Earticle

The total yield of Pinus koraiensis stands was reviewed along forest function by using goal programming, which is one of the operations research techniques. The 4 kinds of management goals are set to identify timber production in the Research Forest of Kangwon National University. As a result, scenario 1 was estimated the best timber production over 2,073 ha area and also 588 ha in the third quarter was showed the most timber harvest. The rate of timber harvest was separated by 10 to 50 percent in non-timber forest function in the scenario 1 and that model was applied to the Research Forest of Kangwon National University. The structure of the area and volume is showed to be balanced quarterly when rate of timber harvest at 10 to 20 percent.

PURPOSES : Pavement Management System contains the data that describe the condition of the road. Under limited budget, the data can be utilized for efficient plans. The objective of this research is to develop a mixed integer program model that maximizes remaining durable years (or Lane-Kilometer-Years) in road maintenance planning. METHODS : An optimization model based on a mixed integer program is developed. The model selects a cluster of sectors that are adjacent to each other according to the road condition. The model also considers constraints required by the Seoul Metropolitan Facilities Management Corporation. They select two lanes at most not to block the traffic and limit the number of sectors for one-time construction to finish the work in given time. We incorporate variable cost constraints. As the model selects more sectors, the unit cost of the construction becomes smaller. The optimal choice of the number of sectors is implemented using piecewise linear constraints. RESULTS : Data (SPI) collected from Pavement Management System managed by Seoul Metropolitan City are fed into the model. Based on the data and the model, the optimal maintenance plans are established. Some of the optimal plans cannot be generated directly in existing heuristic approach or by human intuition. CONCLUSIONS:The mathematical model using actual data generates the optimal maintenance plans.

본연구(本硏究)에서는 OR이나 응용수학분야(應用數學分野)에서 개발(開發)된 기하적(幾何的) 계획법(計劃法)을 탄성이론(彈性理論)에 의(依)한 트러스구조물(構造物)의 최적화(最適化)에 도입(導入)하여 최적화(最適化) 알고리즘으로 적용(適用)하였다. 최적화(最適化) 문제형성(問題形成)에서 목적함수(目的函數)는 중량함수(重量函數), 제약조건식(制約條件式)으로는 축응력(軸應力), 좌굴응력(挫屈應力), 변위(變位), 설계변수(設計變數) 한계제약조건식(限界制約條件式)을 취하였다. 트러스구조물(構造物)의 최적설계(最適設計)에 이 알고리즘을 적용(適用)할 수 있는지의 적용가능성(適用可能性)과 그 효율성(効率性)을 검토(檢討)하기 위하여 절점수(節點數), 부재수(部材數), 극하조건수(戟荷條件數), 설계변수(設計變數)의 초기치(初期値)를 달리한 4종류(種類)의 트러스구조물(構造物)의 최적설계(最適設計)를 실시(實施)하고 그 결과(結果)를 분석(分析)하였다. 연구결과(硏究結果) 최적설계과정(最適設計過程)의 반복회수(反復回數)를 중심(中心)으로 분석(分析)할 때 본연구(本硏究)의 알고리즘은 1~7회(回) 반복과정(反復過程)만으로 Oscillation현상(現像)없이 안정성(安定性)있게 최적해(最適解)에 수감(收歛)하고 있어 본연구(本硏究)의 방법(方法)도 반복회수(反復回數)만을 비교(比較)할 경우 효율적이라는 결론(結論)을 얻었다.

This paper applies an optimization algorithm for the elastic truss structures. The acceleration technique utilized in this study is the geometric programming method developed by the Operation Research or the applied methematics. The applicability and the efficiency of the algorithm applied in this study are tested for four different trusses. Test results show that the optimum solutions are obtained after only one or seven iterations which is very small compared with other techniques and no oscillation is needed for the convergency. Test rusults also show that the Geometric Programming Method is also effective algorithm for the convergency of the Optimum Solution in case of only being compared with the number of iteration.

