• 지능정보연구
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• 제12권3호
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• pp.33-46
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• 2006

물류의 증가로 생산된 제품이 소비자에게 전달되기 전 저장하는 제품 창고의 운영과 유지는 제품원가의 큰 부분을 차지하게 되었다. 취급작업 횟수의 감소, 신속한 출고 작업, 제품의 효율적 관리를 위해 저장설비의 설치, 저장 공간의 추가 확보 등의 시설 투자 방법으로도 이런 문제를 해결 할 수 있지만 기존 저장 공간을 최대한 활용함으로써 취급작업 수를 줄일 수 있다. 저장시설 내에서의 운영방법에는 여러 가지가 있겠지만 저장 공간의 제약이 있기 때문에 제품을 겹쳐 쌓아야 하고 그 경우 반드시 재취급을 고려해야 한다. 재취급 문제는 창고의 취급 효율을 결정짓는 가장 중요한 문제이다. 따라서 창고의 운영 효율을 높이기 위해서는 재취급을 최소화할 수 있는 방법을 고려할 필요가 있다. 본 연구에서는 기대 재취급을 최소화하는 혼적결정 문제를 다루고자 한다. 혼적결정을 위한 최적화 모형을 제시하였고 해를 구하기 위한 유전자 알고리즘을 제시하였다. 이 연구결과는 컨테이너 터미널을 포함하여 재취급이 발생할 수 있는 창고의 운영에 활용될 수 있다.

• 한국농공학회논문집
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• 제64권6호
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• pp.43-53
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• 2022

Rice paddy has been actively converted into upland crop fields as more profitable upland crop cultivation are encouraged along with the decrease in rice consumption. However, the current water supply system remains mainly for paddy water supply, so research on pipeline water supply for upland cultivation is needed. The objective of this study was to optimize storage tank installation locations for pipeline water supply in reservoir irrigation districts. Five of reservoir irrigation districts were selected as the study sites and gridded of 10×10 m in size. Then genetic algorithm was adopted to evaluate the effects of spatial storage tank allocation on total pipeline cost. The lengths of the main and branch pipelines were considered as the objective cost function for the optimization of storage tank installation. Overall the shorter the branch pipeline and the longer the main pipeline, as the number of storage tanks increase. The minimal pipeline cost, i.e., optimal condition was reached when approximately 10% of the storage tank numbers to total upland plots were installed. The methodology presented in this study can be applied to determine the number and spatial arrangement of storage tanks for upland pipeline irrigation system design.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2013년도 춘계학술대회 논문집
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• pp.1167-1168
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• 2013

• Journal of Electrical Engineering and Technology
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• 제10권5호
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• pp.1940-1949
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• 2015

This paper presents an Enhanced Particle Swarm Optimization (EPSO) to solve short-term hydrothermal scheduling (STHS) problem with non-convex fuel cost function and a variety of operational constraints related to hydro and thermal units. The operators of the conventional PSO are dynamically controlled using exponential functions for better exploration and exploitation of the search space. The overall methodology efficiently regulates the velocity of particles during their flight and results in substantial improvement in the conventional PSO. The effectiveness of the proposed method has been tested for STHS of two standard test generating systems while considering several operational constraints like system power balance constraints, power generation limit constraints, reservoir storage volume limit constraints, water discharge rate limit constraints, water dynamic balance constraints, initial and end reservoir storage volume limit constraints, valve-point loading effect, etc. The application results show that the proposed EPSO method is capable to solve the hard combinatorial constraint optimization problems very efficiently.

• 한국농공학회논문집
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• 제47권3호
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• pp.3-13
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• 2005

The conceptual rainfall-runoff models are used to predict complex hydrological effects of a basin. However, to obtain reliable results, there are some difficulties and problems in choosing optimum model, calibrating, and verifying the chosen model suitable for hydrological characteristics of the basin. In this study, Genetic Algorithm and SCE-UA method as global optimization methods were applied to compare the each optimization technique and to analyze the application for the rainfall-runoff models. Modified TANK model that is used to calculate outflow for watershed management and reservoir operation etc. was optimized as a long term rainfall-runoff model. And storage-function model that is used to predict real-time flood using historical data was optimized as a short term rainfall-runoff model. The optimized models were applied to simulate runoff on Pyeongchang-river watershed and Bocheong-stream watershed in 2001 and 2002. In the historical data study, the Genetic Algorithm and the SCE-UA method showed consistently good results considering statistical values compared with observed data.

• 한국해양공학회지
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• 제24권1호
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• pp.153-160
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• 2010

This paper deals with the topology optimized design of the riser support structures for floating production storage and offloading units (FPSOs) under global and local loading conditions. For a preliminary study and validation of the numerical approach, a simplified plate under static loading is first evaluated with the representative topology optimization methods, the Homogenization Design Method (HDM) and Density Method (DM) or Simple Isotropic Material with Penalization (SIMP). In the context of the corresponding riser support structures, the design problem is formulated such that structure shapes based on design domain variables are determined by minimizing the compliance subject to a mass target, considering the stress criterion. An initial design model is generated based on an actual FPSO riser support configuration. The topology optimization results present improved design performances under various loading conditions, while staying within the allowable limit of the offshore area.

