• International Journal of Air-Conditioning and Refrigeration
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• 제13권2호
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• pp.71-82
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• 2005

The shape of $7\times7$ pin-fins heat sink is optimized numerically to obtain the minimum pressure drop and thermal resistance. In this study, the fin height (h), fin width (w), and fan-to-heat sink distance (c) are chosen as the design variables and the pressure drop $({\Delta}P)$ and thermal resistance $(\theta_j)$ are adopted as the objective functions. To obtain the optimum design values, we used the finite volume method for calculating the objective functions, the BFGS method for solving the unconstrained non-linear optimization problem, and the weighting method for predicting the multi-objective problem. The results show that the optimum design variables for the weighting coefficient of 0.5 are as follows: W=4.653 mm, h=59.215mm, and c=2.667mm. The objective functions corresponding to the optimal design are calculated as ${\Delta}P=6.82$ Pa and $(\theta_j)=0.56K/W$. The Pareto solutions are also presented for various weighting coefficients and they will offer very useful data to design the pin-fins heat sink.

• Journal of Computational Design and Engineering
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• 제2권3호
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• pp.157-164
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• 2015

This paper discusses an optimization-based approach for the design of a product platform for industrial three-axis linear-type robots, which are widely used for handling objects in manufacturing lines. Since the operational specifications of these robots, such as operation speed, working distance and orientation, weight and shape of loads, etc., will vary for different applications, robotic system vendors must provide various types of robots efficiently and effectively to meet a range of market needs. A promising step toward this goal is the concept of a product platform, in which several key elements are commonly used across a series of products, which can then be customized for individual requirements. However the design of a product platform is more complicated than that of each product, due to the need to optimize the design across many products. This paper proposes an optimization-based fundamental framework toward the design of a product platform for industrial three-axis linear-type robots; this framework allows the solution of a complicated design problem and builds an optimal design method of fundamental features of robot frames that are commonly used for a wide range of robots. In this formulation, some key performance metrics of the robot are estimated by a reducedorder model which is configured with beam theory. A multi-objective optimization problem is formulated to represent the trade-offs among key design parameters using a weighted-sum form for a single product. This formulation is integrated into a mini-max type optimization problem across a series of robots as an optimal design formulation for the product platform. Some case studies of optimal platform design for industrial three-axis linear-type robots are presented to demonstrate the applications of a genetic algorithm to such mathematical models.

• 산업경영시스템학회지
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• 제40권1호
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• pp.95-104
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• 2017

This paper deals with solution methods for discrete and multi-valued optimization problems. The objective function of the problem incorporates noise effects generated in case that fitness evaluation is accomplished by computer based experiments such as Monte Carlo simulation or discrete event simulation. Meta heuristics including Genetic Algorithm (GA) and Discrete Particle Swarm Optimization (DPSO) can be used to solve these simulation based multi-valued optimization problems. In applying these population based meta heuristics to simulation based optimization problem, samples size to estimate the expected fitness value of a solution and population (particle) size in a generation (step) should be carefully determined to obtain reliable solutions. Under realistic environment with restriction on available computation time, there exists trade-off between these values. In this paper, the effects of sample and population sizes are analyzed under well-known multi-modal and multi-dimensional test functions with randomly generated noise effects. From the experimental results, it is shown that the performance of DPSO is superior to that of GA. While appropriate determination of population sizes is more important than sample size in GA, appropriate determination of sample size is more important than particle size in DPSO. Especially in DPSO, the solution quality under increasing sample sizes with steps is inferior to constant or decreasing sample sizes with steps. Furthermore, the performance of DPSO is improved when OCBA (Optimal Computing Budget Allocation) is incorporated in selecting the best particle in each step. In applying OCBA in DPSO, smaller value of incremental sample size is preferred to obtain better solutions.

• 한국지진공학회:학술대회논문집
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• 한국지진공학회 2006년도 학술발표회 논문집
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• pp.401-408
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• 2006

An optimum design method for hybrid control systems is proposed in this study. By considering both active and passive control systems as a combined or a hybrid system, the optimization of the hybrid system can be achieved simultaneously. In the proposed approach, we consider design parameters of active control devices and the elements of the feedback gain matrix as design variables for the active control system. Required quantity of the added dampers are also treated as design variables for the passive control system. In the proposed method, the cost of both active and passive control devices, the required control efforts and dynamic responses of a target structure are selected as objective functions to be minimized. To effectively address the multi-objective optimization problem, we adopt a preference-based optimization model and apply a genetic algorithm as a numerical searching technique. As an example to verify the validity of the proposed optimization technique, a wind-excited 20-storey building with hybrid control systems is used and the results are presented.

