Earticle

건강을 증진시키고 수명을 연장하기 위한 의료 연구의 중요성이 증가하고 의료 관련 지출이 증가함에 따라 의료 지출의 경제성 평가 역시 중요하게 대두되고 있다. 경제학은 생명 및 건강 지출의 경제성을 평가하는 데 있어 유용한 분석 도구를 제시한다. 본 연구는 생명 및 생명 연장의 경제적 가치 측정을 위해 두 개의 서로 다른 이론 모형을 제시한다. 이를 통해 의료 분야에 있어서 연구 개발 투자와 관련된 비용-편익 분석(cost-benefit analysis)의 이론적 토대를 구축하고자 한다. 우선 Murphy and Topel(2006)의 방법론을 국내 실정에 맞게 수정 보완하여 생명의 가치 및 사망률 감소의 경제적 가치를 평가하기 위한 모형을 제시한다. 또한 Hall and Jones(2007)의 방법론을 우리 한국 경제의 실정에 맞게 수정 보완하여 제시함으로써 생명의 경제적 가치를 대안적 방법으로 추정함은 물론 향후 우리나라에서 건강 관련 지출 비중이 어떤 추이를 보일지 예측하기 위한 모형을 제시한다.

Medical research is on the rise as a global trend, along with spending on medical and healthcare. There is a growing need for evaluating the value of medical research and health spending. In this paper, we introduce two theoretical models to evaluate the economic value of life, health improvement, and life extension. For theoretical models, we modify and extend two existing models in the literature. By using the developed models, we can estimate (i) economic value of life for each age group, (ⅱ) economic value of health improvement and life extension, (ⅲ) economic value of medical improvements, and (iv) time path of the optimal health spending share.

본 연구는 류근관/전계형(2014)에 제시된 두 개의 이론 모형을 한국의 자료에 적용하여 한국에서 연령별 생명의 가치를 추정하고 이에 근거하여 지난 수십 년간 의료발전이 이룩한 생명연장의 경제적 가치를 추정한다. 이어 2010년~2050년에 걸쳐 소득에서 차지하는 건강 관련 지출 비중의 추이가 어떻게 변할지도 예측해 본다. 우리나라에 대해 연령별 생명의 경제적 가치를 구해보면 이는 1~5세 집단에서 가장 높게 나타나는데 보수적으로 계산해도 2010년 화폐가치로 약 8억 원으로 추정된다. 또한 1990년부터 2010년까지 우리나라가 기록한 사망률 감소의 경제적 가치는 약 940조 원으로 추정되고, 향후 사망률이 현재보다 1%만 감소하더라도 그 경제적 가치는 최저 약 48조 원에서 최대 약 125조 원 정도로 추정된다. 한편 2050년에는 국내 총 지출에서 차지하는 건강 관련 지출의 비중이 약 27% 정도로까지 상승할 것으로 예측된다.

Using two economic models in Ryu and Jeon(2014), we estimate economic value of life in Korea for each 5-year age group and forecast optimal share of health spending by year 2050. We find that the economic value of life is highest for those aged 1-5 years, that this value of life is approximately KRW 800 million at the minimum, that the mortality reduction achieved over the years 1990 to 2010 is worth KRW 940~2,325 trillion, that 1% future decline in the mortality rate is estimated to be worth KRW 48~125 trillion, and that for year 2050 the optimal share of health spending is forecasted to be 27% in Korea and 31% in the U.S.

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.

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.

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.

A mathematical model using dynamic programming approach is applied to an optimal unit commitment problem. In this study, the units are treated as stages instead of as state dimension, and the time dimension corresponds to the state dimension instead of stages. A considerable amount of computer time is saved as compared to the normal approach if there are many units in the basin. A case study on the Lower Colorado River Basin System is presented to demonstrate the capabilities of the optimal scheduling of hydropower units.

With the increasing deployment of distributed generators in the distribution system, a very large search space is required when dynamic programming (DP) is applied for the optimized dispatch schedules of voltage and reactive power controllers such as on-load tap changers, distributed generators, and shunt capacitors. This study proposes a new optimal voltage and reactive power scheduling method based on dynamic programming with a heuristic searching space reduction approach to reduce the computational burden. This algorithm is designed to determine optimum dispatch schedules based on power system day-ahead scheduling, with new control objectives that consider the reduction of active power losses and maintain the receiving power factor. In this work, to reduce the computational burden, an advanced voltage sensitivity index (AVSI) is adopted to reduce the number of load-flow calculations by estimating bus voltages. Moreover, the accumulated switching operation number up to the current stage is applied prior to the load-flow calculation module. The computational burden can be greatly reduced by using dynamic programming. Case studies were conducted using the IEEE 30-bus test systems and the simulation results indicate that the proposed method is more effective in terms of saving electric charges and improving the voltage profile than loss minimization.

