Hyoungtae Kim | Woosong University (original) (raw)
Papers by Hyoungtae Kim
Journal of Society of Korea Industrial and Systems Engineering, 2015
Journal of Society of Korea Industrial and Systems Engineering, 2014
J. Soc. Korea Ind. Syst. Eng., 2013
J. Soc. Korea Ind. Syst. Eng., 2013
J. Soc. Korea Ind. Syst. Eng., 2013
Lifetime Data Analysis, 2004
We consider a multi-component load-sharing system in which the failure rate of a given component ... more We consider a multi-component load-sharing system in which the failure rate of a given component depends on the set of working components at any given time. Such systems can arise in software reliability models and in multivariate failure-time models in biostatistics, for example. A load-share rule dictates how stress or load is redistributed to the surviving components after a component fails within the system. In this paper, we assume the load share rule is unknown and derive methods for statistical inference on load-share parameters. We consider components with (individual) constant failure rates in two environments: (1) the system load is distributed evenly among the working components, and (2) only assume the load for each working component increases when other components in the system fail. Tests for these special load-share models are investigated.
Lifetime Data Analysis, 2004
We consider a multi-component load-sharing system in which the failure rate of a given component ... more We consider a multi-component load-sharing system in which the failure rate of a given component depends on the set of working components at any given time. Such systems can arise in software reliability models and in multivariate failure-time models in biostatistics, for example. A load-share rule dictates how stress or load is redistributed to the surviving components after a component fails within the system. In this paper, we assume the load share rule is unknown and derive methods for statistical inference on load-share parameters. We consider components with (individual) constant failure rates in two environments: (1) the system load is distributed evenly among the working components, and (2) only assume the load for each working component increases when other components in the system fail. Tests for these special load-share models are investigated.
International Journal of Production Economics, 2011
This research seeks to determine the optimal order amount for the retailer given uncertainty in a... more This research seeks to determine the optimal order amount for the retailer given uncertainty in a supply-chain's logistics network due to unforseeable disruption or various types of defects (e.g., shipping damage, missing parts and misplacing products). Mixture distribution models characterize problems from solitary failures and contingent events causing network to function ineffectively. The uncertainty in the number of good products successfully reaching the distribution center (DC) and retailer poses a challenge in deciding product-order amounts. Because the commonly used ordering plan developed for maximizing expected profits does not allow retailers to address concerns about contingencies, this research proposes two improved procedures with risk-averse characteristics towards low probability and high impact events. Several examples illustrate the impact of DC's operation policies and model assumptions on retailer's product-ordering plan and resulting sales profit.
This research seeks to determine the optimal order amount for the retailer given uncertainty in a... more This research seeks to determine the optimal order amount for the retailer given uncertainty in a supply-chain's logistics network due to unforseeable disruption or various types of defects (e.g., shipping damage, missing parts and misplacing products). Mixture distribution models characterize problems from solitary failures and contingent events causing network to function ineffectively. The uncertainty in the number of good products successfully reaching the distribution center (DC) and retailer poses a challenge in deciding product-order amounts. Because the commonly used ordering plan developed for maximizing expected profits does not allow retailers to address concerns about contingencies, this research proposes two improved procedures with risk-averse characteristics towards low probability and high impact events. Several examples illustrate the impact of DC's operation policies and model assumptions on retailer's product-ordering plan and resulting sales profit.
International Journal of Production Economics, 2011
This research seeks to determine the optimal order amount for a retailer given uncertainty in a s... more This research seeks to determine the optimal order amount for a retailer given uncertainty in a supplychain's logistics network due to unforeseeable disruption or various types of defects (e.g., shipping damage, missing parts and misplacing products). Mixture distribution models characterize problems from solitary failures and contingent events causing network to function ineffectively. The uncertainty in the number of good products successfully reaching distribution centers (DCs) and retail stores poses a challenge in deciding product-order amounts. Because the commonly used ordering plan developed for maximizing expected profits does not allow retailers to address concerns about contingencies; this research proposes two improved procedures with risk-averse characteristics towards low probability and high impact events. Several examples illustrate the impact of a DC's operation policies and model assumptions on a retailer's product-ordering plan and resulting sales profit.
This research seeks to determine the optimal order amount for the retailer given uncertainty in a... more This research seeks to determine the optimal order amount for the retailer given uncertainty in a supply-chain's logistics network due to unforseeable disruption or various types of defects (e.g., shipping damage, missing parts and misplacing products). Mixture distribution models characterize problems from solitary failures and contingent events causing network to function ineffectively. The uncertainty in the number of good products successfully reaching the distribution center (DC) and retailer poses a challenge in deciding product-order amounts. Because the commonly used ordering plan developed for maximizing expected profits does not allow retailers to address concerns about contingencies, this research proposes two improved procedures with risk-averse characteristics towards low probability and high impact events. Several examples illustrate the impact of DC's operation policies and model assumptions on retailer's product-ordering plan and resulting sales profit.
In this paper, the single-period inventory problem, what is called newsboy problem, was revisited... more In this paper, the single-period inventory problem, what is called newsboy problem, was revisited with two different conditions, uncertain supply and risk-averseness. Eeckhoudt et al. investigated the effect of riskaverseness of a newsboy on the optimal order quantity in a stochastic demand setting. In contrary to Eeckhoudt et al. [5] this paper investigates the effect of risk-averseness in a stochastic supply setting. The findings from this investigation say that if * represents the optimal order quantity without risk-averseness then the risk-averse optimal order quantity can be greater than * and can be less than * as well. Likewise this research proved that in the outstanding problem framework the degree of risk-aversion itself is not enough to make magnitude comparison between two optimal order quantities.