본 논문은 선형계획모형을 이용하여 H 아리수 정수 센터의 최적 취수량 결정 방법을 연구 하였다. 현재 H 아리수 센터에서는 관리자의 경험과 숙련도에 의지하여 취수량을 결정하고 있다. 그런데 매시 변하는 수요를 만족 시키면서 시간대별로 요금이 서로 다른 전력의 사용을 최소화 하는 취수량 결정은 근무자들의 경험과 숙련도를 넘어서는 간단한 문제가 아니다. 따라서 수리적 기법 중 하나인 선형계획모형을 이용해 취수량을 결정하고, 비용 절감을 시도하였다. 본 연구에서 제안한 선형계획 모형은 수요예측치를 기본 입력자료로 사용하고 있는데 예측오차가 발생할 경우 정수지 높이 제한을 위반하는 경우가 발생한다. 이를 해결하기 위해서는 정확한 수요예측이 선행되어야 한다. 그러나 아무리 좋은 예측 기법을 사용하더라도 실수요와 오차는 있게 마련이고 이는 여전히 높이 제한의 제약을 만족 시키지 못하는 결과를 불러일으킨다. 따라서 예측오차를 수용 할 수 있는 안전 마진 상수를 이용한 대안을 제안하였다. 본 연구에서 제안한 선형 계획 모형을 통한 취수량 결정은 수위 모니터링을 위해 항시 작업자가 근무 할 필요가 없기 때문에 인건비 면에서도 많은 절약이 예측되어 총 비용 감축은 훨씬 더 많으리라 기대된다.

Currently, the H purification plant determines the hourly water intake amount based on operator experience and skill. Therefore, inevitably, there are deviations among operators. While meeting time-varying demand and maintaining the proper water level in the clean water reservoir, the methodology for minimizing electricity cost, when dealing with different electricity rate time zones, is a very complicated problem, which is beyond an operator's capability. To solve this problem, a linear programming (LP) model is proposed, which can determine the optimal hourly water intake amount for minimizing the daily electricity cost. It is shown that an inaccurate estimate for the hourly water usage in the demand areas causes the water level constraint to be violated, which is the weak point of the proposed LP method. However, several examples with real-field data show that we can practically and safely solve this problem with safety margins. It is also shown that the safety margin method still works effectively whether the estimate is accurate or not. The operators need not attend the site at all times under the proposed LP method, and we can additionally expect reductions in labor costs.

본 논문에서는 유전적 프로그래밍(Genetic Programing)을 이용하여 최적의 다항식을 생성하는 기법을 제시하고자 한다. 다항식은 비선형성이 큰 응답면을 모델링해야 하며, 이를 위하여 GP 트리 생성시 2-3차 오더의 Taylor Series를 사용하는 방법을 시도하였다. 아울러 생서되는 다항식의 크기를 제어하기 위해서 GP 트리가 표현할 수 있는 다항식의 최대 차수를 제한함과 동시에 하나의 주 트리와 보 트리로 구성되는 GAGPT(Group of Additive Genetic Programming Trees) 사용을 모색하였다. 마지막으로 두 개의 응용 예를 통하여 본 방법의 유용성을 검증하였다.

This paper deals with the problem of generating optimal polynomials using Genetic Programming(GP). The polynomial should approximate nonlinear response surfaces. Also, there should be a consideration regarding the size of the polynomial, It is not desirable if the polynomial is too large. To build small or medium size of polynomials that enable to model nonlinear response surfaces, we use the low order Tailor series in the function set of GP, and put the constrain on generating GP tree during the evolving process in order to prevent GP trees from becoming too large size of polynomials. Also, GAGPT(Group of Additive Genetic Programming Trees) is adopted to help achieving such purpose. Two examples are given to demonstrate our method.

The wireless mesh network (WMN) is a next-generation technology for data networking that has the advantage in cost and the flexibility in its construction because of not requiring the infra-structure such as the ethernet. This paper focuses on the optimal link scheduling problem under the wireless mesh network to effectuate real-time streaming by using the constraint programming. In particular, Under the limitation of half-duplex transmission in wireless nodes, this paper proposes a solution method to minimize the makespan in scheduling packet transmission from wireless nodes to the gateway in a WMN with no packet transmission conflicts due to the half-duplex transmission. It discusses the conflicts in packet transmission and deduces the condition of feasible schedules, which defines the model for the constraint programming. Finally it comparatively shows and discusses the results using two constraint programming solvers, Gecode and the IBM ILOG CP solver.

