• 한국경영과학회지
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• 제32권2호
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• pp.99-107
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• 2007

We consider (worst-case) robust optimization versions of the Economic Order Quantity (EOQ) model with decreasing cost functions. Two variants of the EOQ model are discussed, in which the purchasing costs are decreasing power functions in either the order quantity or demand rate. We develop the corresponding worst-case robust optimization models of the two variants, where the parameters in the purchasing cost function of each model are uncertain but known to lie in an ellipsoid. For the robust EOQ model with the purchasing cost being a decreasing function of the demand rate, we derive the analytical optimal solution. For the robust EOQ model with the purchasing cost being a decreasing function of the order quantity, we prove that it is a convex optimization problem, and thus lends itself to efficient numerical algorithms.

• Journal of Electrical Engineering and Technology
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• 제13권6호
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• pp.2262-2267
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• 2018

In electric machine design, there is a large computation cost for finite element analyses (FEA) when analyzing nonlinear characteristics in the machine Therefore, for the optimal design of an electric machine, designers commonly use an optimization algorithm capable of excellent convergence performance. However, robustness consideration, as this factor can guarantee machine performances capabilities within design uncertainties such as the manufacturing tolerance or external perturbations, is essential during the machine design process. Moreover, additional FEA is required to search robust optimum. To address this issue, this paper proposes a computationally efficient robust optimization algorithm. To reduce the computational burden of the FEA, the proposed algorithm employs a useful technique which termed static analysis assisted technique (SAAT). The proposed method is verified via the effective robust optimal design of electric machine to reduce cogging torque at a reasonable computational cost.

• Management Science and Financial Engineering
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• 제18권2호
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• pp.23-27
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• 2012

The problem of jointly determining a robust optimal bundle of price and order quantity for a retailer in a single-retailer, single supplier, single-product supply chain is considered. Demand is modeled as a decreasing power function of product price, and unit purchasing cost is modeled as a decreasing power function of order quantity and demand. Parameters defining the two power functions are uncertain but their possible values are characterized by ellipsoids. We extend a previous study in two ways; the purchasing cost function is generalized to take into account the economies of scale realized by higher product demand in addition to larger order quantity, and an exact transformation into an equivalent convex optimization program is developed instead of a geometric programming approximation scheme proposed in the previous study.

• Computers and Concrete
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• 제23권6호
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• pp.433-444
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• 2019

As rice husk ash (RHA) is not produced in controlled manufacturing process like cement, its properties vary significantly even within the same lot. In fact, properties of Rice Husk Ash Based Concrete (RHABC) are largely dictated by uncertainty leading to huge deviations from their expected values. This paper proposes a Robust Cost Optimization (RCO) procedure for RHABC, which minimizes such unwanted deviation due to uncertainty and provides guarantee of achieving desired strength and workability with least possible cost. The RCO simultaneously minimizes cost of RHABC production and its deviation considering feasibility of attaining desired strength and workability in presence of uncertainty. RHA related properties have been modeled as uncertain-but-bounded type as associated probability density function is not available. Metamodeling technique is adopted in this work for generating explicit expressions of constraint functions required for formulation of RCO. In doing so, the Moving Least Squares Method is explored in place of conventional Least Square Method (LSM) to ensure accuracy of the RCO. The efficiency by the proposed MLSM based RCO is validated by experimental studies. The error by the LSM and accuracy by the MLSM predictions are clearly envisaged from the test results. The experimental results show good agreement with the proposed MLSM based RCO predicted mix properties. The present RCO procedure yields RHABC mixes which is almost insensitive to uncertainty (i.e., robust solution) with nominal deviation from experimental mean values. At the same time, desired reliability of satisfying the constraints is achieved with marginal increment in cost.

• International Journal of Automotive Technology
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• 제4권4호
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• pp.193-201
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• 2003

The problem of brake judder is typically caused by defects of quality manufacturing. DFSS (Design for six sigma) is a design process for quality improvement. DFSS will result in more improved but less expensive quality products. This paper presents an implementation of DFSS for quality improvement of the brake judder of heavy-duty trucks. Carrying out 5 steps of DFSS, the major reasons for defects of quality are found. The numerical approximation of the brake system is derived by means of the response surface method. Its quality for brake judder is improved by using the sigma based robust design methodology. Results are compared between the conventional deterministic optimal design and the proposed sigma based robust design. The proposed one shows that manufacturing cost may increase as the quality level increase. The proposed one, however, is more economical in aspect of the overall cost since the probability of failure dramatically goes down.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
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• pp.40-43
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• 1996

To design high quality products at low cost is one of very important task for engineers Design optimization for performances can be one solution in this task. This is robust design which has been proved effectively in many field of engineering design. In this paper, the concept of robust design is introduced and combined to fuzzy optimization and nonsingleton fuzzy logic system. The optimum parameter set points were obtained by the fuzzy optimization method and nonsingleton fuzzy logic system. These methods are applied to a filter circuit, a part of the audio circuit of mobile radio transceiver. The results are compared each other.

