerical studies have been carried out by solving a hybrid fiber reinforced laminate composite cylindrical shell, stiffened composite cylindrical shell and pressure vessel with varied number of design objectives. The studies presented in this paper clearly indicate that well spread Pareto optimal solutions can be obtained employing the proposed algorithm.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.513-524
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• 2006

The ammunition allocation problem is a Multi-objective optimization(MOO) problem, maximizing fill-rate of multiple user troops and minimizing transportation time. Recent studies attempted to solve this problem by the prior preference articulation approach such as goal programming. They require that all the preference information of decision makers(DM) should be extracted prior to solving the problem. However, the prior preference information is difficult to implement properly in a rapidly changing state of war. Moreover they have some limitations such as heavy cognitive effort required to DM. This paper proposes a new ammunition allocation model based on more reasonable assumptions and uses an interactive MOO method to the ammunition allocation problem to overcome the limitations mentioned above. In particular, this article uses the GDF procedure, one of the well-known interactive optimization methods in the MOO liter-ature, in solving the ammunition allocation problem.