Airpower is a crucial force for suppressing military threats and achieving victory in wars. This study evaluates newly introduced fighter forces, considering factors such as fighter performance and power index, operational environment, capacity of each airbase, survivability, and force sustainment capability to determine the optimal deployment plan that maximizes operational effectiveness and efficiency. Research methods include optimization techniques such as MIP(mixed integer programming), allocation problems, and experimental design. This optimal allocation mathematical model is constructed based on various constraints such as survivability, mission criticality, and aircraft's performance data. The scope of the study focuses the fighter force and their operational radius is limited to major Air Force and joint operations, such as air interdiction, defensive counter-air operations, close air support, maritime operations and so on. This study aims to maximize the operational efficiency and effectiveness of fighter aircraft operations. The results of proposed model through experiments showed that it was for superior to the existing deployment plan in terms of operation and sustainment aspects when considering both wartime and peacetime.

본 논문에서는 다목적 구조물인 다중연결 해양부유체를 대상으로 변형 기반 모드 차수축소법을 적용하고 차수축소모델의 구조응 답 예측 성능을 향상시키기 위해 유전 알고리즘 기반의 센서 배치 최적화를 수행하였다. 다중연결 해양부유체의 차수축소모델 생성 에 필요한 변형 기반 모드 데이터를 얻기 위해 다양한 규칙파랑하중조건에 대한 유체-구조 연성 수치해석을 수행하고 변형 기반 모드 의 직교성, 자기상관계수를 이용하여 주요 변형 기반 모드를 선정하였다. 다중연결 해양부유체의 경우 차수축소모델의 구조응답 예 측 성능이 계측 및 예측 구조응답 위치에 따라 민감하기 때문에 유전 알고리즘 기반의 최적화를 수행하여 최적의 센서 배치를 도출하 였다. 최적화 결과, 모든 센서 배치 조합에 대한 차수축소모델 생성 및 예측 성능 평가 대비 약 8배의 계산 비용을 절감하였으며, 예측 성능 평가 지표인 평균 제곱근 오차가 초기 센서 배치보다 84% 감소하였다. 또한, 다중연결 해양부유체 모형시험 결과를 이용하여 불 규칙파랑하중에 대한 최적화된 센서 배치의 차수축소모델의 구조응답 예측 성능을 평가 및 검증하였다.

This paper presents a path planning optimization model for the engineering units to install obstacles in the shortest time during wartime. In a rapidly changing battlefield environment, engineering units operate various engineering obstacles to fix, bypass, and delay enemy maneuvers, and the success of the operation lies in efficiently planning the obstacle installation path in the shortest time. Existing studies have not reflected the existence of obstacle material storage that should be visited precedence before installing obstacles, and there is a problem that does not fit the reality of the operation in which the installation is continuously carried out on a regional basis. By presenting a Mixed Integrer Programming optimization model reflecting various constraints suitable for the battlefield environment, this study attempted to promote the efficient mission performance of the engineering unit during wartime.

In contemporary global warfare, the significance and imperative of air transportation have been steadily growing. The Republic of Korea Air Force currently operates only light and medium-sized military cargo planes, but does not have a heavy one. The current air transportation capability is limited to meet various present and future air transport needs due to lack of performance such as payload, range, cruise speed and altitude. The problem of population cliffs and lack of airplane parking space must also be addressed. These problems can be solved through the introduction of heavy cargo planes. Until now, most studies on the need of heavy cargo plane and increasing air transport capability have focused on the necessity. Some of them suggested specific quantity and model but have not provided scientific evidence. In this study, the appropriate ratio of heavy cargo plane suitable for the Korea's national power was calculated using principal component analysis and cluster analysis. In addition, an optimization model was established to maximize air transport capability considering realistic constraints. Finally we analyze the results of optimization model and compare two alternatives for force structure.

In contemporary global warfare, the significance and imperative of air transportation have been steadily growing. Nevertheless, the Korean Air Force currently operates only with small and medium-sized military cargo planes, lacking larger aircraft. Consequently, the efficiency of their operations is constrained by the limited air transport capacity and the aging of their existing fleet, among other factors. Therefore, we have to consider to make future air transportation capability. Although the 2nd large-sized cargo-plane acquisition project is ongoing, its quantity is very small. In this study, we propose an optimal prediction model that takes into account practical constraints such as parking space availability, pilot availability, wartime daily maximum loads, while simultaneously maximizing both the effectiveness and efficiency of transport capacity for future warfare envirionment.