• 한국항해항만학회지
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• 제33권4호
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• pp.271-275
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• 2009

A strategic decision making on location selection for product transportation includes many tangible and untangible factors. To choose the best locations is a difficult job in the sense that objectives usually conflict with each other. In this paper, we consider a multi stage multi criteria transshipment problem with different types of items to be transported from the sources to the destination points. For the optimization of the problem, a goal programming formulation will be presented in which the location selection for each product type will be determined under the multi objective criteria. In the study, we generalize the transshipment model with a variety of product types and finite number of different intermediate nodes between origins and destinations. For the selection of the criteria we selected the costs(fixed cost and transportation cost), location numbers, and unsatisfied demand for each type of products in multi stage transportation, which are the main goals in transshipment modelling problems. The related conditions are also modelled through linear formats.

• 산업경영시스템학회지
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• 제34권2호
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• pp.112-120
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• 2011

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 design approach for multi-objective design problem under uncertainty. The proposed design approach consists of four design processes including set representation, set propagation, set modification, and set narrowing. This approach enables the flexible and robust design while incorporating designer's preference structure. In contrast to existing optimization techniques, this approach generates a ranged set of design solutions that satisfy changing sets of performance requirements.

• 산업경영시스템학회지
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• 제34권4호
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• pp.138-144
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• 2011

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 values over others. In the first and second reports of a four-part paper, a set-based design approach has been proposed for achieving design flexibility and robustness while capturing designer's preference, and its effectiveness has been illustrated with a simple vehicle side-door impact beam design problem. This report presents the applicability of the proposed design approach to the large-scale multi-objective design optimization with a successful implementation of real vehicle side-door structure design.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2002년도 추계학술대회 및 정기총회
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• pp.395-398
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• 2002

본 논문에서는 ‘다목적 함수 최적화 문제(Multi-objective Optimization Problem MOP)’를 풀기 위하여 유전자 알고리즘을 진화적 게임 이론 적용시킨 ‘내쉬 유전자 알고리즘(Nash GA)’과 본 논문에서 새로이 제안하는 공진화 알고리즘의 구조를 설명하고 이 두 알고리즘의 결과를 시뮬레이션을 통하여 비교 검토함으로써 ‘진화적 게임 이론(Evolutionary Game Theory : EGT)’의 두 가지 아이디어 -‘내쉬의 균형(Equilibrium)’과 ‘진화적 안정전략(Evolutionary Stable Strategy . ESS)’-에 기반한 최적화 알고리즘들이 다목적 함수 문제의 최적해를 탐색할 수 있음을 확인한다.

• 국제학술발표논문집
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• The 2th International Conference on Construction Engineering and Project Management
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• pp.642-652
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• 2007

Many contracting firms and project managers in the construction industry have started to utilize multi objective optimization methods to handle multiple conflicting goals for completing the project within the stipulated time and budget with required quality and safety. These optimization methods have increased the pressure on decision makers to search for an optimal resources utilization plan that optimizes simultaneously the total project cost, completion time, and crashing cost by considering indirect cost, contractual penalty cost etc., practically charging them in terms of direct cost of the project which is fuzzy in nature. This paper presents a multiple fuzzy goal programming model (MFGP) that supports decision makers in performing the challenging task. The model incorporates the fuzziness which stems from the imprecise aspiration levels attained by the decision maker to these objectives that are quantified through fuzzy linear membership function. The membership values of these objectives are then maximized which forms the fuzzy decision. The problem is solved using LINGO 8 optimization solver and the best compromise solution is identified. Comparison between solutions of MFGP, fuzzy multi objective linear programming (FMOLP) and multiple goal programming (MGP) are also presented. Additionally, an interactive decision making process is developed to enable the decision maker to interact with the system in modifying the fuzzy data and model parameters until a satisfactory solution is obtained. A case study is considered to demonstrate the feasibility of the proposed model for optimization of project network parameters in the construction industry.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 추계학술대회논문집A
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• pp.378-383
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• 2001

In order to improve automobile maneuverability and tire durability, it is very important for one to determine a suitable sidewall contour producing the ideal tension and strain-energy distributions. In order to determine such a sidewall contour, one must apply multi-objective optimization technique. The optimization problem of tire carcass contour involves several objective functions. Hence, we execute the tire contour optimization for improving the maneuverability and the tire durability using satisficing trade-off method. And, the tire optimization also requires long cup time for the sensitivity analysis. In order to resolve this numerical difficulty, we apply neural network algorithm.