• 전자공학회논문지SC
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• 제46권3호
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• pp.15-21
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• 2009

본 논문에서는 변수 불확실성을 가지는 이산시간 특이시스템의 강인 안정화 기법과 강인 보장비용 제어기법을 다룬다. 제안하는 제어기법은 제어기 존재조건에서 준정부호조건(semi-definite condition)이나 시스템 행렬의 분해 없이 볼록최적화(convex optimization)가 가능한 선형행렬부등식 접근방법을 이용하여 제안한다. 먼저, 강인 안정화 상태궤환 제어기는 폐루프 시스템의 정규성, 코잘 및 안정화를 만족하는 제어기의 존재조건과 설계방법을 선형행렬부등식으로 제시한다. 그리고 보장비용 함수의 상한치의 최소화를 보장하는 강인 보장비용 제어기 설계방법은 강인 안정화 제어기 설계를 기반으로 제안한다. 예제를 통하여 제안한 제어기 설계기법의 타당성을 확인한다

• 경영과학
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• 제24권2호
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• pp.73-80
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• 2007

Robust Design(RD) is a cost-effective methodology to determine the optimal settings of control factors that make a product performance insensitive to the influence of noise factors. To better facilitate the robust design optimization, a dual response surface approach, which models both the process mean and standard deviation as separate response surfaces, has been successfully accepted by researchers and practitioners. However, the construction of response surface approximations has been limited to problems with only a few variables, mainly due to an excessive number of experimental runs necessary to fit sufficiently accurate models. In this regard, an innovative response surface approach has been proposed to investigate robust design optimization problems with larger number of variables. Response surfaces for process mean and standard deviation are partitioned and estimated based on the multi-level approximation method, which may reduce the number of experimental runs necessary for fitting response surface models to a great extent. The applicability and usefulness of proposed approach have been demonstrated through an illustrative example.

• Journal of Magnetics
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• 제19권4호
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• pp.385-392
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• 2014

Uncertainties in loads, materials and manufacturing quality must be considered during electromagnetic devices design. This paper presents an effective methodology for robust optimization design based on the variance decomposition in order to keep higher accuracy of the robustness prediction. Sobol' theory is employed to estimate the response variance under some specific tolerance in design variables. Then, an optimal design is obtained by adding a criterion of response variance upon typical optimization problems as a constraint of the optimization. The main contribution of this paper is that the proposed method applies the variance decomposition to obtain a more accurate variance of the response, as well save the computational cost. The performance and robustness of the proposed algorithms are investigated through a numerical experiment with both an analytic function and the TEAM 22 problem.

• 대한기계학회논문집A
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• 제38권5호
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• pp.535-543
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• 2014

강건최적설계 분야에서 목적함수의 강건성은 목적함수의 변화가 둔감한 해를 강조한다. 일반적으로 목적함수의 강건성은 설계변수나 파라미터에 대한 목적함수의 변동을 줄임으로써 달성할 수 있다. 하지만, 기존의 방법들에서는 변동에 둔감한 목적을 달성하기 위해 목적함수의 값이 희생되는 경우가 있다. 또한, 설계변수의 수가 증가할수록 비선형계획법을 이용한 강건최적설계의 수치적 계산비용은 증가한다. 본 연구에서는 상한함수를 사용한 새로운 강건성지수와 비선형계획법에서의 강건최적설계 방법을 제안한다. 또한, 제안한 방법의 효율성을 향상시키기 위하여 선형화된 함수의 상한 값을 이용한 방법도 소개한다. 이를 다양한 수학예제에 적용하고 기존의 강건성지수와 수치적 성능 비교를 통해 제안한 방법의 유용성을 검증한다. 제안한 강건성지수는 목적함수의 성능에 손실이 발생하지 않으며 효율성을 크게 향상시킬 수 있다.