• Journal of Korean Society for Quality Management
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• v.12 no.2
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• pp.33-36
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• 1984

This paper is concerned with the optimal replacement policy of auto-scale with increasing error-variance. This optimization model is to minimize the sum of the cost of defective and excess weight allowance for a target value. The numerical example for the proposed problem is solved by Golden-Section Search and Simpsons's rule.

• Journal of the Korean Operations Research and Management Science Society
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• v.3 no.2
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• pp.91-93
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• 1978

A methodology which can be employed to obtain an optimal preventive maintenance policy that maximizes the total machine running time over the specified working period is proposed. A system considered is comprised of m machines and r

• Journal of applied mathematics & informatics
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• v.6 no.1
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• pp.127-142
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• 1999

This paper develops a methematical model for growth and exploitation of a schooling fish species using a realistic catch-rate function and imposing a tax on the catch to control harvesting. Fishing effort is assumed to depend on the net revenue. The steady states of the system are determined and their local and global stabil-ity are discussed. Taking the tax as a control variable; the optimal harvest policy is formulated and solved as a control problem. The results are illustrated with the help of a numerical example.

• Journal of Korean Institute of Industrial Engineers
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• v.13 no.2
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• pp.59-63
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• 1987

An age replacement policy is considered for a system under a stepdown warranty. It is assumed that only minimal repairs are performed for failures occurred before age T.A unique optimal value of T which minimiges the expected cost rate is obtained. The cases of the free replacement warranty, prorata warranty and hybrid warranty are also considered and some numerical examples are given.

• Journal of the Korean Data and Information Science Society
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• v.18 no.3
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• pp.689-696
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• 2007

This paper considers a Bayesian approach to determine an optimal PM policy with random maintenance quality. Thus, we assume that the quality of a PM action is a random variable following a probability distribution. When the failure time is Weibull distribution with uncertain parameters, a Bayesian approach is established to formally express and update the uncertain parameters for determining an optimal PM policy. Finally, the numerical examples are presented for illustrative purpose.

• Journal of Korean Institute of Industrial Engineers
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• v.4 no.1
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• pp.49-55
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• 1978

This paper presents a new method, called a modified economic order quantity method, for determining the optimal inventory policy, which uses the rate of return as its decision criterion. Especially for the simplest possible inventory system with constant demand rate, no backlogging, no lead time, etc., the formula for the optimal order policy is derived. Also mentioned are the relative merits and shortcomings of this method compared to the conventional EOQ model.

• Journal of Korean Institute of Industrial Engineers
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• v.4 no.1
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• pp.1-3
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• 1978

This paper presents a model for determining the optimal number of minimal repairs before replacement. The basic concept parallels the periodic replacement model with minimal repair at failure introduced by Barlow and Hunter, only difference being the replacement signalled by the number of previous minimal repairs performed on the unit. In the case of Weibull distribution, which is widely used as a general failure distribution, the optimal solution could be obtained numerically and seems more cost effective compared to the Barlow and Hunter's Policy II.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.56
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• pp.1-8
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• 2000

We present in this paper an optimal stocking policy for a repairable inventory system under reliability improvement. For this purpose we illustrate commercial flight lines with a large number of planes. This model is supported by a central repair facility. For modeling the nonstationary M/M/s system we implemented SIMAN for computing the time dependent number of units in the repair facility with any number of units. In this model we provide the required inventory level at each location. 1y month. for various levels of associated stock-out risk.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5264-5281
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• 2017

Multihop relay-based cellular networks are attracting much interest because of their throughput enhancement, coverage extension, and low infrastructure cost. In these networks, relay stations (RSs) between a base station (BS) and mobile stations (MSs) drastically increase the overall spectral efficiency, with improved channel quality for MSs located at the cell edge or in shadow areas, and enhanced throughput of MSs in hot spots. These relay-based networks require an advanced radio resource management scheme because the optimal amount of radio resource for a BS-to-RS link should be allocated according to the MS channel quality and distribution, considering the interference among RSs and neighbor BSs. In this paper, we propose optimal resource planning algorithms that maximize the overall utility of relay-based networks under a proportional fair scheduling policy. In the first phase, we determine an optimal scheduling policy for distributing BS-to-RS link resources to RSs. In the second phase, we determine the optimal amount of the BS-to-RS link resources using the results of the first phase. The proposed algorithms efficiently calculate the optimal amount of resource without exhaustive searches, and their accuracy is verified by comparison with simulation results, in which the algorithms show a perfect match with simulations.

• Journal of KIBIM
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• v.12 no.2
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• pp.1-11
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• 2022

Road traffic accidents (RTAs) are the leading cause of accidental death among children. RTA reduction is becoming an increasingly important social issue among children. Municipalities aim to resolve this issue by introducing "Smart Pedestrian Crosswalks" that help prevent traffic accidents near children's facilities. Nonetheless such facilities tend to be installed in relatively limited number of areas, such as the school zone. In order for budget allocation to be efficient and policy effects maximized, optimal location selection based on machine learning is needed. In this paper, we employ machine learning models to select the optimal locations for smart pedestrian crosswalks to reduce the RTAs of children. This study develops an optimal location index using variable importance measures. By using k-means clustering method, the authors classified the crosswalks into three types after the optimal location selection. This study has broadened the scope of research in relation to smart crosswalks and traffic safety. Also, the study serves as a unique contribution by integrating policy design decisions based on public and open data.