Kash Barker | University of Oklahoma (original) (raw)

Papers by Kash Barker

Research paper thumbnail of Multiobjective Stochastic Inoperability Decision Tree for Infrastructure Preparedness

Journal of Infrastructure Systems, 2013

Research paper thumbnail of Proportional hazards models of infrastructure system recovery

Reliability Engineering & System Safety, 2014

Research paper thumbnail of Decision Trees with Single and Multiple Interval-Valued Objectives

Research paper thumbnail of Improved Acquisition for System Sustainment: Multiobjective Tradeoff Analysis for Condition-Based Decision-Making

Research paper thumbnail of Interval-valued availability framework for supplier selection based on component importance

International Journal of Production Research, 2015

Research paper thumbnail of Modeling infrastructure resilience using Bayesian networks: A case study of inland waterway ports

Computers & Industrial Engineering, 2016

Research paper thumbnail of Removing hazardous products from the food chain

Research paper thumbnail of Modeling a severe supply chain disruption and post-disaster decision making with application to the Japanese earthquake and tsunami

Http Dx Doi Org 10 1080 0740817x 2013 876241, Aug 15, 2014

ABSTRACT Modern supply chains are increasingly vulnerable to disruptions, and a disruption in one... more ABSTRACT Modern supply chains are increasingly vulnerable to disruptions, and a disruption in one part of the world can cause supply difficulties for companies around the globe. This article develops a model of severe supply chain disruptions in which several suppliers suffer from disabled production facilities and firms that purchase goods from those suppliers may consequently suffer a supply shortage. Suppliers and firms can choose disruption management strategies to maintain operations. A supplier with a disabled facility may choose to move production to an alternate facility, and a firm encountering a supply shortage may be able to use inventory or buy supplies from an alternate supplier. The supplier’s and firm’s optimal decisions are expressed in terms of model parameters such as the cost of each strategy, the chances of losing business, and the probability of facilities reopening. The model is applied to a simulation based on the 2011 Japanese earthquake and tsunami, which closed several facilities of key suppliers in the automobile industry and caused supply difficulties for both Japanese and U.S. automakers.

Research paper thumbnail of Bayesian Kernel Methods for Non-Gaussian Distributions: Binary and Multi-class Classification Problems

The public reporting burden for this collection of information is estimated to average 1 hour per... more The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information.

Research paper thumbnail of Quantifying the risk of project delays with a genetic algorithm

International Journal of Production Economics, 2015

Research paper thumbnail of Bayesian Kernel Methods for Critical Infrastructure Resilience Modeling

Vulnerability, Uncertainty, and Risk, 2014

Research paper thumbnail of Analyzing interdependent impacts of resource sustainability

Environment Systems and Decisions, 2013

ABSTRACT

Research paper thumbnail of A Bayesian beta kernel model for binary classification and online learning problems

Statistical Analysis and Data Mining, 2014

ABSTRACT Recent advances in data mining have integrated kernel functions with Bayesian probabilis... more ABSTRACT Recent advances in data mining have integrated kernel functions with Bayesian probabilistic analysis of Gaussian distributions. These machine-learning approaches can incorporate prior information with new data to calculate probabilistic rather than deterministic values for unknown parameters. This article extensively analyzes a specific Bayesian kernel model that uses a kernel function to calculate a posterior beta distribution that is conjugate to the prior beta distribution. Numerical testing of the beta kernel model on several benchmark datasets reveals that this model's accuracy is comparable with those of the support vector machine (SVM), relevance vector machine, naive Bayes, and logistic regression, and the model runs more quickly than all the other algorithms except for logistic regression. When one class occurs much more frequently than the other class, the beta kernel model often outperforms other strategies to handle imbalanced datasets, including under-sampling, over-sampling, and the Synthetic Minority Over-Sampling Technique. If data arrive sequentially over time, the beta kernel model easily and quickly updates the probability distribution, and this model is more accurate than an incremental SVM algorithm for online learning.

