최근 노인 인구가 증가함에 따라, 이들의 삶의 질에 대한 사회적 관심도가 높아지고 있으며, 노인들의 건강하고 활기찬 노후를 고려하는 활동적 노후 및 고령친화도시의 개념이 주목받고 있다. 이러한 상황에서 많은 지자체는 노인들이 지역 사회에서 여생을 의미 있게 보낼 수 있도록 다양한 노인여가복지 서비스를 제공하기 위해 노력하고 있다. 그러나 실질적인 노인 수요에 부합하는 서비스 공급이 이루어지지 못하고 있으며, 지역별로 노인여가복지 서비스의 공간적 격차가 발생하고 있는 실정이다. 이는 노인여가복지 입지와 관련하여 체계적인 법적 기준이 부재하기 때문이다. 이에 본 연구는 서울시의 노인여가복지 시설에 대한 수요와 공급의 공간적 불일치성을 탐색하고, 공간 효율성과 형평성을 고려한 노인복지센터의 최적 입지 대안을 제시하고 있다. 연구 결과, 여러 입지 시나리오에 따라 서울시 노인복지센터의 공간적 접근성을 향상시킬 수 있는 다양한 최적 입지 대안들을 제시할 수 있었으며, 향후 노인복지 서비스 공급과 관련한 계획 및 정책에 있어 중요한 기초 자료로 활용될 수 있을 것으로 기대된다

Most of real-world decision-making processes are used to optimize problems with many objectives of conflicting. Since the betterment of some objectives requires the sacrifice of other objectives, different objectives may not be optimized simultaneously. Consequently, Pareto solution can be considered as candidates of a solution with respect to a multi-objective optimization (MOP). Such problem involves two main procedures: finding Pareto solutions and choosing one solution among them. So-called multi-objective genetic algorithms have been proved to be effective for finding many Pareto solutions. In this study, we suggest a fitness evaluation method based on the achievement level up to the target value to improve the solution search performance by the multi-objective genetic algorithm. Using numerical examples and benchmark problems, we compare the proposed method, which considers the achievement level, with conventional Pareto ranking methods. Based on the comparison, it is verified that the proposed method can generate a highly convergent and diverse solution set. Most of the existing multi-objective genetic algorithms mainly focus on finding solutions, however the ultimate aim of MOP is not to find the entire set of Pareto solutions, but to choose one solution among many obtained solutions. We further propose an interactive decision-making process based on a visualized trade-off analysis that incorporates the satisfaction of the decision maker. The findings of the study will serve as a reference to build a multi-objective decision-making support system.

This paper deals with a vehicle routing problem with resource repositioning (VRPRR) which is a variation of well-known vehicle routing problem with pickup and delivery (VRPPD). VRPRR in which static repositioning of public bikes is a representative case, can be defined as a multi-objective optimization problem aiming at minimizing both transportation cost and the amount of unmet demand. To obtain Pareto sets for the problem, famous multi-objective optimization algorithms such as Strength Pareto Evolutionary Algorithm 2 (SPEA2) can be applied. In addition, a linear combination of two objective functions with weights can be exploited to generate Pareto sets. By varying weight values in the combined single objective function, a set of solutions is created. Experiments accomplished with a standard benchmark problem sets show that Variable Neighborhood Search (VNS) applied to solve a number of single objective function outperforms SPEA2. All generated solutions from SPEA2 are completely dominated by a set of VNS solutions. It seems that local optimization technique inherent in VNS makes it possible to generate near optimal solutions for the single objective function. Also, it shows that trade-off between the number of solutions in Pareto set and the computation time should be considered to obtain good solutions effectively in case of linearly combined single objective function.

In this study, a model to optimize residual chlorine concentrations in a water supply system was developed using a multi-objective genetic algorithm. Moreover, to quantify the effects of optimized residual chlorine concentration management and to consider customer service requirements, this study developed indices to quantify the spatial and temporal distributions of residual chlorine concentration. Based on the results, the most economical operational method to manage booster chlorination was derived, which would supply water that satisfies the service level required by consumers, as well as the cost-effectiveness and operation requirements relevant to the service providers. A simulation model was then created based on an actual water supply system (i.e., the Multi-regional Water Supply W in Korea). Simulated optimizations were successful, evidencing that it is possible to meet the residual chlorine concentration demanded by consumers at a low cost.