Amphibious operations represent a pivotal military maneuver involving the transfer of landing forces via ships, boats, and aircraft from sea to land. The success of such operations can be the decisive factor in the outcome of a war. Nevertheless, planning an amphibious assault is an intricate and formidable task, demanding careful consideration of numerous variables. This complexity is particularly evident in the formulation of loading plans for troops and equipment onto naval vessels. Historical accounts underscore the profound repercussions of errors in planning and loading on the execution of these operations. In pursuit of efficient loading procedures characterized by precision and time-effectiveness, our study has delved into the realm of optimization modeling. Employing a mixed-integer mathematical programming approach, this optimization model offers a valuable tool to streamline and enhance the preparatory phase of amphibious operations.

PURPOSES : The purpose of this study is to build an optimization model using the capacity and initial travel speed of the volume delay functions for network calibration performed in the traffic demand analysis process. METHODS : The optimization model contains an error term between the observed traffic volume and estimated traffic volume, based on the user equilibrium principle, and was constructed as a bi-level model by applying range constraints on capacity and travel time. In addition, we searched the split section to apply the method of adjusting the section instead of adjusting the single link. The optimization model is constructed by applying the warm-start method using the bush of the origin-based model so that parameter adjustment and traffic assignment are repeatedly executed within the model and the convergence of the model configured %RSSE. RESULTS : As a result of analysis using the toy network, the optimization model is that the observed traffic volume is estimated when there are no restrictions and, when the constraint conditions were set, the error with the observed traffic volume and error rate was significantly reduced. As a result of the comparative analysis of the trial-and-error methods, KTDB optimum values, and optimization models in empirical analysis using a large-scale network, the evaluation indexes (e.g., RMSE and %RMSE) were significantly improved by applying the optimization model. CONCLUSIONS : Based on the empirical analysis, the optimization model of this study can be applied to large-scale networks and it is expected that the efficiency and reliability of road network calibration will be improved by repeatedly performing parameter adjustment and traffic assignment within the model.

In the satellite operation phase, a ground station should continuously monitor the status of the satellite and sends out a tasking order, and a satellite should transmit data acquired in the space to the Earth. Therefore, the communication between the satellites and the ground stations is essential. However, a satellite and a ground station located in a specific region on Earth can be connected for a limited time because the satellite is continuously orbiting the Earth, and the communication between satellites and ground stations is only possible on a one-to-one basis. That is, one satellite can not communicate with plural ground stations, and one ground station can communicate with plural satellites concurrently. For such reasons, the efficiency of the communication schedule directly affects the utilization of the satellites. Thus, in this research, considering aforementioned unique situations of spacial communication, the mixed integer programming (MIP) model for the optimal communication planning between multiple satellites and multiple ground stations (MS-MG) is proposed. Furthermore, some numerical experiments are performed to verify and validate the mathematical model. The practical example for them is constructed based on the information of existing satellites and ground stations. The communicable time slots between them were obtained by STK (System Tool Kit), which is a well known professional software for space flight simulation. In the MIP model for the MS-MG problems, the objective function is also considered the minimization of communication cost, and ILOG CPLEX software searches the optimal schedule. Furthermore, it is confirmed that this study can be applied to the location selection of the ground stations.

With the increased interest in the quality of life of modern people, the implementation of the five-day working week, the increase in traffic convenience, and the economic and social development, domestic and international travel is becoming commonplace. Furthermore, in the past, there were many cases of purchasing packaged goods of specialized travel agencies. However, as the development of the Internet improved the accessibility of information about the travel area, the tourist is changing the trend to plan the trip such as the choice of the destination. Web services have been introduced to recommend travel destinations and travel routes according to these needs of the customers. Therefore, after reviewing some of the most popular web services today, such as Stubby planner (http://www.stubbyplanner.com) and Earthtory (http://www.earthtory.com), they were supposed to be based on traditional Traveling Salesman Problems (TSPs), and the travel routes recommended by them included some practical limitations. That is, they were not considered important issues in the actual journey, such as the use of various transportation, travel expenses, the number of days, and lodging. Moreover, although to recommend travel destinations, there have been various studies such as using IoT (Internet of Things) technology and the analysis of cyberspatial Big Data on the web and SNS (Social Networking Service), there is little research to support travel routes considering the practical constraints. Therefore, this study proposes a new mathematical model for applying to travel route recommendation service, and it is verified by numerical experiments on travel to Jeju Island and trip to Europe including Germany, France and Czech Republic. It also expects to be able to provide more useful information to tourists in their travel plans through linkage with the services for recommending tourist attractions built in the Internet environment.