Research paper thumbnail of Importance measures for inland waterway network resilience

Transportation Research Part E: Logistics and Transportation Review, 2014

Research paper thumbnail of Integrating simulation and risk-based sensitivity analysis methods in hospital emergency department design

International Series in Operations Research & Management Science, 2012

Research paper thumbnail of Measures of Inland Waterway Network Resilience

INCOSE International Symposium, 2013

Research paper thumbnail of Sensitivity analysis for simulation-based decision making: Application to a hospital emergency service design

Simulation Modelling Practice and Theory, 2012

An increasing concern of decision makers when dealing with system design is preparation for a wid... more An increasing concern of decision makers when dealing with system design is preparation for a wide range of potentially uncertain operating conditions. This paper provides a novel multiobjective approach for simulation-driven decision making that accounts for not only ...

Research paper thumbnail of Stochastic Measures of Network Resilience: Applications to Waterway Commodity Flows

Risk Analysis, 2014

Given the ubiquitous nature of infrastructure networks in today&a... more Given the ubiquitous nature of infrastructure networks in today's society, there is a global need to understand, quantify, and plan for the resilience of these networks to disruptions. This work defines network resilience along dimensions of reliability, vulnerability, survivability, and recoverability, and quantifies network resilience as a function of component and network performance. The treatment of vulnerability and recoverability as random variables leads to stochastic measures of resilience, including time to total system restoration, time to full system service resilience, and time to a specific α% resilience. Ultimately, a means to optimize network resilience strategies is discussed, primarily through an adaption of the Copeland Score for nonparametric stochastic ranking. The measures of resilience and optimization techniques are applied to inland waterway networks, an important mode in the larger multimodal transportation network upon which we rely for the flow of commodities. We provide a case study analyzing and planning for the resilience of commodity flows along the Mississippi River Navigation System to illustrate the usefulness of the proposed metrics.

Research paper thumbnail of Proportional hazards models of infrastructure system recovery

Reliability Engineering & System Safety, 2014

Research paper thumbnail of Resilience-based network component importance measures

Reliability Engineering & System Safety, 2013

Research paper thumbnail of Multiobjective Stochastic Inoperability Decision Tree for Infrastructure Preparedness

Journal of Infrastructure Systems, 2013

Research paper thumbnail of Proportional hazards models of infrastructure system recovery

Reliability Engineering & System Safety, 2014

Research paper thumbnail of Decision Trees with Single and Multiple Interval-Valued Objectives

Research paper thumbnail of Improved Acquisition for System Sustainment: Multiobjective Tradeoff Analysis for Condition-Based Decision-Making

Research paper thumbnail of Interval-valued availability framework for supplier selection based on component importance

International Journal of Production Research, 2015

Research paper thumbnail of Modeling infrastructure resilience using Bayesian networks: A case study of inland waterway ports

Computers & Industrial Engineering, 2016

Research paper thumbnail of Removing hazardous products from the food chain

Research paper thumbnail of Modeling a severe supply chain disruption and post-disaster decision making with application to the Japanese earthquake and tsunami

Http Dx Doi Org 10 1080 0740817x 2013 876241, Aug 15, 2014

ABSTRACT Modern supply chains are increasingly vulnerable to disruptions, and a disruption in one... more ABSTRACT Modern supply chains are increasingly vulnerable to disruptions, and a disruption in one part of the world can cause supply difficulties for companies around the globe. This article develops a model of severe supply chain disruptions in which several suppliers suffer from disabled production facilities and firms that purchase goods from those suppliers may consequently suffer a supply shortage. Suppliers and firms can choose disruption management strategies to maintain operations. A supplier with a disabled facility may choose to move production to an alternate facility, and a firm encountering a supply shortage may be able to use inventory or buy supplies from an alternate supplier. The supplier’s and firm’s optimal decisions are expressed in terms of model parameters such as the cost of each strategy, the chances of losing business, and the probability of facilities reopening. The model is applied to a simulation based on the 2011 Japanese earthquake and tsunami, which closed several facilities of key suppliers in the automobile industry and caused supply difficulties for both Japanese and U.S. automakers.