To make a satisfactory decision regarding project scheduling, a trade-off between the resource-related cost and project duration must be considered. A beneficial method for decision makers is to provide a number of alternative schedules of diverse project duration with minimum resource cost. In view of optimization, the alternative schedules are Pareto sets under multi-objective of project duration and resource cost. Assuming that resource cost is closely related to resource leveling, a heuristic algorithm for resource capacity reduction (HRCR) is developed in this study in order to generate the Pareto sets efficiently. The heuristic is based on the fact that resource leveling can be improved by systematically reducing the resource capacity. Once the reduced resource capacity is given, a schedule with minimum project duration can be obtained by solving a resource-constrained project scheduling problem. In HRCR, VNS (Variable Neighborhood Search) is implemented to solve the resource-constrained project scheduling problem. Extensive experiments to evaluate the HRCR performance are accomplished with standard benchmarking data sets, PSPLIB. Considering 5 resource leveling objective functions, it is shown that HRCR outperforms well-known multi-objective optimization algorithm, SPEA2 (Strength Pareto Evolutionary Algorithm-2), in generating dominant Pareto sets. The number of approximate Pareto optimal also can be extended by modifying weight parameter to reduce resource capacity in HRCR.

This paper will present a simulation-optimization model for the scheduling of multi-projects. The objectives of this research include the minimization of value added projects execution cost, project completion time, project tardiness, and underutilization of contracted or outsourced resources. It is the three-phase research. In first phase, a mathematical and simulation models will be developed for multi-objectives. In second phase simulation model will be coupled with genetic algorithm to form a simulation-optimization model. The efficiency of genetic algorithm (GA) will be improved simultaneously with fine-tuning and hybridizing with other algorithms. The third phase will involve the presentation of a numerical example for the real time application of proposed research. Solution of numerical obtained with fine-tuned and hybridized simulation integrated GA will be compared with already available methods of simulation-optimization. This research will be useful for the scheduling of projects to achieve the befits of high profit, effective resource utilization, and customer satisfaction with on time delivery of projects.

Direct spring loaded pressure relief valve(DSLPRV) is a safety valve to relax surge pressure of the pipeline system. DSLPRV is one of widely used safety valves for its simplicity and efficiency. However, instability of the DSLPRV can caused by various reasons such as insufficient valve volume, natural vibration of the spring, etc. In order to improve reliability of DSLPRV, proper selection of design factors of DSLPRV is important. In this study, methodology for selecting design factors for DSLPRV was proposed. Dynamics of the DSLPRV disk was integrated into conventional 1D surge pressure analysis. Multi-objective genetic algorithm was also used to search optimum design factors for DSLPRV.

The application of the theoretical model to real assembly lines has been one of the biggest challenges for researchers and industrial engineers. There should be some realistic approach to achieve the conflicting objectives on real systems. Therefore, in this paper, a model is developed to synchronize a real system (A discrete event simulation model) with a theoretical model (An optimization model). This synchronization will enable the realistic optimization of systems. A job assignment model of the assembly line is formulated for the evaluation of proposed realistic optimization to achieve multiple conflicting objectives. The objectives, fluctuation in cycle time, throughput, labor cost, energy cost, teamwork and deviation in the skill level of operators have been modeled mathematically. To solve the formulated mathematical model, a multi-objective simulation integrated hybrid genetic algorithm (MO-SHGA) is proposed. In MO-SHGA each individual in each population acts as an input scenario of simulation. Also, it is very difficult to assign weights to the objective function in the traditional multi-objective GA because of pareto fronts. Therefore, we have proposed a probabilistic based linearization and multi-objective to single objective conversion method at population evolution phase. The performance of MO-SHGA is evaluated with the standard multi-objective genetic algorithm (MO-GA) with both deterministic and stochastic data settings. A case study of the goalkeeping gloves assembly line is also presented as a numerical example which is solved using MO-SHGA and MO-GA. The proposed research is useful for the development of synchronized human based assembly lines for real time monitoring, optimization, and control.