서울시 공공자전거 시스템은 2015년 정식도입 이래 45,000대 규모로 계속 성장해왔다. 그러나 자산의 노후화에 따른 유지보수와 교체 비용이 지속적으로 증가해왔다. 2023년 약 4,500대를 폐기하였고, 약 30억원 규모의 자산 손실이 발생하였다. 그러므로 과학적이고 데이터 기반의 자산관리 정책 수립이 시급하다. 본 연구의 목적은 확률적 동적계획법(Stochastic Dynamic Programming, SDP)를 활용하여 장기비용을 절감하고, 목표서비스 수준을 만족하는 최적 운영 정책을 수립하는 것이다. 이를 위해서 5단계의 물리적 상태와 누적시간을 기초로 총 15개의 상태를 정의하고, 실제 정비이력을 분석하여, 상태 전이확률을 추정하였다. 그리고 운영비, 유지보수비, 교체비, 방치에 따른 기회비용을 포함한 비용구조를 추정하였다. 수립된 SDP모델은 가치반복법(Value Iteration)을 통해서 상태 기반 최적 정책을 도출하였고, 연도별 자산 도입이력을 반영한 초기 상태 분포를 바탕으로, 전체 자산군에 대해 에이전트 기반 시뮬레 이션으로 정책 성과를 검증하였다. 그 결과, SDP 최적 정책이 상태기반 교체 정책 대비 비용 절감 효과를 보였다. 또한 월별 비용의 변동계수도 비교한 여러 정책 중 가장 낮아 예측 가능한 예산운영이 가능함을 보였다. 그리고, 고장 자전거에 대해 교체보다 수리를 하는 것이 비용 효율적임을 입증하였다.

Since its official launch in 2015, Seoul's public bicycle system has grown continuously to a fleet of 45,000 bicycles. However, maintenance and replacement costs have steadily increased due to asset aging. In 2023, approximately 4,500 bicycles were disposed of, resulting in asset losses of about 3 billion KRW. Therefore, there is an urgent need to establish scientific and data-driven asset management policies. The purpose of this study is to develop an optimal operational policy that reduces long-term costs and satisfies target service levels using Stochastic Dynamic Programming (SDP). To this end, we defined 15 states based on five physical condition levels and cumulative operating time, and estimated state transition probabilities by analyzing actual maintenance records. We also estimated a cost structure that includes operating costs, maintenance costs, replacement costs, and opportunity costs associated with neglect. The SDP model derived state-based optimal policies through Value Iteration, and policy performance was validated using agent-based simulation for the entire asset fleet, based on initial state distributions reflecting annual asset acquisition history. The results showed that the SDP optimal policy achieved cost savings compared to condition-based replacement policies. Furthermore, the coefficient of variation for monthly costs was the lowest among all compared policies, demonstrating that predictable budget management is achievable. Additionally, it was proven that repairing malfunctioning bicycles is more cost-effective than replacing them.

Web services have been widely applied in e-business or organizations to establish versatile applications. However, service composition with constraint to QoS constraints still remains as a challenging issue, because both the service performance and the QoS requirements of applications tend to be dynamically changed at runtime. To address this problem, in this paper we present a novel service composition framework, which takes advantage of flexibility provided by cloud systems and applies evolution strategy to solve the optimal programming problem of multiple QoS constrained service composition. Furthermore, a QoS negation mechanism is proposed to satisfy the dynamical and elastic cloud environments. Massive experiments are conducted to investigate the effectiveness of the proposed framework, and the results show that it can significantly reduce the costs of large-scale service-based application in terms of various QoS metrics.