Research paper thumbnail of Bayesian Kernel Methods for Non-Gaussian Distributions: Binary and Multi-class Classification Problems

The public reporting burden for this collection of information is estimated to average 1 hour per... more The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information.

Research paper thumbnail of Quantifying the risk of project delays with a genetic algorithm

International Journal of Production Economics, 2015

Research paper thumbnail of Bayesian Kernel Methods for Critical Infrastructure Resilience Modeling

Vulnerability, Uncertainty, and Risk, 2014

Research paper thumbnail of Analyzing interdependent impacts of resource sustainability

Environment Systems and Decisions, 2013

ABSTRACT

Research paper thumbnail of A Bayesian beta kernel model for binary classification and online learning problems

Statistical Analysis and Data Mining, 2014

ABSTRACT Recent advances in data mining have integrated kernel functions with Bayesian probabilis... more ABSTRACT Recent advances in data mining have integrated kernel functions with Bayesian probabilistic analysis of Gaussian distributions. These machine-learning approaches can incorporate prior information with new data to calculate probabilistic rather than deterministic values for unknown parameters. This article extensively analyzes a specific Bayesian kernel model that uses a kernel function to calculate a posterior beta distribution that is conjugate to the prior beta distribution. Numerical testing of the beta kernel model on several benchmark datasets reveals that this model's accuracy is comparable with those of the support vector machine (SVM), relevance vector machine, naive Bayes, and logistic regression, and the model runs more quickly than all the other algorithms except for logistic regression. When one class occurs much more frequently than the other class, the beta kernel model often outperforms other strategies to handle imbalanced datasets, including under-sampling, over-sampling, and the Synthetic Minority Over-Sampling Technique. If data arrive sequentially over time, the beta kernel model easily and quickly updates the probability distribution, and this model is more accurate than an incremental SVM algorithm for online learning.

Research paper thumbnail of Importance measures for inland waterway network resilience

Transportation Research Part E: Logistics and Transportation Review, 2014

Research paper thumbnail of Integrating simulation and risk-based sensitivity analysis methods in hospital emergency department design

International Series in Operations Research & Management Science, 2012

Research paper thumbnail of Measures of Inland Waterway Network Resilience

INCOSE International Symposium, 2013

Research paper thumbnail of Sensitivity analysis for simulation-based decision making: Application to a hospital emergency service design

Simulation Modelling Practice and Theory, 2012

An increasing concern of decision makers when dealing with system design is preparation for a wid... more An increasing concern of decision makers when dealing with system design is preparation for a wide range of potentially uncertain operating conditions. This paper provides a novel multiobjective approach for simulation-driven decision making that accounts for not only ...

Research paper thumbnail of Stochastic Measures of Network Resilience: Applications to Waterway Commodity Flows

Risk Analysis, 2014

Given the ubiquitous nature of infrastructure networks in today&a... more Given the ubiquitous nature of infrastructure networks in today's society, there is a global need to understand, quantify, and plan for the resilience of these networks to disruptions. This work defines network resilience along dimensions of reliability, vulnerability, survivability, and recoverability, and quantifies network resilience as a function of component and network performance. The treatment of vulnerability and recoverability as random variables leads to stochastic measures of resilience, including time to total system restoration, time to full system service resilience, and time to a specific α% resilience. Ultimately, a means to optimize network resilience strategies is discussed, primarily through an adaption of the Copeland Score for nonparametric stochastic ranking. The measures of resilience and optimization techniques are applied to inland waterway networks, an important mode in the larger multimodal transportation network upon which we rely for the flow of commodities. We provide a case study analyzing and planning for the resilience of commodity flows along the Mississippi River Navigation System to illustrate the usefulness of the proposed metrics.

Research paper thumbnail of Proportional hazards models of infrastructure system recovery

Reliability Engineering & System Safety, 2014

Research paper thumbnail of Resilience-based network component importance measures

Reliability Engineering & System Safety, 2013