• Journal of the Korean Operations Research and Management Science Society
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• v.35 no.1
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• pp.97-109
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• 2010

In this paper, we study a joint design problem of sampling inspections and service capacities for multi-products. Products are supplied in batches after rectifying inspections, that is, rejected lots are 100% inspected and defective products are reworked to good ones. When supplied, all defective products are uncovered through total inspection and returned to service. By exploiting the first and second order properties of the objective function, we identify the optimal inspection policies and service capacities for individual products, and develop allocation algorithms to obtain an optimal allocation of the limited total service capacity to products with the small number of computations.

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.4
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• pp.43-56
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• 2009

In korean peninsular, aircraft defense with SAM (Surface-to-Air Missile) is very important because of short range of combat space in depth. Effective and successful defense operation largely depends on two factors, SAM's location and the number of SAM for each target based on missile's availability in each SAM's location. However, most previous papers have handled only the former. In this paper, we developed Set covering model which can handle both factors simultaneously and Multi-heuristic algorithm for solving allocation problem of the batteries and missile assignment problem in each battery. Genetic algorithm is used to decide optimal location of the batteries. To determine the number of SAM, a heuristic algorithm is applied for solving missile assignment problem. If the proposed model is applied to allocation of SAM, it will improve the effectiveness of air defense operations.

• Journal of the Korean Operations Research and Management Science Society
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• v.29 no.4
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• pp.175-187
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• 2004

We consider the simultaneous selection of part routes for multiple part types in Flexible Manufacturing Systems (FMSs). Using an optimization framework we investigate two alternative route assignment policies. The one, called routing mix policy in the literature, specifies the optimal proportion of each part type to be produced along its alternative routes, assuming that the proportions can be kept during execution. The other one, which we propose and call pallet allocation policy, partitions the pallets assigned to each part type among the routes. The optimization framework used is a nonlinear programming superimposed on a closed queueing network model of an FMS which produces multiple part types with distinct repeated visits to certain workstations. The objective is to maximize the weighted throughput. Our study shows that the simultaneous use of multiple routes leads to reduced bottleneck utilization, improved workload balance, and a significant increase in the FMS's weighted throughput, without any additional capital investments. Based on numerical work, we also conjecture that pallet allocation policy is more robust than routing mix policy, operationally easier to implement, and may yield higher revenues.

• Journal of Information Processing Systems
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• v.7 no.3
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• pp.397-412
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• 2011

Granular Computing has emerged as a unified and coherent framework of designing, processing, and interpretation of information granules. Information granules are formalized within various frameworks such as sets (interval mathematics), fuzzy sets, rough sets, shadowed sets, probabilities (probability density functions), to name several the most visible approaches. In spite of the apparent diversity of the existing formalisms, there are some underlying commonalities articulated in terms of the fundamentals, algorithmic developments and ensuing application domains. In this study, we introduce two pivotal concepts: a principle of justifiable granularity and a method of an optimal information allocation where information granularity is regarded as an important design asset. We show that these two concepts are relevant to various formal setups of information granularity and offer constructs supporting the design of information granules and their processing. A suite of applied studies is focused on knowledge management in which case we identify several key categories of schemes present there.

• IEMEK Journal of Embedded Systems and Applications
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• v.11 no.3
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• pp.153-162
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• 2016

This paper proposes an optimal dynamic resource allocation method in IoT (Internet of Things) parking guidance system using Q-learning resource allocation model. In the proposed method, a resource allocation using a forecasting model based on Q-learning is employed for optimal utilization of parking guidance system. To demonstrate efficiency and availability of the proposed method, it is verified by computer simulation and practical testbed. Through simulation results, this paper proves that the proposed method can enhance total throughput, decrease penalty fee issued by SLA (Service Level Agreement) and reduce response time with the dynamic number of users.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1915-1919
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• 2003

A practical method of adaptive rate allocation to source and channel codings for an independent loss channel is proposed for Internet video. It is based on the observations that the values of residual loss probabilities at the optimal code rates for different packet loss probabilities are closely clustered to the average residual loss probability for a transmission frame size n in RS(n,k) code and for a total bit rate R. These observations aye then exploited to find the code rate for maximum PSNR. Simulation results demonstrate that the proposed method achieves a near-optimal bit-rate allocation in the joint source-channel coding of H.263 and RS(n,k) codings.

• IE interfaces
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• v.11 no.1
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• pp.85-95
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• 1998

This study is focussed on an optimal vehicle routing model for multi-supply centers in two-echelon logistic system. The aim of this study is to deliver goods for demand sites with optimal decision. This study investigated an integrated model using step-by-step approach based on relationship that exists between the inventory allocation and vehicle routing with restricted amount of inventory and transportations such as the capability of supply centers, vehicle capacity and transportation parameters. Three sub-models are developed: 1) sector-clustering model, 2) a vehicle-routing model based on clustering and a heuristic algorithm, and 3) a vehicle route scheduling model using TSP-solver based on genetic and branch-and-bound algorithm. Also, we have developed computer programs for each sub-models and user interface with visualization for major inputs and outputs. The application and superior performance of the proposed model are demonstrated by several sample runs for the inventory-allocation and vehicle routing problems.

• Journal of the military operations research society of Korea
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• v.22 no.1
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• pp.30-42
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• 1996

This study deals with an optimal firecar location and allocation models under uncertain enemy attack at the airforce base. It allocates just one firecar on the runaway and the other firecars on the headquarter of firecar company in usual situation. It is possible for several facilities at the airforce base to be attacked simultaneously by missiles enemy air attacker, other things under uncertain enemy attack. We formulate two stochastic LP location-allocation models to deal with uncertainty. One model is to locate all firecars on one site like present situation. We generate a new firecar location with a weighted average method. We call this model "centralized allocation model". The other model is to distribute firecars on several possible sites. We call this model "distributed allcoation model". Finally, we compare two models with computer experimentations on 8 airforce bases.on 8 airforce bases.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.3
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• pp.46-58
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• 1998

Multi-disk data allocation problem examined in this paper is to find a method to distribute a Binary Cartesian Product File on multiple disks to maximize parallel disk I/O accesses for partial match retrieval. This problem is known to be NP-hard, and heuristkc approaches have been applied to obtain sub-optimal solutions. Recently, efficient methods have been proposed with a restriction that the number of disks in which files are stored should be power of 2. In this paper, we propose a new disk Allocation method based on Genetic Algorithm(GA) to remove the restriction on the number of disks to be applied. Using the schema theory, we prove that our method can find a near-optimal solutionwith high probability. We compare the quality of solution derived by our method with General Disk Modulo, Binary Disk Modulo, and Error Correcting Code methods through the simulation. The simulation results show that proposed GA is superior to GDM method in all cases and provides comparable performance to the BDM method which has a restriction on the number of disks.

• The Transactions of the Korea Information Processing Society
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• v.6 no.10
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• pp.2659-2669
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• 1999

This paper a heuristic, clustering based task allocation scheme applicable to non-directed task graph on a distributed system. This scheme firstly builds a task-machine graph, and then applies a clustering process where in a pair of tasks that are connected to the highest cost edge is merged into a big one or a task is allocated to a machine. During the process, the proposed scheme figure out a machine onto which the task allocation may cause deduction of large communication overhead that has incurred between the task and tasks that are already allocated to the machine while the computation costs is slightly increased in the machine. Simulation for the various task graphs shows that the scheduling using the proposed scheme result far better than ones by using the traditional schemes. A comparison with optimal task scheduling also promises that our scheme derives optimal results more occasionally than the traditional schemes do.