• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.402-402
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• 2019

최근 기후변화에 따른 용수사용량의 계절별 변화가 나타나고 있다. 따라서, 효율적인 용수 관리에 대한 관심은 배수지 및 송수 시스템의 운영을 넘어 정수장의 운영에서도 그 변화가 나타나고 있다. 수질관리 측면에 다소 집중되었던 정수장 운영의 중요도는 수량을 함께 관리하는 방향으로 변화할 것으로 전망되며, 따라서 취수 단계에서부터 용수 공급의 전 과정을 고려하는 지능형 정수장 관리시스템이 주목받고 있다. 상수도 공급을 위한 정수장의 운영은 크게 원수의 취수 및 도수, 정수처리, 정수된 용수의 저장, 배수 및 급수의 과정으로 구분할 수 있다. 이때, 원수의 취수와 도수, 정수처리 과정에는 상대적으로 긴 시간이 소요되므로, 정수장의 운영 관리자는 이러한 지연시간을 감안해서 배수지의 상태를 예측하여 취수계획을 결정해야 한다. 한편, 정수장 시설을 운영하기 위해서는 전력이 소모되며, 산업전력 단가는 시간대별 변동폭이 큰 것으로 알려져 있다. 따라서, 정수장의 효율적인 운영을 위해서는 용수의 수요예측과 배수지 수위변동, 취수 및 정수설비의 규모 등을 고려하는 동시에, 전력 단가가 낮은 시간대에 설비를 집중적으로 운영할 수 있는 계획을 수립해야 한다. 본 연구에서는 선형계획법(Linear Programming, LP)을 이용하여, 수요예측을 바탕으로 장기취수계획을 수립하기 위한 방안을 세 가지로 구분하였으며, 각각의 장단점을 다음과 같이 예상하였다. 1) 24시간 간격으로 시간당 취수계획을 수립하는 최적화 방안, 2) 24시간의 시간당 취수계획을 1시간 간격으로 수립하는 실시간 최적화 방안, 3) 전체 모의기간 동안의 시간당 취수계획을 한번에 수립하는 최적화 방안. 24시간 간격 최적화는 수립 및 적용이 간단한 반면, 실시간 수요변화를 고려할 수 없어 단위시간(24시간) 후반부의 최적화 효율이 떨어지는 단점이 있다. 1시간 간격의 실시간 최적화는 수요변화를 가장 정확히 반영하는 반면, 최적화 수행 횟수가 증가하는 단점이 있다. 전체 모의기간 최적화는 장기 수요예측을 고려한 탄력적 취수계획을 수립하는 반면, 수요예측의 불확실성에 따른 오차 발생위험이 크다. 본 연구에서는 국내 H 정수장을 대상으로 각각의 최적 취수계획 수립 방안을 정수장 운영의 안정성, 탄력성, 경제성 등을 기준으로 비교, 분석하였다.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.2
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• pp.83-88
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• 2007

본 논문에서 선형함수의 곱의 형태로 표현된 비선형 함수를 목적식 또는 제약식에 가지는 비선형 최적화 문제를 새로운 변수를 추가하여 선형 Relaxation 최적화 문제로 Reformulation 하는 기법을 소개한다. 특히, 선형함수의 곱의 형태를 가지는 비선형 함수를 포함하는 비선형 정수 최적화 문제를 선형 정수 최적화 문제로 Relaxation할 경우 두 최적화 문제의 해가 일치함을 보인다. 또한 소개된 Relaxation 기법을 응용하여, 추가되는 변수의 수를 증가시킴으로서, 보다 Tight한 Relaxation 문제를 도출하는 과정에 대하여 소개한다.

• Journal of Korea Water Resources Association
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• v.52 no.8
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• pp.565-573
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• 2019

An optimization model of water intake planning is developed based on a linear programming (LP) for the intelligent water purification plant operation system. The proposed optimization model minimizes the water treatment costs of raw water purification by considering a time-delay of treatment process and hourly electricity tariff, which is subject to various operation constraints, such as water intake limit, storage tank capacity, and water demand forecasts. For demonstration, the developed model is applied to H water purification center. Here, we have tested three optimization strategies and the results are compared and analyzed in economic and safety aspects. The optimization model is expected to be used as a decision support tool for optimal water intake scheduling of domestic water purification centers.