• 대한임베디드공학회논문지
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• 제18권1호
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• pp.31-40
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• 2023

Recent advances in flash technologies, such as 3D processing and multileveling schemes, have successfully increased the flash capacity. Unfortunately, these technology advances significantly degrade flash's reliability due to a smaller cell geometry and a finer-grained cell state control. In this paper, we propose an asymmetric BER-aware reliability optimization technique (aBARO), new flash optimization that improves the flash reliability. To this end, we first reveal that bit errors of 3D NAND flash memory are highly skewed among flash cell states. The proposed aBARO exploits the unique per-state error model in flash cell states by selecting the most error-prone flash states and by forming narrow threshold voltage distributions (for the selected states only). Furthermore, aBARO is applied only when the program time (tPROG) gets shorter when a flash cell becomes aging, thereby keeping the program latency of storage systems unchanged. Our experimental results with real 3D MLC and TLC flash devices show that aBARO can effectively improve flash reliability by mitigating a significant number of bit errors. In addition, aBARO can also reduce the read latency by 40%, on average, by suppressing the read retries.

• Journal of Electrical Engineering and Technology
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• 제12권5호
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• pp.1709-1718
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• 2017

This paper presents a hybrid stochastic deterministic multi-timescale scheduling (SDMS) approach for generation scheduling of a power grid. SDMS considers flexible resource options including conventional generation flexibility in a chance-constrained day-ahead scheduling optimization (DASO). The prime objective of the DASO is the minimization of the daily production cost in power systems with high penetration scenarios of variable generation. Furthermore, energy storage is scheduled in an hourly-ahead deterministic real-time scheduling optimization (RTSO). DASO simulation results are used as the base starting-point values in the hour-ahead online rolling RTSO with a 15-minute time interval. RTSO considers energy storage as another source of grid flexibility, to balance out the deviation between predicted and actual net load demand values. Numerical simulations, on the IEEE RTS test system with high wind penetration levels, indicate the effectiveness of the proposed SDMS framework for managing the grid flexibility to meet the net load demand, in both day-ahead and real-time timescales. Results also highlight the adequacy of the framework to adjust the scheduling, in real-time, to cope with large prediction errors of wind forecasting.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1859-1864
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• 2004

An effective methodology is reported for the optimal design of multisite batch production/transportation and storage networks under uncertain demand forecasting. We assume that any given storage unit can store one material type which can be purchased from suppliers, internally produced, internally consumed, transported to or from other plant sites and/or sold to customers. We further assume that a storage unit is connected to all processing and transportation stages that consume/produce or move the material to which that storage unit is dedicated. Each processing stage transforms a set of feedstock materials or intermediates into a set of products with constant conversion factors. A batch transportation process can transfer one material or multiple materials at once between plant sites. The objective for optimization is to minimize the probability averaged total cost composed of raw material procurement, processing setup, transportation setup and inventory holding costs as well as the capital costs of processing stages and storage units. A novel production and inventory analysis formulation, the PSW(Periodic Square Wave) model, provides useful expressions for the upper/lower bounds and average level of the storage inventory. The expressions for the Kuhn-Tucker conditions of the optimization problem can be reduced to two sub-problems. The first yields analytical solutions for determining lot sizes while the second is a separable concave minimization network flow subproblem whose solution yields the average material flow rates through the networks for the given demand forecast scenario. The result of this study will contribute to the optimal design and operation of large-scale supply chain system.

• 제어로봇시스템학회논문지
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• 제10권6호
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• pp.537-544
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• 2004

An effective methodology is reported for determining the optimal lot size of batch processing and storage networks which include uncertain demand forecasting. We assume that any given storage unit can store one material type which can be purchased from suppliers, internally produced, infernally consumed, transported to or from other sites and/or sold to customers. We further assume that a storage unit is connected to all processing and transportation stages that consume/produce or move the material to which that storage unit is dedicated. Each processing stage transforms a set of feedstock materials or intermediates into a set of products with constant conversion factors. A batch transportation process can transfer one material or multiple materials at once between sites. The objective for optimization is to minimize the probability averaged total cost composed of raw material procurement, processing setup, transportation setup and inventory holding costs as well as the capital costs of processing stages and storage units. A novel production and inventory analysis formulation, the PSW(Periodic Square Wave) model, provides useful expressions for the upper/lower bounds and average level of the storage inventory. The expressions for the Kuhn-Tucker conditions of the optimization problem can be reduced to two sub-problems. The first yields analytical solutions for determining lot sires while the second is a separable concave minimization network flow subproblem whose solution yields the average material flow rates through the networks for the given demand forecast scenario. The result of this study will contribute to the optimal design and operation of the global supply chain.