• Korean Management Science Review
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• v.25 no.3
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• pp.45-57
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• 2008

This paper considers a genetic algorithm for sequencing activities and allocating resources to reduce the over all completion time of workflow in the presence resource constraints. The algorithm provides an integrated solution for two sub-problems. The first is to decide the priority for the activities which require the same resource. The other problem is to select one among available resources for each activity by considering the incurred setup time and the performance factor of each resource. We evaluate the algorithm performance for three different kinds of workflows including parallel structures. Computational results show that the proposed algorithm is more effective than a previous work.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.11
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• pp.89-98
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• 1998

The enhanced resource-allocating network(ERAN) that adaptively generates hidden units of radial basis function(RBF) network for systems modeling has been proposed. The ERAN is an improved version of the resource-allocating network(RAN) that allocates new hidden units based on the novelty of observation data. The learning process of the ERAN involves allocation of new hidden units and adjusting the network parameters. The network starts with no hidden units. As observation data are received, the network adds a hidden units only if the three network growth criteria are satisfied. The network parameters are adjusted by the LMS algorithm. The performance of the ERAN is compared with the RAN for nonlinear static systems modeling problem with sequential and random learning. For two simulations, the ERAN has been shown to realize RBF networks with better accuracy with fewer hidden units.

• Journal of the Korean Operations Research and Management Science Society
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• v.31 no.3
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• pp.11-25
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• 2006

Space may be a scarce resource in manufacturing shops, warehouses, freight terminals, and container terminals. This Paper discusses how to locate temporary storage Inventories In limited storage areas. A typical inventory is delivered from the location of the preceding process to the storage area and stored In the storage area during the certain period of time. And it may be relocated from the storage position to another. Finally. it is delivered from the final storage area to the location of the next process. Because this logistic process for an inventory requires handling activities, transportation activities, and storage spaces, the limitation in capacities of handling equipment, transportation equipment, and storage space must be considered when allocating spaces to the inventory. This problem Is modeled as a multicommodity minimal cost flow problem. And a heuristic algorithm for the Problem is proposed. Numerical experiments are presented to validate the mathematical model and the heuristic algorithm.

• Industrial Engineering and Management Systems
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• v.10 no.1
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• pp.54-64
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• 2011

The purpose of this study is to optimally allocate the human resources to tasks while minimizing the total daily human resource costs and smoothing the human resource usage. The human resource allocation problem (hRAP) under consideration contains two kinds of special constraints, i.e. operational precedence and skill constraints in addition to the ordinary constraints. To deal with the multiple objectives and the special constraints, first we designed this hRAP as a network problem and then proposed a Pareto multistage decisionbased genetic algorithm (P-mdGA). During the evolutionary process of P-mdGA, a Pareto evaluation procedure called generalized Pareto-based scale-independent fitness function approach is used to evaluate the solutions. Additionally, in order to improve the performance of P-mdGA, we use fuzzy logic controller for fine-tuning of genetic parameters. Finally, in order to demonstrate the applicability and to evaluate the performance of the proposed approach, P-mdGA is applied to solve a case study in a hotel, where the managers usually need helpful automatic support for effectively allocating hotel staff to hotel tasks.

• Journal of the Korea Society of Computer and Information
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• v.14 no.8
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• pp.81-88
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• 2009

Many studies and policies are suggested for customer satisfaction to survive in multimedia content service markets. there are policies like a segregating the clients using the contents service and allocating the media server's resources distinctively by clusters using the cluster analysis method of CRM. The problem of this policy is fixed allocation of media server resources. It is inefficient for costly media server resource. To resolve the problem and enhance the utilization of media server resource, the ACRFA (Adaptive Client Request Filtering Algorithm) was suggested per cluster to allocate media server resources by flexible resource allocation method.

• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.3
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• pp.322-333
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• 2012

AESA radar is able to instantaneously and adaptively position and control the beam, and such adaptive beam pointing of AESA radar enables to remarkably improve the multi-mission capability. For this reason, Radar Resource Management(RRM) becomes new challenging issue. RRM is a technique efficiently allocating finite resources, such as energy and time to each task in an optimal and intelligent way. Especially radar beam scheduling is the most critical component for the success of RRM. In this paper, we proposed a rule-based scheduling algorithm and Simulated Annealing(SA) based scheduling algorithm, which are alternatively selected and applied to beam scheduler according radar load status in real-time. The performance of the proposed algorithm was evaluated on the multi-function radar scenario. As a result, we showed that our proposed algorithm can process a lot of beams at the right time with real time capability, compared with applying only rule-based scheduling algorithm. Additionally, we showed that the proposed algorithm can save scheduling time remarkably, compared with applying only SA-based scheduling algorithm.

• Journal of the Korea Society for Simulation
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• v.28 no.1
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• pp.67-79
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• 2019

A warship equipped with Multi Function Radar(MFR) performs operations by evaluating the degree of threats based on threats' symptom and allocating the resource of MFR to the corresponding threats. This study suggests a simulation-based approach and greedy algorithm in order to effectively allocate the resource of an MFR for warships, and compares these two approaches. As a detection probability function depending on the amount of allocations to each threat, we consider linear and exponential functions. Experimental results show that both the simulation-based approach and greedy algorithm allocate resource similarly to the randomly generated threats, and the greedy algorithm outperforms the simulation-based approach in terms of computational perspective. For a various cases of threats, we analyze the results of MFR resource allocation using the greedy algorithm.

• International Journal of Aeronautical and Space Sciences
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• v.17 no.1
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• pp.89-100
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• 2016

Given the unpredictability of the space environment, satellite communications are manually performed by exchanging telecommands and telemetry. Ground support for orbiting satellites is given only during limited periods of ground antenna visibility, which can result in conflicts when multiple satellites are present. This problem can be regarded as a scheduling problem of allocating antenna support (task) to limited visibility (resource). To mitigate unforeseen errors and costs associated with manual scheduling and mission planning, we propose a novel method based on a genetic algorithm to solve the ground support problem of multiple satellites and antennae with visibility conflicts. Numerous scheduling parameters, including user priority, emergency, profit, contact interval, support time, remaining resource, are considered to provide maximum benefit to users and real applications. The modeling and formulae are developed in accordance with the characteristics of satellite communication. To validate the proposed algorithm, 20 satellites and 3 ground antennae in the Korean peninsula are assumed and modeled using the satellite tool kit (STK). The proposed algorithm is applied to two operation modes: (i) telemetry, tracking, and command and (ii) payload. The results of the present study show near-optimal scheduling in both operation modes and demonstrate the applicability of the proposed algorithm to actual mission control systems.

• International journal of advanced smart convergence
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• v.6 no.1
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• pp.39-43
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• 2017

As the industry grows, the amount of data grows exponentially, and data analysis using these serves as a predictable solution. As data size increases and processing speed increases, it has begun to be applied to new fields by combining artificial intelligence technology as well as simple big data analysis. In this paper, we propose a method to quickly apply a machine learning based algorithm through efficient resource allocation. The proposed algorithm allocates memory for each attribute. Learning Distinct of Attribute and allocating the right memory. In order to compare the performance of the proposed algorithm, we compared it with the existing K-means algorithm. As a result of measuring the execution time, the speed was improved.

• Journal of the Korea Society of Computer and Information
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• v.12 no.3
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• pp.147-153
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• 2007

Task scheduling is an essential part of any computer system for allocating tasks to a processor of the system among various competitors. As we know, in real-time system, the failure of scheduling a hard real-time task my lead to disastrous consequence. Besides efficiency, resource and speed, real-time system has to take time constraint in serious consideration. This paper proposes a priority-driven scheduling algorithm for real-time multiprocessor system. which is called L-RE coordinates algorithm. L-RE coordinates is a new way of describing the task scheduling problem. In the algorithm, we take both deadline and laxity into consideration for allocating the priority. The simulation result shows that the new algorithm is viable and performance better than EDF and LLF algorithm on schedulability and context switch respectively.