This paper deals with a vehicle routing problem with resource repositioning (VRPRR) which is a variation of well-known vehicle routing problem with pickup and delivery (VRPPD). VRPRR in which static repositioning of public bikes is a representative case, can be defined as a multi-objective optimization problem aiming at minimizing both transportation cost and the amount of unmet demand. To obtain Pareto sets for the problem, famous multi-objective optimization algorithms such as Strength Pareto Evolutionary Algorithm 2 (SPEA2) can be applied. In addition, a linear combination of two objective functions with weights can be exploited to generate Pareto sets. By varying weight values in the combined single objective function, a set of solutions is created. Experiments accomplished with a standard benchmark problem sets show that Variable Neighborhood Search (VNS) applied to solve a number of single objective function outperforms SPEA2. All generated solutions from SPEA2 are completely dominated by a set of VNS solutions. It seems that local optimization technique inherent in VNS makes it possible to generate near optimal solutions for the single objective function. Also, it shows that trade-off between the number of solutions in Pareto set and the computation time should be considered to obtain good solutions effectively in case of linearly combined single objective function.

Naval weapons systems of the Republic of Korea are acquired through the Defense Planning Management System. Recently, acquisition of some naval ships have been delayed, and the causes of the delays have been recognized as inappropriate project management at the Execution Phase. However, we argue that the delay problem in naval ships acquisition should be approached, with due regard for the entire Defense Planning Management System. That is, We should try to investigate from Planning Phase to those of Programming, Budgeting and Execution Phases. Therefore, in this study, we investigated the actual cases of the delay in naval acquisition at all phases of the Defense Planning Management System. Based on the investigation, we tried to identify the naval ship Acquisition Delay Factors and find out the Weights of those factors. As the next step, we calculated the Influence Measures on the naval missions, including the Cost of Naval Capability Gap derived from the delays in acquisition of naval ships. As a final step, we calculated the Acquisition Delay Measures based on the interrelationship between the Acquisition Delay Factors and the Influence Measures. Then we evaluated and analyzed what the results stand for. Finally, we made suggestions for future improvement. The improvement suggestions we made for preventing delay in acquisition of naval ships in this study are as follows. First, we need a shift in perception. It is necessary to measure the Acquisition Delay Factors in acquiring naval ships and manage them from the Planning Phase. Second, resolution must be concerted efforts. All relevant agencies, not just a few, should work together to resolve the problems of acquisition delay. Third, analysis must be based on the accumulation of data. This allows the elaborating of naval ship Acquisition Delay Factors and Delay Measures. If this research method is applied to other military weapons systems in the future, we may be able to not just identify the Acquisition Delay Factors in acquisition of other military weapons systems, but also pursue improvement in those cases.

To make a satisfactory decision regarding project scheduling, a trade-off between the resource-related cost and project duration must be considered. A beneficial method for decision makers is to provide a number of alternative schedules of diverse project duration with minimum resource cost. In view of optimization, the alternative schedules are Pareto sets under multi-objective of project duration and resource cost. Assuming that resource cost is closely related to resource leveling, a heuristic algorithm for resource capacity reduction (HRCR) is developed in this study in order to generate the Pareto sets efficiently. The heuristic is based on the fact that resource leveling can be improved by systematically reducing the resource capacity. Once the reduced resource capacity is given, a schedule with minimum project duration can be obtained by solving a resource-constrained project scheduling problem. In HRCR, VNS (Variable Neighborhood Search) is implemented to solve the resource-constrained project scheduling problem. Extensive experiments to evaluate the HRCR performance are accomplished with standard benchmarking data sets, PSPLIB. Considering 5 resource leveling objective functions, it is shown that HRCR outperforms well-known multi-objective optimization algorithm, SPEA2 (Strength Pareto Evolutionary Algorithm-2), in generating dominant Pareto sets. The number of approximate Pareto optimal also can be extended by modifying weight parameter to reduce resource capacity in HRCR.

The Cooperative Engagement Capability (CEC) System produces a synergy between the sensors and shooters that are used on various platforms by integrating them. Even the US Navy has been recently adopting the CEC system that maximizes the effectiveness of the air defense operations by efficiently coordinating the dispersed air defense assets. The Navy of other countries are conducting research studies on the theory and application methods for the CEC system. The ROK Navy has limited air defense capabilities due to its independent weapons systems on battle ships. Therefore, the ROK Navy is currently going through a phase where research on proving the validity of building the CEC system because it will provide a way to overcome the limit of the platform based air defense capability. In this study, our goal is to propose methods that maximize the air defense capability of ROK Navy, identify the available assets for constructing the CEC system, and estimate effects of the CEC system when it is applied to the naval operations. In addition, we will provide a simple model that was developed to estimate these effects and a case study with virtual data to demonstrate the effects of the system when it is applied to the naval operations. The research result of this study will provide a way for building the basis of the Korean CEC system.