Regression testing remains one of the most laborious and costly software testing activities in software maintenance. Its laboriousness and costing increases with increasing number of test suits: need to changed re-testing strategy. The strategy should identify and eliminate redundant test cases in order to minimize the test cases from test suites. The aim of regression testing is to reduce the retesting effort and achieve the adequate testing coverage. An important research problem, in this context, is the new developed algorithm is needed to reduce the retesting effort, cost and time without disturbance of thoroughness. The traditional optimal page replacement algorithm is used for managing the virtual memory. Our approach has been showing that the novel optimal page replacement algorithm reducing the redundant test cases during retesting of modified object oriented program. Our testing approach is on unit level. The logic based transitive relationship model achieves the adequate testing coverage to the modified object oriented program. Hybridization of clock page replacement-counting based algorithm is used for making the logic based transition relationship model. An Illustrative example has been presented to establish the effectiveness of our methodology coupled with logic based transitive relationship model.

Installing distributed generators can effectively reduce network power consumption and improve power quality in the distribution network, but it will also bring some technical problems. Therefore, we need to study how to efficiently and quickly determine the optimal investment capacity of distributed generators in the distribution network. In this paper, we set the minimization of the power loss as the objective and establish the most investment scale decision-making model of distributed generation access to power distribution system. In terms of optimization algorithm, optimization time of traditional sequential quadratic programming will dramatically increase with the increasing number of variables and restraints. In order to pursue higher efficiency optimization, we apply the improved sequential quadratic programming that is fast sequential quadratic programming method for solving nonlinear problems of distributed generators optimal investment size. We apply the constructed model to systems with different nodes, and the results show that the proposed new optimization method is not only scientific and effective, and can significantly improve decision-making efficiency.

Renewable sources integration is gaining importance in electrical utilities all over the world. The liberization of power sector in competitive regime, the share of renewable energy sources is increasing and it is essential to carry out the impact of clean energy on the system performance In this paper, analysis has been carried out with the PV-based distribution generation in the power system network. A Mixed Integer Nonlinear Programming (MINLP) approach has been utilized for determining optimal location and number of distributed generators considering minimization of fuel cost of conventional and solar PV power. The pattern of nodal real and reactive power prices have been obtained with and without PV integration. The results are also obtained for, loss reduction, fuel cost saving and voltage profile. The impact of different load models as PQ load and Zip load model has been studied. The proposed MINLP based optimization approach has been applied for IEEE24 bus reliability test system.

To select the best Third-Party Logistics(TPL) transportation suppliers(companies) it is essential to make a trade off between quantitative and qualitative factors. When many constraints exist, choosing the companies becomes more complicated. In this paper an integration of an analytical hierarchy process and multicriteria goal programming is proposed to consider both quantitative and qualitative factors in choosing the best transportation companies within constrained environment. First, through the previous studies, we derive important factors and priorities by applying Analytic Hierarchy Process(AHP). Then, along with the results obtained by AHP, to choose the best transportation companies we establish multicriteria Goal Programming(GP) model considering fixed, total and transportation cost,rate of damaged goods and rate of delayed goods as major criteria. We also derive optimal quantities of goods transported from each selected transportation company to destinations based upon the proposed method.

This study presents an interval linear fractional programming (ILFP) approach for optimizing power flow allocation in a two-line transmission network under data uncertainty. The power system is modeled by an interval linear fractional objective that maximizes power transfer efficiency subject to interval-valued constraints on total demand, line capacities, and non-negativity of line flows. Interval coefficients are represented in midpoint-width form, and a Charnes-Cooper variable transformation is combined with an extended Lagrangian formulation to derive interval Karush-Kuhn-Tucker type optimality conditions. A detailed numerical example on a two-line network, including graphical illustration of the feasible region and interval objective surface, demonstrates the proposed solution procedure. In addition, a sensitivity analysis with respect to demand, capacity, and objective-interval parameters is reported, showing how variations in interval bounds affect the optimal efficiency range and highlighting the suitability of the ILFP framework for decision-making in power systems with uncertain data.