The application of the theoretical model to real assembly lines has been one of the biggest challenges for researchers and industrial engineers. There should be some realistic approach to achieve the conflicting objectives on real systems. Therefore, in this paper, a model is developed to synchronize a real system (A simulation model) with a theoretical model (An optimization model). This synchronization will enable the realistic optimization of systems. A job assignment model of the assembly line is formulated for the evaluation of proposed realistic optimization with multiple objectives. The objectives, fluctuation in cycle time, throughput, labor cost, energy cost, teamwork and deviation in the skill level of operators have been modeled mathematically. To solve the formulated mathematical model, a multi-objective simulation integrated hybrid genetic algorithm (MO-SHGA) is proposed. The performance of MO-SHGA is evaluated with the standard multi-objective genetic algorithm (MO-GA) with both deterministic and stochastic data settings. A case study of the goalkeeping gloves assembly line is also presented as a numerical example which is solved using MO-SHGA and MO-GA.

Optimal design of the water supply pipe network aims to minimize construction cost while satisfying the required hydraulic constraints such as the minimum and maximum pressures, and velocity. Since considering one single design factor (i.e., cost) is very vulnerable for including future conditions and cannot satisfy operator’s needs, various design factors should be considered. Hence, this study presents three kinds of design factors (i.e., minimizing construction cost, maximizing reliability, and surplus head) to perform multi-objective optimization design. Harmony Search (HS) Algorithm is used as an optimization technique. As well-known benchmark networks, Hanoi network and Gyeonggi-do P city real world network are used to verify the applicability of the proposed model. In addition, the proposed multi-objective model is also applied to a real water distribution networks and the optimization results were statistically analyzed. The results of the optimal design for the benchmark and real networks indicated much better performance compared to those of existing designs and the other approach (i.e., Genetic Algorithm) in terms of cost and reliability, cost, and surplus head. As a result, this study is expected to contribute for the efficient design of water distribution networks.

Recently, a concept of damped outrigger system has been proposed for tall buildings. Structural characteristics and design method of this system were not sufficiently investigated to date. In this study, control performance of damped outrigger system for building structures subjected to seismic excitations has been investigated. And optimal design method of damped outrigger system has been proposed using multi-objective genetic algorithm. To this end, a simplified numerical model of damped outrigger system has been developed. State-space equation formulation proposed in previous research was used to make a numerical model. Multi-objective genetic algorithms has been employed for optimal design of the stiffness and damping parameters of the outrigger damper. Based on numerical analyses, it has been shown that the damped outrigger system control dynamic responses of the tall buildings subjected to earthquake excitations in comparison with a traditional outrigger system.

Reduction of microvibration is regarded as important in high-technology facilities with high precision equipments. In this paper, smart control technology is used to improve the microvibration control performance. Mr damper is used to make a smart base isolation system amd fuzzy logic control algorithm is employed to appropriately control the MR damper. In order to develop optimal fuzzy control algorithm, a multi-objective genetic algorithm is used in this study. As an excitation, a train-induced ground acceleration is used for time history analysis and three-story example building structure is employed. Microvibration control performance of passive and smart base isolation systems have been investigated in this study. Numerical simulation results show that the multi-objective genetic algorithm can provide optimal fuzzy logic controllers for smart base isolation system and the smart control system can effectively reduce microvibration of a high-technology facility subjected to train-induced excitation.

초고층 건물의 구조설계시 풍하중에 의한 횡방향 변위를 적절한 값 이내로 줄이는 것이 가장 중요한 문제 중에 하나이다. 이를 위해서 추가적인 감쇠기 및 진동제어장치를 사용하는 방법이 일반적으로 고려되고 있다. 이 때 일반적으로 구조물의 특성은 변화없이 추가되는 제어장치에 대해서만 최적설계를 수행하게 된다. 본 연구에서는 구조물과 스마트 제어장치의 다목적 통합 최적화를 통하여 추가되는 스마트 제어장치로 인하여 구조물의 물량을 줄일 수 있는 가능성을 검토하였다. 이를 위하여 다이어그리드 구조시스템이 적용된 60층 초고층 건물을 예제 구조물로 선택하였고, 인공 풍하중에 대한 풍응답을 검토하였다. 스마트 제어장치로는 TMD에 MR 감쇠기를 설치한 스마트 TMD를 사용하였다. 구조물의 응답과 구조물량 및 제어장치의 용량을 동시에 줄이는 것이 필요하므로 본 연구에서는 다목적 유전자알고리즘을 적용하였다. 수치해석결과 제어성능목표를 만족시키면서 구조물의 물량과 제어장치의 용량을 적절하게 줄일 수 있는 다양한 설계 최적안을 얻을 수 있었다.