The private sector is currently reviewing the feasibility of the project or deciding economic policies by analyzing the economic ripple effects. However, the arms acquisition project focuses on the need for the national defense weapons system by analyzing the costs and the effectiveness of the analysis and reviewing the necessity and feasibility of the project. In order to analyze the economic ripple effects, KB (the Bank of Korea) prepares and publishes an analysis table of industrial associations in a given unit. IAAR (the industrial association analysis report) is difficult to apply directly to the defense weapons system. Therefore, research on the economic ripple effects applicable to the defense arms procurement project was needed. In this study, we propose the generic methodology for estimating economical and technical ripple effects resulted in acquiring new weapon systems. Based on the analysis of inter-industrial relations, economical ripple effects are estimated with production inducing effects, value-induced effects, employment-induced effects and export-induced effects. Also, the technological ripple effects are estimated with technological intensity represented by investment cost in research and development. To show the validity of proposed methodology, a case study of acquiring new weapon systems such as GR (guided rocket), destroyer, and helicopter is accomplished. From the case study, it is concluded that these economical & technological ripple effects can be used as a reference to decision making in the course of acquiring major future defense weapons systems.

This paper deals with solution methods for discrete and multi-valued optimization problems. The objective function of the problem incorporates noise effects generated in case that fitness evaluation is accomplished by computer based experiments such as Monte Carlo simulation or discrete event simulation. Meta heuristics including Genetic Algorithm (GA) and Discrete Particle Swarm Optimization (DPSO) can be used to solve these simulation based multi-valued optimization problems. In applying these population based meta heuristics to simulation based optimization problem, samples size to estimate the expected fitness value of a solution and population (particle) size in a generation (step) should be carefully determined to obtain reliable solutions. Under realistic environment with restriction on available computation time, there exists trade-off between these values. In this paper, the effects of sample and population sizes are analyzed under well-known multi-modal and multi-dimensional test functions with randomly generated noise effects. From the experimental results, it is shown that the performance of DPSO is superior to that of GA. While appropriate determination of population sizes is more important than sample size in GA, appropriate determination of sample size is more important than particle size in DPSO. Especially in DPSO, the solution quality under increasing sample sizes with steps is inferior to constant or decreasing sample sizes with steps. Furthermore, the performance of DPSO is improved when OCBA (Optimal Computing Budget Allocation) is incorporated in selecting the best particle in each step. In applying OCBA in DPSO, smaller value of incremental sample size is preferred to obtain better solutions

In order to achieve success in ground operations, searching for moving targets is one of critical factors. Usually, the system of searching for adversary ground moving targets has complex properties which includes target’s moving characteristics, camouflage level, terrain, weather, available search time window, distance between target and searcher, moving speed, target’s tactics, etc. The purpose of this paper is to present a practical quantitative method for effectively searching for infiltrated moving targets considering aforementioned complex properties. Based upon search theories, this paper consists of two parts. One is infiltration route analysis, through terrain and mobility analysis. The other is building dynamic probability maps through Monte Carlo simulation to determine the prioritized searching area for moving targets. This study primarily considers ground moving targets’ moving pattern. These move by foot and because terrain has a great effect on the target’s movement, they generally travel along a constrained path. With the ideas based on the terrain’s effect, this study deliberately performed terrain and mobility analysis and built a constrained path. In addition, dynamic probability maps taking terrain condition and a target’s moving speed into consideration is proposed. This analysis is considerably distinct from other existing studies using supposed transition probability for searching moving targets. A case study is performed to validate the effectiveness and usefulness of our methodology. Also, this study suggests that the proposed approach can be used for searching for infiltrated ground moving target within critical time window. The proposed method could be used not only to assist a searcher’s mission planning, but also to support the tactical commander’s timely decision making ability and ensure the operations’ success.

Many real world optimization problems are discrete and multi-valued. Meta heuristics including Genetic Algorithm and Particle Swarm Optimization have been effectively used to solve these multi-valued optimization problems. However, extensive comparative study on the performance of these algorithms is still required. In this study, performance of these algorithms is evaluated with multi-modal and multi-dimensional test functions. From the experimental results, it is shown that Discrete Particle Swarm Optimization (DPSO) provides better and more reliable solutions among the considered algorithms. Also, additional experiments shows that solution quality of DPSO is not lowered significantly when bit size representing a solution increases. It means that bit representation of multi-valued discrete numbers provides reliable solutions instead of becoming barrier to performance of DPSO.