In recognition of hydrologic uncertainty and seasonality, reservoir inflows are described as periodic Markov processes. The optimization of reservoir operations involves determination of the optimal release volumes in the continuous time periods so that the expected total rewards resulting from the operations are maximized. This paper develops stationary and non-stationary stochastic dynamic programming models for identifying efficient reservoir release polices employing the best forecast of the current period's and previous period's inflow volumes were developed. As a case study, SDP and NACSDP models were applied in the Baekgok reservoir, Jincheon of Chungbuk province, to maximize annual operating rewards. The results indicated that NACSDP model could achieve 29.8 and 56.1% higher annual operating rewards than SDP and conventional reservoir operation.

본 논문은 수출입 컨테이너 화물의 국제물류에 대한 최적의 복합운송 경로를 도출하기 위한 동적 프로그램 알고리즘을 제시한다. 현재 3자물류 시장의 급부상, 운송업계의 경쟁가열화, 운송경로의 다양화 및 글로벌화가 추구되면서 복합운송을 고려한 수송계획의 효율화가 필요한 실정이다. 그러므로 2가지 이상의 운송수단을 이용하는 복합운송의 특징을 살펴보고, 운송비용과 운송시간을 고려한 WCSPP(Weighted Constrained Shortest Path Problem) 모형을 제시한다. 본 모형을 통해 도출된 목적함수 결과 값를 이용하여 실행가능영역을 설정한 뒤 동적 프로그래밍(Dynamic Programming)중 하나인 Label Setting 알고리즘을 응용하여 두 가지 목적함수를 동시에 만족할 수 있는 파레토 최적해를 도출하였다. 또한 본 알고리즘의 성능을 향상시키기 위해 가지치기 규칙을 함께 제안한다. 본 알고리즘을 부산에서 출발하여 로테르담까지 도착하는 실제 운송경로에 적용하였으며, 기존의 단일운송수단 경로와 비교해 봄으로써 운송비용 및 운송시간의 절감효과를 정량적으로 측정하였다.

Because of rapid expansion of third party logistics, fierce competition in the transportation industry, and the diversification and globalization of transportation channels, an effective transportation planning by means of multimodal transport is badly needed. Accordingly, this study aims to suggest an optimal transport algorithm for the multimodal transport in the international logistics. As a solution for this problem, first of all, we have applied a pruning algorithm to simplify it, suggesting a heuristic algorithm for constrained shortest path problem to find out a feasible area with an effective time range, which has been applied to the Label Setting Algorithm, consequently leading to multiple Pareto optimal solutions. Also, in order to test the efficiency of the algorithm for constrained shortest path problem, this paper has applied it to the actual transportation path from Busan port of Korea to Rotterdam port of Netherlands.

본 논문은 수출입 컨테이너 화물의 국제물류에 대한 최적의 복합운송 경로를 도출하기 위한 동적 프로그램 알고리즘을 제시한다. 현재 3자물류 시장의 급부상, 운송업계의 경쟁가열화, 운송경로의 다양화 및 글로벌화가 추구되면서 복합운송을 고려한 수송계획의 효율화가 필요한 실정이다. 그러므로 2가지 이상의 운송수단을 이용하는 복합운송의 특징을 살펴보고, 운송비용과 운송시간을 고려한 WCSPP(Weighted Constrained Shortest Path Problem) 모형을 제시한다. 본 모형을 통해 도출된 목적함수 결과값을 이용하여 실행가능영역을 설정한 뒤, 동적 프로그래밍(Dynamic Programming)중 하나인 Label Setting 알고리즘을 응용하여, 두가지 목적함수를 동시에 만족할 수 있는 파레토 최적해를 도출하였다. 또한 본 알고리즘의 성능을 향상시키기 위해 가지치기 규칙을 함께 제안한다. 본 알고리즘을 부산에서 출발하여 로테르담까지 도착하는 실제 운송경로에 적용하였으며, 단일운송수단 및 기존의 복합운송 경로와 비교해 봄으로써 운송비용 및 운송시간의 절감효과를 정량적으로 측정하였다.

Because of rapid expansion of third party logistics, fierce competition in the transportation industry, and the diversification and globalization of transportation channels, an effective transportation planning by means of multimodal transport is badly needed. Accordingly, this study aims to suggest an optimal transport algorithm for the multimodal transport in the international logistics. Cargoes and stopovers can be changed numerously according to the change of transportation modes, thus being a NP-hard problem. As a solution for this problem, first of all, we have applied a pruning algorithm to simplify it, suggesting a heuristic algorithm for constrained shortest path problem to find out a feasible area with an effective time range and effective cost range, which has been applied to the Label Setting Algorithm, consequently leading to multiple Pareto optimal solutions. Also, in order to test the efficiency of the algorithm for constrained shortest path problem, this paper has applied it to the actual transportation path from Busan port of Korea to Rotterdam port of Netherlands.

