Optimal outpatient appointment system with uncertain parameters using adaptive-penalty genetic algorithm
The optimal number of doctors and appointment interval for an outpatient appointment system in a class of individual block/fixed interval are determined using an adaptive-penalty Genetic Algorithm. The length of service time for doctor consultation, the time required for the laboratory tests, and th...
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th-cmuir.6653943832-543282018-09-04T10:12:03Z Optimal outpatient appointment system with uncertain parameters using adaptive-penalty genetic algorithm Napat Harnpornchai Kittawit Autchariyapanitkul Jirakom Sirisrisakulchai Songsak Sriboonchitta Computer Science The optimal number of doctors and appointment interval for an outpatient appointment system in a class of individual block/fixed interval are determined using an adaptive-penalty Genetic Algorithm. The length of service time for doctor consultation, the time required for the laboratory tests, and the time deviating from the appointment time are modelled by random variables. No-show patients are also included in the system. Using the adaptive penalty scheme, optimization constraints are automatically and numerically handled. The solution methodology is readily applicable to other appointment systems. The study has a significant implication from the viewpoint of economic and risk management of health care service. 2018-09-04T10:12:03Z 2018-09-04T10:12:03Z 2015-09-01 Journal 18838014 13430130 2-s2.0-84943376386 10.20965/jaciii.2015.p0585 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84943376386&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/54328 |
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Computer Science Napat Harnpornchai Kittawit Autchariyapanitkul Jirakom Sirisrisakulchai Songsak Sriboonchitta Optimal outpatient appointment system with uncertain parameters using adaptive-penalty genetic algorithm |
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The optimal number of doctors and appointment interval for an outpatient appointment system in a class of individual block/fixed interval are determined using an adaptive-penalty Genetic Algorithm. The length of service time for doctor consultation, the time required for the laboratory tests, and the time deviating from the appointment time are modelled by random variables. No-show patients are also included in the system. Using the adaptive penalty scheme, optimization constraints are automatically and numerically handled. The solution methodology is readily applicable to other appointment systems. The study has a significant implication from the viewpoint of economic and risk management of health care service. |
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Napat Harnpornchai Kittawit Autchariyapanitkul Jirakom Sirisrisakulchai Songsak Sriboonchitta |
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Napat Harnpornchai Kittawit Autchariyapanitkul Jirakom Sirisrisakulchai Songsak Sriboonchitta |
author_sort |
Napat Harnpornchai |
title |
Optimal outpatient appointment system with uncertain parameters using adaptive-penalty genetic algorithm |
title_short |
Optimal outpatient appointment system with uncertain parameters using adaptive-penalty genetic algorithm |
title_full |
Optimal outpatient appointment system with uncertain parameters using adaptive-penalty genetic algorithm |
title_fullStr |
Optimal outpatient appointment system with uncertain parameters using adaptive-penalty genetic algorithm |
title_full_unstemmed |
Optimal outpatient appointment system with uncertain parameters using adaptive-penalty genetic algorithm |
title_sort |
optimal outpatient appointment system with uncertain parameters using adaptive-penalty genetic algorithm |
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2018 |
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https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84943376386&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/54328 |
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