• Journal of the Korea Society of Computer and Information
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• v.14 no.2
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• pp.27-35
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• 2009

Integer programming is a very effective technique for searching optimal solution of combinatorial optimization problems. However, its applicability is limited to linear models. In this paper, I propose an effective method for solving a nonlinear optimization problem by integrating the powerful search performance of integer programming and the flexibility of neighborhood search algorithms. In the first phase, integer programming is executed with subproblem which can be represented as a linear form from the given problem. In the second phase, a neighborhood search algorithm is executed with the whole problem by taking the result of the first phase as the initial solution. Through the experimental results using a nonlinear maximal covering problem, I confirmed that such a simple integration method can produce far better solutions than a neighborhood search algorithm alone. It is estimated that the success is primarily due to the powerful performance of integer programming.

• Journal of the Korea Society of Computer and Information
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• v.15 no.10
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• pp.1-9
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• 2010

In this paper, we propose a methodology in which a power-delay product of a binary adder is optimized based on the heterogeneous adder architecture. We formulate the power-delay product of the heterogeneous adder by using integer linear programming(ILP). For the use of ILP optimization, we adopt a transformation technique in which the initial non-linear expression for the power-delay product is converted into linear expression. The experimental result shows the superiority of the suggested method compared to the cases in which only conventional adder is used.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.11
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• pp.787-794
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• 2019

In this paper, a Mixed Integer Linear Programming(MILP) approach for solving optimal Weapon-Target Assignment(WTA) problem of multiple dissimilar Closed-In Weapon Systems (CIWS) is proposed. Generally, WTA problems are formulated in nonlinear mixed integer optimization form, which often requires impractical exhaustive search to optimize. However, transforming the problem into a structured MILP problem enables global optimization with an acceptable computational load. The problem of interest considers defense against several threats approaching the asset from various directions, with different time of arrival. Moreover, we consider multiple dissimilar CIWSs defending the asset. We derive a MILP form of the given nonlinear WTA problem. The formulated MILP problem is implemented with a commercial optimizer, and the optimization result is proposed.

• Journal of the Korea Society of Computer and Information
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• v.15 no.9
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• pp.47-55
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• 2010

Linear constraint satisfaction optimization problem is a kind of combinatorial optimization problem involving linearly expressed objective function and complex constraints. Integer programming is known as a very effective technique for such problem but require very much time and memory until finding a suboptimal solution. In this paper, we propose a method to improve the search performance by integrating local search and integer programming. Basically, simple hill-climbing search, which is the simplest form of local search, is used to solve the given problem and integer programming is applied to generate a neighbor solution. In addition, constraint programming is used to generate an initial solution. Through the experimental results using N-Queens maximization problems, we confirmed that the proposed method can produce far better solutions than any other search methods.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.8
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• pp.327-336
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• 2007

Lots of effort toward design optimizations have been paid for a cost-effective system design in various ways from a transistor level to RTL designs. In this paper, we propose a bit level optimization of an adder design for expanding its design space. For the bit-level optimization, a heterogeneous adder organization utilizing a mixture of carry propagation schemes is proposed to design a delay-area efficient adder which were not available in an ordinary design space. Then, we develop an optimization method based on Integer Linear Programming to search the expanded design space of the heterogeneous adder. The novelty of the Proposed architecture and optimization method is introducing a bit level reconstruction/recombination of IPs which have same functionality but different speed and area characteristics for producing more find-grained delay-area optimization.

• Journal of Korea Society of Industrial Information Systems
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• v.7 no.1
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• pp.58-65
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• 2002

When we estimate the optimal solution satisfy the objective function and subjective equation in the integer programming, The optimal solution of the objective function Z is decided by the positive integer at extreme point or revised extreme point in the convex set. The convex set is made up the linear subjective equation. The purpose of the paper is thus to establish a stepwise optimization in the integer programming model by estimating the variation △C/sub j/ of the constant term C/sub j/ in the linear objective function, after an application of the modified Branch & Bound method.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.5
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• pp.427-436
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• 2010

This paper deals with a task assignment problem of multiple UAVs performing multiple tasks on multiple targets. The task assignment problem of multiple UAVs is a kind of combinatorial optimization problems such as traveling salesman problem or vehicle routing problem, and it has NP-hard computational complexity. Therefore, computation time increases as the size of considered problem increases. To solve the problem efficiently, approximation methods or heuristic methods are widely used. In this study, the problem is formulated as a mixed integer linear program, and is solved by a mixed integer linear programming and a genetic algorithm, respectively. Numerical simulations for the environment of the multiple targets, multiple tasks, and obstacles were performed to analyze the optimality and efficiency of each method.