Significant increase of container flows in marine terminals requires more efficient automatic port systems. This paper presents a novel routing and collision avoidance algorithm of linear motor based shuttle cars using random access sequence dynamic programming (RAS DP). The proposed RAS DP is accomplished online for determining optimal paths for each shuttle car.

항만 물동량의 증가로 자동화 항만 시스템에 대한 연구가 활발히 진행되고 있다. 본 연구는 선형 모터 기반 이송 장비인 LMTT 시스템의 자동화 운용에 있어 충돌 및 교착상태를 방지하기 위해 Random Access Sequence Dynamic Programming(RAS DP)를 이용한 최적 경로 탐색 알고리즘을 제안한다. 제안하는 알고리즘은 실시간으로 각 shuttle car의 최적 경로를 탐색한다.

Significant increase of container flows in marine terminals requires more efficient automatic port systems. This paper presents a novel routing and collision avoidance algorithm of linear motor based shuttle cars using random access sequence dynamic programming (RAS DP). The proposed RAS DP is accomplished online for determining optimal paths for each shuttle car.

Quality function deployment (QFD) is a useful method in product design and development to maximize customer satisfaction. In the QFD, the technical attributes (TAs) affecting the product performance are identified, and product performance is improved to optimize customer requirements (CRs). For product development, determining the optimal levels of TAs is crucial during QFD optimization. Many optimization methods have been proposed to obtain the optimal levels of TAs in QFD. In these studies, the levels of TAs are assumed to be continuous while they are often taken as discrete in real world application. Another assumption in QFD optimization is that the requirements of the heterogeneous customers can be generalized and hence only one house of quality (HoQ) is used to connect with CRs. However, customers often have various requirements and preferences on a product. Therefore, a product market can be partitioned into several market segments, each of which contains a number of customers with homogeneous preferences. To overcome these problems, this paper proposes an optimization approach to find the optimal set of TAs under multi-segment market. Dynamic Programming (DP) methodology is developed to maximize the overall customer satisfaction for the market considering the weights of importance of different segments. Finally, a case study is provided for illustrating the proposed optimization approach.

For optimal multireservoir system operation, a stochastic dynamic programming based on genetic algorithm(GASDP) was developed in this study. In the suggested model, multireservoir system operating rules were derived using a feed-forward genetic algorithm on formulation of potential release allocation for each water user within the system to decompose a multireservoir system into a set of single reservoir subsystem. The case study of three reservoir system in parallel at Umsong-gun of Chung-buk Province illustrated the methodology. The performance of the GASDP model was compared with the single SDP model and Conventional method used for deriving the multireservoir operating rules from optimization results. The GASDP model provided better performance in increasing the expected total discounted income and reducing water spills of the system than the other methods, but also it could avoid being trapped at the local optimum and overcome the dimensionality curse in searching processes.

The structured lighting technique is a very accurate depth measurement method. One problem in this technique is that the laser light is invisible very often partly or as a whole caused from occlusion, light absorbtion, and reflection on the surface. If there is no laser light detected on a row of the image frame, the depth could not be computed at the point on the row. In this paper, a Dynamic Programing-based efficient laser line tracking algorithm is proposed to find the most likely line of the laser light utilizing all the possible information of the laser light trace. The performance of the algorithm is shown.

An autonomous navigation technology for the mobile robot is investigated in this paper. The proposed robot path planning algorithm employs the dynamic programming to find the optimal path. The algorithm finds the global optimal path through the local computation on the environmental map. Since the robot computes the new path at every point, it can avoid the obstacle successfully during the navigation. The experimental results of the robot navigation are included in this paper.

Among many energy resources, natural gas has recently received a remarkable amount of attention, particularly from the electrical generation industry. This is in part due to increasing shale gas production, providing an environment-friendly fossil fuel, and high risk of nuclear power. Because South Korea, the world's second largest LNG importing nation after Japan, has no international natural gas pipelines and relies on imports in the form of LNG, the natural gas has been traditionally procured by long term LNG contracts at relatively high price. Thus, there is a need of developing an Asian LNG trading hub, where LNG can be traded at more competitive spot prices. In a natural gas spot market, the amount of natural gas to be bought should be carefully determined considering a limited storage capacity and future pricing dynamics. In this work, the problem to find the optimal amount of natural gas in a spot market is formulated as a Markov decision process (MDP) in risk neutral environment and the optimal base stock policy which depends on a stage and price is established. Taking into account price and demand uncertainties, the basestock target levels are simply approximated from dynamic programming. The simulation results show that the basestock policy can be one of effective ways for procurement of LNG in a spot market.