Journal of Society of Korea Industrial and Systems Engineering, 2015
Journal of Society of Korea Industrial and Systems Engineering, 2014
J. Soc. Korea Ind. Syst. Eng., 2013
J. Soc. Korea Ind. Syst. Eng., 2013
J. Soc. Korea Ind. Syst. Eng., 2013
Lifetime Data Analysis, 2004
We consider a multi-component load-sharing system in which the failure rate of a given component ... more We consider a multi-component load-sharing system in which the failure rate of a given component depends on the set of working components at any given time. Such systems can arise in software reliability models and in multivariate failure-time models in biostatistics, for example. A load-share rule dictates how stress or load is redistributed to the surviving components after a component fails within the system. In this paper, we assume the load share rule is unknown and derive methods for statistical inference on load-share parameters. We consider components with (individual) constant failure rates in two environments: (1) the system load is distributed evenly among the working components, and (2) only assume the load for each working component increases when other components in the system fail. Tests for these special load-share models are investigated.
Lifetime Data Analysis, 2004
We consider a multi-component load-sharing system in which the failure rate of a given component ... more We consider a multi-component load-sharing system in which the failure rate of a given component depends on the set of working components at any given time. Such systems can arise in software reliability models and in multivariate failure-time models in biostatistics, for example. A load-share rule dictates how stress or load is redistributed to the surviving components after a component fails within the system. In this paper, we assume the load share rule is unknown and derive methods for statistical inference on load-share parameters. We consider components with (individual) constant failure rates in two environments: (1) the system load is distributed evenly among the working components, and (2) only assume the load for each working component increases when other components in the system fail. Tests for these special load-share models are investigated.
International Journal of Production Economics, 2011
This research seeks to determine the optimal order amount for the retailer given uncertainty in a... more This research seeks to determine the optimal order amount for the retailer given uncertainty in a supply-chain's logistics network due to unforseeable disruption or various types of defects (e.g., shipping damage, missing parts and misplacing products). Mixture distribution models characterize problems from solitary failures and contingent events causing network to function ineffectively. The uncertainty in the number of good products successfully reaching the distribution center (DC) and retailer poses a challenge in deciding product-order amounts. Because the commonly used ordering plan developed for maximizing expected profits does not allow retailers to address concerns about contingencies, this research proposes two improved procedures with risk-averse characteristics towards low probability and high impact events. Several examples illustrate the impact of DC's operation policies and model assumptions on retailer's product-ordering plan and resulting sales profit.
This research seeks to determine the optimal order amount for the retailer given uncertainty in a... more This research seeks to determine the optimal order amount for the retailer given uncertainty in a supply-chain's logistics network due to unforseeable disruption or various types of defects (e.g., shipping damage, missing parts and misplacing products). Mixture distribution models characterize problems from solitary failures and contingent events causing network to function ineffectively. The uncertainty in the number of good products successfully reaching the distribution center (DC) and retailer poses a challenge in deciding product-order amounts. Because the commonly used ordering plan developed for maximizing expected profits does not allow retailers to address concerns about contingencies, this research proposes two improved procedures with risk-averse characteristics towards low probability and high impact events. Several examples illustrate the impact of DC's operation policies and model assumptions on retailer's product-ordering plan and resulting sales profit.
International Journal of Production Economics, 2011
This research seeks to determine the optimal order amount for a retailer given uncertainty in a s... more This research seeks to determine the optimal order amount for a retailer given uncertainty in a supplychain's logistics network due to unforeseeable disruption or various types of defects (e.g., shipping damage, missing parts and misplacing products). Mixture distribution models characterize problems from solitary failures and contingent events causing network to function ineffectively. The uncertainty in the number of good products successfully reaching distribution centers (DCs) and retail stores poses a challenge in deciding product-order amounts. Because the commonly used ordering plan developed for maximizing expected profits does not allow retailers to address concerns about contingencies; this research proposes two improved procedures with risk-averse characteristics towards low probability and high impact events. Several examples illustrate the impact of a DC's operation policies and model assumptions on a retailer's product-ordering plan and resulting sales profit.
This research seeks to determine the optimal order amount for the retailer given uncertainty in a... more This research seeks to determine the optimal order amount for the retailer given uncertainty in a supply-chain's logistics network due to unforseeable disruption or various types of defects (e.g., shipping damage, missing parts and misplacing products). Mixture distribution models characterize problems from solitary failures and contingent events causing network to function ineffectively. The uncertainty in the number of good products successfully reaching the distribution center (DC) and retailer poses a challenge in deciding product-order amounts. Because the commonly used ordering plan developed for maximizing expected profits does not allow retailers to address concerns about contingencies, this research proposes two improved procedures with risk-averse characteristics towards low probability and high impact events. Several examples illustrate the impact of DC's operation policies and model assumptions on retailer's product-ordering plan and resulting sales profit.
In this paper, the single-period inventory problem, what is called newsboy problem, was revisited... more In this paper, the single-period inventory problem, what is called newsboy problem, was revisited with two different conditions, uncertain supply and risk-averseness. Eeckhoudt et al. investigated the effect of riskaverseness of a newsboy on the optimal order quantity in a stochastic demand setting. In contrary to Eeckhoudt et al. [5] this paper investigates the effect of risk-averseness in a stochastic supply setting. The findings from this investigation say that if * represents the optimal order quantity without risk-averseness then the risk-averse optimal order quantity can be greater than * and can be less than * as well. Likewise this research proved that in the outstanding problem framework the degree of risk-aversion itself is not enough to make magnitude comparison between two optimal order quantities.