Dynamic Parameter Design by Ant Colony Optimization and Neural Networks

Chang, Hsu-Hwa; Chen, Yan Kwang; Chen, Mu-Chen

doi:10.1142/s0217595907001280

Cited by 5 publications

(2 citation statements)

References 20 publications

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“…Assuming there was no intercept in the linear ideal function, there came to yij=βMj, where Mj denoted the j th signal factors level and β was the slope. To evaluate the performance in the dynamic EMS systems using the Taguchi method, the equation SN=10log10false(β/MSEfalse) was applied, in which MSE was the mean square error of the distance from the measured response to the best-fitted line [50,51].…”

Section: The Dynamic Taguchi Methods and Neural Network For The Optmentioning

confidence: 99%

The Application of GSCM in Eliminating Healthcare Waste: Hospital EDC as an Example

Chang

Wang

Liao

et al. 2019

IJERPH

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Eliminating unnecessary healthcare waste in hospitals and providing better healthcare quality are the core issues of green supply chain management (GSCM). Hence, this study used a hospital’s emergency department crowding (EDC) problem to illustrate how to establish an emergency medicine service (EMS) simulation system to obtain a robust parameters setting for solving hospitals’ EDC and waste problems, thereby increasing healthcare quality. Inappropriate resource allocation results in more serious EDC; more serious EDC results in increasing operating costs. Therefore, in the healthcare system, waste includes inappropriate costs and inappropriate resource allocation. The EMS of a medical center in central Taiwan was the object of the study. In this study, the dynamic Taguchi method was used to set the signal factor, noise factor, and control factors to simulate the EMS system to obtain the optimal parameters setting. The performance was set to Emergency Department Work Index (EDWINC) and system time (waiting time and service time) per patient. The signal factor was set to the number of physicians; the noise factor was set to patient arrival rate; the control factors included persuading Triage 4 and Triage 5 outpatients, checkup process, bed occupation rate in the emergency department (ED), and medical checkup sequence for Triage 4 and Triage 5 patients. This study makes two significant contributions. First, the study introduces the GSCM concept to the healthcare setting to bring green innovation to hospitals. Hospital administrators may hence design better GSCM activities to facilitate healthcare processes to provide better healthcare outcomes. Second, the study applied the dynamic Taguchi method to the EMS and neural network (NN) to construct a computational model revealing the cause (factors) and effect (performances) relationship. In addition, the genetic algorithm (GA), a solution method, was used to obtain the optimal parameters setting of the EDC in Taiwan. Hence, after obtaining the solutions, the unnecessary waste in EDC—inappropriate costs and inappropriate resource allocation—is reduced.

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Section: The Dynamic Taguchi Methods and Neural Network For The Optmentioning

confidence: 99%

The Application of GSCM in Eliminating Healthcare Waste: Hospital EDC as an Example

Chang

Wang

Liao

et al. 2019

IJERPH

View full text Add to dashboard Cite

show abstract

“…To evaluate a dynamic Taguchi system's performance, the formula SN = 10 log 10 (b/MSE) is used. Here, MSE represents the mean square error of the distance between the measured response and best fitted line (Chang, Chen, & Chen, 2007).…”

Section: Dynamic Taguchi Methodsmentioning

confidence: 99%