The increasing of wind power penetration level presents challenges in classical optimal reactive power dispatch (ORPD) which is usually formulated as a deterministic optimization problem. This paper proposes a two-stage stochastic programming model for ORPD by considering the uncertainties of wind speed and load in a specified time interval. To avoid the excessive operation, the schedule of compensators will be determined in the first-stage while accounting for the costs of adjusting the compensators (CACs). Under uncertainty effects, on-load tap changer (OLTC) and generator in the second-stage will compensate the mismatch caused by the first-stage decision. The objective of the proposed model is to minimize the sum of CACs and the expected energy loss. The stochastic behavior is formulated by three-point estimate method (TPEM) to convert the stochastic programming into equivalent deterministic problem. A hybrid Genetic Algorithm-Interior Point Method is utilized to solve this large-scale mixed-integer nonlinear stochastic problem. Two case studies on IEEE 14-bus and IEEE 118-bus system are provided to illustrate the effectiveness of the proposed method.

In this paper, we introduce some approximate optimal solutions and an augmented Lagrangian function in nonlinear programming, establish dual function and dual problem based on the augmented Lagrangian function, discuss the relationship between the approximate optimal solutions of augmented Lagrangian problem and that of primal problem, obtain approximate KKT necessary optimality condition of the augmented Lagrangian problem, prove that the approximate stationary points of augmented Lagrangian problem converge to that of the original problem. Our results improve and generalize some known results.

We consider an insured who wishes to determine his optimal reporting strategy over a given planning horizon, when he has option of reporting of not reporting his at-fault accidents. Assuming that the premium in future period is continually adjusted by the insured's loss experience, the insured would not report every loss incurred. Rather, considering the benefits and costs of each decision, the insured may want to seek a way of optimizing his interests over the planning horizon. The situation is modeled as a dynamic programming problem. We consider an insured's discounted expected cost minimization problem, where the premium increase in future period is affected by the size of the current claim. More specifically, we examine two cases ; (1) the premium increase in the next is a linear function (a constant fraction) of the current claim size; (2) the premium increase in the next period is a concave function of the current claim size. In each case, we derive the insured's optimal reporting strategy.

The purpose of this study is to develop an efficient model for the least cost design of multi-site detention systems. The IDP (Incremental Dynamic Programming) model for optimal design is composed of two sub-models : hydrologic-hydraulic model and optimization model. The objective function of IDP is the sum of costs ; acquisition cost of the land, construction cost of detention basin and pumping system. Model inputs include channel characteristics, hydrologic parameters, design storm, and cost function. The model is applied to the Jung-Rang Cheon basin in Seoul, a watershed with cetention basins in multiple branching channels. The application results show that the detention system can be designed reasonably for various conditions and the model can be applied to multi-site detention system design.

This paper presents a optimal Var allocation algorithm for minimizing power loss and improving voltage profile in a given system. In this paper, nodal input data is considered as Gaussian distribution with their mean value and their variance. A stochastic Linear Programming technique based on chance constrained method is applied to solve the probabilistic constraint. The test result in IEEE-14 Bus model system showes that the voltage distribution of load buses is improved and the power loss is more reduced than before Var allocation.

This paper develops an optimization model for portfolio allocations in 22 emerging equity markets by incorporating the conflicting objectives of maximizing return and minimizing risk in a single objective function. The resulting model allows users to input their risk aversion value to obtain the optimal portfolio mix. The model is extended to include minimum and maximum allocations per geographical region as well as minimum and maximum number ofmarkets per region, given that the investor desires to minimize portfolio risk with a minimum portfolio return to be achieved.

Global state space's optimal policy is used for offline controller in the form of table by using Dynamic Programming. If an optimal policy table has a large amount of control data, it is difficult to use the system in a low capacity system. To resolve these problem, controller using the compressed optimal policy table is proposed in this paper. A DCT is used for compression method and the cosine function is used as a basis. The size of cosine function decreased as the frequency increased. In other words, an essential information which is used for restoration is concentrated in the low frequency band and a value of small size that belong to a high frequency band could be discarded by quantization because high frequency's information doesn't have a big effect on restoration. Therefore, memory could be largely reduced by removing the information. The compressed output is stored in memory of embedded system in offline and optimal control input which correspond to state of plant is computed by interpolation with Inverse DCT in online. To verify the performance of the proposed controller, computer simulation was accomplished with a one link inverted pendulum.