• 국제학술발표논문집
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• The 5th International Conference on Construction Engineering and Project Management
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• pp.243-251
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• 2013

Cash management is a major concern for all contractors in the construction industry. It is arguable that cash is the most critical resource of all. A contractor needs to secure sufficient funds to navigate the project to the end, while keeping an eye on maximizing profits along the way. Past research attempted to address such topic via developing models to tackle the time-cost tradeoff problem, cash flow forecasting, and cash flow management. Yet, little was done to integrate the three aspects of cash management together. This paper, as such, presents a comprehensive model that integrates the time-cost tradeoff problem, cash flow management, and cash flow forecasting. First, the model determines the project optimal completion time by considering the different alternative construction methods available for executing project activities. Second, it investigates different funding alternatives and proposes a project-level cash management plan. Two funding alternatives are considered; they are borrowing and company own financing. The model was built as a combinatorial optimization model that utilizes ant colony search capabilities. The model also utilizes Microsoft Project software and spreadsheets to maintain an environment that incorporates activities, their durations, and other project data, in order to estimate project completion time and cost. Ant Colony Optimization algorithm was coded as a Macro program using VBA. Finally, an example project was used to test the developed model, where it acted reliably in maximizing the contractor's profit in the test project.

• 한국통신학회논문지
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• 제35권9B호
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• pp.1281-1286
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• 2010

개미 기반 라우팅 방안은 개미집단 최적화 알고리즘의 한 부류로, 자연에서 서식하는 개미의 행동양식을 라우팅에 적용한 방안이다. 이동 애드혹 네트워크는 토폴로지가 동적으로 변하므로 경로 설정이 지역적 정보에 기반을 둘 필요가 있다. 따라서 이동 애드혹 네트워크에서의 라우팅은 개미집단 최적화의 한 응용분야로 알려져 있다. 본 논문에서는 이동 애드혹 네트워크에 적용한 개미 기반 라우팅 알고리즘인 SIR (swarm intelligence routing)에 경로선택과 링크 장애 시 처리 방법을 개선한 방안인 EPMAR (ant-based routing method using enhanced path maintenance)을 제안하고, 그 성능을 AntHocNet 및 SIR과 비교, 분석하였다. 분석 결과, 제안한 방안이 AntHocNet이나 SIR보다 패킷 전달율은 높고, 치명적 경로 장애가 더 적게 발생함을 입증하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권6호
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• pp.2504-2526
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• 2016

Wireless sensors are always deployed in brutal environments, but as we know, the nodes are powered only by non-replaceable batteries with limited energy. Sending, receiving and transporting information require the supply of energy. The essential problem of wireless sensor network (WSN) is to save energy consumption and prolong network lifetime. This paper presents a new communication protocol for WSN called Dynamical Threshold Control Algorithm with three-parameter Particle Swarm Optimization and Ant Colony Optimization based on residual energy (DPA). We first use the state of WSN to partition the region adaptively. Moreover, a three-parameter of particle swarm optimization (PSO) algorithm is proposed and a new fitness function is obtained. The optimal path among the CHs and Base Station (BS) is obtained by the ant colony optimization (ACO) algorithm based on residual energy. Dynamical threshold control algorithm (DTCA) is introduced when we re-select the CHs. Compared to the results obtained by using APSO, ANT and I-LEACH protocols, our DPA protocol tremendously prolongs the lifecycle of network. We observe 48.3%, 43.0%, and 24.9% more percentages of rounds respectively performed by DPA over APSO, ANT and I-LEACH.

• Structural Engineering and Mechanics
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• 제45권3호
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• pp.391-410
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• 2013

A numerical method is presented here to detect and assess structural damages from changes in natural frequencies using Ant Colony Optimization (ACO) algorithm. It is possible to formulate the inverse problem in terms of optimization and then to utilize a solution technique employing ACO to assess the damage/damages of structures using natural frequencies. The laboratory tested data has been used to verify the proposed algorithm. The study indicates the potentiality of the developed code to solve a wide range of inverse identification problems in a systematic manner. The developed code is used to assess damages of beam like structures using a first few natural frequencies. The outcomes of the simulated results show that the developed method can detect and estimate the amount of damages with satisfactory precision.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2004년도 춘계공동학술대회 논문집
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• pp.319-322
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• 2004