Particle Swarm Optimization (PSO) which has been well known to solve continuous problems can be applied to discrete combinatorial problems. Several DPSO (Discrete Particle Swarm Optimization) algorithms have been proposed to solve discrete problems such a

In a technological driven environment, a depreciation estimate which is based on traditional like analysis results in a decelerated rate of capital recovery This time pattern of technological growths models needs to be incorporated into life analysis fram

This paper introduces a case study for efficient generation of production schedules in a tube manufacturing system. The considered scheduling problem consists of two sub problems : lot sizing for a job and job sequencing. Since these problems require simu

  본 논문은 존 조정하에서의 자동반송차량 네트워크에서 발생하는 고착을 해결하기 위한 두가지 효과적인 알고리듬을 소개한다. 이 알고리듬들은 특별히 양방향 네트워크에 알맞도록 고안되었다. 사이클 제거 알고리듬은 고착을 예방하기 위한 차량의 안전한 라우트를 결정하나, 그래프 축소 알고리듬은 고착을 회피하기 위하여 미래 잠재적인 고착 발생 조건을 결정한다. 시뮬레이션을 통하여 알고리듬들의 성능을 비교 분석한 결과 작업물의 이동 횟수와 알고리듬의 시간 복잡성

  본 논문은 시간 제약을 갖는 차량 라우팅 문제를 해결하기 위해 유전자 알고리듬과 부분 최적화 알고리듬을 적용한 방법을 소개한다. 유전자 알고리듬에서의 염색체는 노드를 나타내는 정수의 순열로 표현되어 직접적인 해를 나타내지 않지만, 경험적 방법에 의한 해석을 통해 유효한 해로 변형되도록 하였다. 유전자 알고리듬에 의해 생성된 주어진 수의 우수한 해들에는 세 부분 최적화 방법이 순차적으로 적용되어 보다 좋은 해를 생성하도록 하였다. 부분 최적화 방법들에

우리의 전통 발효식품인 동치미로부터 E. coli O157에 대한 항균 활성을 나타내는 L. plantarum K11 균주가 생산하는 박테리오신의 활성에 영향을 미치는 배양 조건에 관하여 살펴보았다. 본 균주가 생산하는 박테리오신은 MRS 배지 상에서 가장 높은 활성을 나타내었고 M17, BHI 및 TSB 보다 균의 증식속도도 빠르게 나타났다. 대수기 초기부터 활성이 점점 증가하기 시작하여 정지기 때 최대에 이르렀고 이후에는 급격히 감소하였다 배양온도의 영향으로는 온도가 상승함에 따라 박테리오신의 활성도 증가하여 최대 활성은 37circC상에서 나타났고 45circC에서는 오히려 활성이 감소하였다. 배지의 초기 pH 영향을 살펴본 결과 배양 8시간부터 pH 7.0과 8.0에서 활성이 나타나기 시작하여 배양이 진행될수록 pH 7.0에서 최대로 나타났으나, pH 5.0과 9.0에서는 활성이 매우 약하게 나타났다 Glucose 0.5, 1.0%와 lactose 0.5sim1.5%를 첨가한 경우 대조구에 비해 박테리오신 활성이 2배 이상 증가하였으나, galactose 1.0%, fructose 1.0% 및 maltose 1.5% 이상 첨가 시에는 대조구 보다 오히려 활성이 감소되었다. 질소원은 0.5%의 beef extract나 tryptone 혹은 0.5와 1.0%의 peptone이나 yeast extract에 의해 대조구와 동일한 활성을 나타내었으나, 그 이상의 농도로 첨가했을 때에는 활성이 감소되었다. 또한 NaCl 0.5%와 K2HPO4 0.5% 첨가한 경우 박테리오신의 활성이 대조구보다 2배 증가한 반면, NaCl 2.0%, K2HPO4 2.0% 이상, MgSO4cdot7H2O 1.5% 이상 및 MnSO4cdotH2O 1.0% 이상을 첨가했을 때에는 대조구 활성의 50% 이하로 감소하였다.

In designing and operating cellular networks, it is assumed that the area of coverage is geographically divided into hexagonal cells. Among these cells, a certain number of cells are chosen to install switches that serve as relays for communications between any pair of cells. Then, each cell is assigned to a switch to complete the cellular network. This decision regarding assignment of cells to switches is known as a CSA (Cell-to-Switch Assignment) problem. Since this problem is so-called NP hard problem, many researches have proposed heuristic-based algorithms to provide near-optimal solutions with a reasonable computation time. Considering these characteristics of the CSA problem, this work develops a genetic algorithm and a local search algorithm. Throu호out a number of experiments, the performance of the proposed algorithms are evaluated, and compared with existing heuristic method

In this work, a simulation study is accomplished to evaluate the effectiveness of EDG-based deadlock avoidance strategy. The considered strategy has been proposed in order to effectively handle conflicts and deadlocks occurring in zone-control AGV (Automated Guided Vehicle) systems. It is based on the prediction of deadlock possibility and prohibition of vehicle movement to the next zone until the deadlock possibility is removed. Throughout simulation study applied to complex network-type AGV path, the considered deadlock-avoidance strategy has provided satisfactory results even under the large number of vehicles.