국지적 스케일에서 작동하는 주택시장의 공간성을 반영하는 개념 중 하나가 주택시장지역(housing market area, HMA)이다. 주택시장의 역동성을 분석하고 주택 수요와 공급에 대한 체계적인 계획 수립을 위한 틀을 제공하기 위해서는 먼저 HMA를 구획할 필요가 있다. 본 연구의 목적은 HMA 구획을 위한 혼합 정수 계획법(mixed integer programming, MIP) 형태의 공간 최적화 모형을 개발하는 것이다. HMA 구획을 위한 가장 중요한 기준은 HMA의 내적 응집력을 의미하는 자족성이다. 구획된 HMA 전체를 대상으로 평가되는 전역적 자족성을 목적함수 형태로, 개별 HMA 별로 평가되는 국지적 자족성을 제약조건 형태로 고려하였다. 서울시 구별 인구이동을 사례로 개발된 최적화 모형을 적용한 결과 2010년의 경우 공급-측면 및 수요-측면 자족도 수준이 0.70일 때 3개, 0.65일 때 4개의 HMA가 구획되었다. 휴리스틱 기법인 Intramax 결과와 비교한 결과 첫째, Intramax와는 달리 MIP 접근을 통하여 얻은 HMA는 계층적으로 조직되지 않았다. 둘째, Intramax 결과는 국지적인 해인데 반하여, MIP 결과는 전역적 최적해였다. 셋째, 전역적 자족도의 극대화가 반드시 모든 HMA에서 국지적 자족도의 향상을 가져오지는 않았다. 이러한 결과는 HMA의 구조를 보다 명확하게 이해하기 위해서는 MIP 접근이 필요하며, HMA 구획 방법론으로서 Intramax 접근이 분명한 한계를 갖는다는 것을 의미한다.

Conventional data envelopment analysis (DEA) models require that inputs and outputs are given as crisp values. Very often, however, some of inputs and outputs are given as imprecise data where they are only known to lie within bounded intervals. While a typical approach to addressing this situation for optimization models such as DEA is to conduct sensitivity analysis, it provides only a limited ex-post measure against the data imprecision. Robust optimization provides a more effective ex-ante measure where the data imprecision is directly incorporated into the model. This study aims to apply robust optimization approach to DEA models with imprecise data. Based upon a recently developed robust optimization framework which allows a flexible adjustment of the level of conservatism, we propose two robust optimization DEA model formulations with imprecise data; multiplier and envelopment models. We demonstrate that the two models consider different risks regarding imprecise efficiency scores, and that the existing DEA models with imprecise data are special cases of the proposed models. We show that the robust optimization for the multiplier DEA model considers the risk that estimated efficiency scores exceed true values, while the one for the envelopment DEA model deals with the risk that estimated efficiency scores fall short of true values. We also show that efficiency scores stratified in terms of probabilistic bounds of constraint violations can be obtained from the proposed models. We finally illustrate the proposed approach using a sample data set and show how the results can be used for ranking DMUs.

PURPOSES : The purpose of this study is to present a linear programing optimization model for the design of lane-based lane-uses and signal timings for an isolated intersection. METHODS: For the optimization model, a set of constraints for lane-uses and signal settings are identified to ensure feasibility and safety of traffic flow. Three types of objective functions are introduced for optimizing lane-uses and signal operation, including 1) flow ratio minimization of a dual-ring signal control system, 2) cycle length minimization, and 3) capacity maximization. RESULTS : The three types of model were evaluated in terms of minimizing delay time. From the experimental results, the flow ratio minimization model proved to be more effective in reducing delay time than cycle length minimization and capacity maximization models and provided reasonable cycle lengths located between those of other two models. CONCLUSIONS : It was concluded that the flow ratio minimization objective function is the proper one to implement for lane-uses and signal settings optimization to reduce delay time for signalized intersections.