물류/택배업계의 공급사슬관리에서 차량에 의한 고객의 요구 서비스 시간대 만족은 고객의 재고수준을 낮추고 또한 서비스 수준의 향상에 매우 중요한 제약조건이다. 최근에 소개된 메타휴리스틱인 개미해법(Ant Colony Optimization: ACO)은 NP-hard 문제의 해공간 탐색에 있어서 상당한 장점을 가지고 있으나, 시간대 제약이 있는 차량경로문제(Vehicle Routing Problems with Time Windows: VRPTW)에 대한 적용은 아주 미비한 실정이다. 따라서, 본 연구에서는 ACO 를 VRPTW에 적용하여 최선의 차량경로 해를 구하기 위한 여러 변형을 제시하고, 이들의 영향을 다양한 실험문제를 이용하여 분석하고자 한다. 계산실험 결과, 기본 ACO 에 여러 설계 요소들을 추가함에 따라 계산시간이 다소 증가하지만 보다 우수한 차량경로 해를 구할 수 있었다.

• 경영과학
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• 제25권2호
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• pp.73-80
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• 2008

The location area planning is to assign cells to the location areas of a wireless communication network in an optimum manner. The two important cost components are cost of location update and cost of paging that are of conflicting in nature; i.e., minimizing the registration cost might increase the search cost. Hence, it is important to find a compromise between the location update and paging operations such that the cost of mobile terminal location tracking cost is a minimum. The complete mobile network is divided into location areas. Each location area consists of a group of cells. In fact this is shown to be an NP-complete problem in an earlier study. In this paper, we use an ant colony optimization method to obtain the best/optimal group of cells for a given a network.

• 산업공학
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• 제19권2호
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• pp.168-173
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• 2006

In the Reporting Cell Location Management (RCLM) system, a subset of cells in the network is designated as the reporting cells. Each mobile terminal performs location update only when it enters one of these reporting cells. When a call arrives, the paging is confined to the reporting cell the user last reported and the neighboring bounded non-reporting cells. Frequent location update may result in degradation of quality of service due to interference. Miss on the location of a mobile terminal will necessitate a search operation on the network when a call comes in. We must decide the number of reporting cells and which cell should be reporting cell to balance the registration (location update) and search (paging) operations to minimize the cost of RCLM system. This paper proposes a ranking ant colony system (RACS) for optimization of RCLM system.

• 대한산업공학회지
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• 제35권2호
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• pp.160-170
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• 2009

This paper studies a vehicle routing problem variant which considers customers to require simultaneous delivery and pick-up under time windows(VRPSDP-TW). The objective of this paper is to minimize the total travel distance of routes that satisfy both the delivery and pick-up demand. We propose a heuristic algorithm for solving the VRPSDP-TW, based on the ant colony system(ACS). In route construction, an insertion algorithm based ACS is applied and the interim solution is improved by local search. Through iterative processes, the heuristic algorithm drives the best solution. Experiments are implemented to evaluate a performance of the algorithm on some test instances from literature.

• 경영과학
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• 제26권3호
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• pp.55-65
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• 2009

The objective of this paper is to propose an ant colony optimization (ACO) for clustering design in wireless sensor network problem. This proposed ACO approach is designed to deal with the dynamics of the sensor nodes which can be adaptable to topological changes to any network graph in a time. Long communication distances between sensors and a sink in a sensor network can greatly consume the energy of sensors and reduce the lifetime of a network. We can greatly minimize the total communication distance while minimizing the number of cluster heads using proposed ACO. Simulation results show that our proposed method is very efficient to find the best solutions comparing to the optimal solution using CPLEX in 100, 200, and 400 node sensor networks.

• Journal of Information Processing Systems
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• 제16권5호
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• pp.1169-1182
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• 2020

The ant colony optimization (ACO) algorithm is a classical metaheuristic optimization algorithm. However, the conventional ACO was liable to trap in the local minimum and has an inherent slow rate of convergence. In this work, we propose a novel combinatorial ACO algorithm (CG-ACO) to alleviate these limitations. The genetic algorithm and the cloud model were embedded into the ACO to find better initial solutions and the optimal parameters. In the experiment section, we compared CG-ACO with the state-of-the-art methods and discussed the parameter stability of CG-ACO. The experiment results showed that the CG-ACO achieved better performance than ACO_R, simple genetic algorithm (SGA), CQPSO and CAFSA and was more likely to reach the global optimal solution.