At present, the significance of a new manufacturing system that can shift from ‘mass production’ and consider life cycles of a product is pointed out and extremely expected. In such a situation, it is recognized that the modular design, often called ‘unit design,’ is the important design methodology which realizes the new production system enabling ‘cost reduction,’ ‘flexible production of a multi-functional artifact,’ ‘settlement of an environmental issue,’ and so on. A module (unit) of a product is generally defined as ‘the parts group made into the sub-system from a certain specific viewpoint.’ So far, there have been many researches related to the modular design. However, they are often limited to a certain viewpoint (objective). This paper proposes a simple but effective method for multi-objective modular design. In the proposed method, a new design metric, called similarity index, is proposed to evaluate the modular design candidates from the multiple viewpoints.

Various computer-based simulation tools such as 3D-CAD and CAE systems are widely used to design automotive body structure at the early phase of design. Designers must search the optimal solution that satisfies a number of performance requirements by usin

The design flexibility and robustness have become key factors to handle various sources of uncertainties at the early phase of design. Even though designers are uncertain about which single values to specify, they usually have a preference for certain val

The computer-based simulation tools are currently used overwhelmingly to simulate the performance of automotive designs. Then, the search for an optimal solution that satisfies a number of performance requirements usually involves numerous iterations amon

The early phase of design intrinsically contains multiple sources of uncertainty in describing design, and nevertheless the decision-making process at this phase exerts a critical effect upon drawing a successful design. This paper proposes a set-based de

본 연구에서는 지진하중을 받는 대공간구조물의 동적응답을 저감시키기 위하여 스마트 면전시스템을 제안하였다. MR 감쇠기와 저감쇠 탄성베어링을 사용하여 스마트 면진시스템을 구성하였으며 최적설계된 LRB 면진시스템과 비교하여 진동 제어성능을 검토하였다. 스마트 면진시스템은 제어알고리즘에 따라서 제어성능이 크게 좌우된다. 본 연구에서는 스마트 면진시스템이 설치된 대공간 구조물을 효과적으로 제어하기 위하여 퍼지제어기를 사용하였다. 면전시스템이 적용된 대공간 구조물의 동적응답과 면진층 변위는 서로 상충관계가 있으므로 퍼지제어기를 최적화하기 위하여 두 응답을 목적함수로 하는 다목적 유전자알고리즘을 사용하여하였다. 수치해석결과 본 연구에서 제안한 스마트 면진시스템을 적용하면 최적설계된 LRB 시스템에 비하여 면진층 변위 및 대공간 구조물의 동적응답을 대폭 줄일 수 있는 것을 확인하였다.

본 논문에서는 비선형 지진격리교량의 최적 설계 방법을 제시하였다. 최적설계를 위한 목적함수로는 교각과 지진격리장치의 파괴확률을 고려하였으며, 상충하는 두 목적함수를 동시에 최적화하는 다수의 해를 효율적으로 검색하고자 유전자 알고리즘에 기반한 다목적 최적화기법을 도입하였다. 또한, 최적화 과정에서 요구되는 다수의 비선형 시간이력해석을 수행하지 않고도 교량의 확률적 응답을 효율적으로 예측할 수 있는 추계학적 선형화 방법을 접목하였다. 제시하는 방법의 효율성을 검증하기 위한 수치 예로서 실제 교량인 남한강교를 고려하였고, 제안하는 방법과 기존 비선형 시간이력해석을 이용한 생애주기비용 기반 설계법을 각각 적용하여 내진성능을 비교하였다. 내진성능을 비교한 결과, 제시하는 방법이 기존의 비용에 기반한 최적설계보다 우수한 성능 및 경제성을 보임을 검증하였다. 또한, 다양한 지진하중에 대해서도 제안된 방법이 보다 개선된 성능을 보임을 확인하였다.