Kahjuks pole selle sisu tõlget valitud keeles. Andmed kuvatakse inglise keeles.

Using Monte Carlo simulation to refine emergency logistics response models: a case study.

Authors
Ruth Banomyong, Apichat Sopadang

Purpose – The purpose of this paper is to provide a framework for the development of emergency logistics response models. The proposition of a conceptual framework is in itself not sufficient and simulation models are further needed in order to help emergency logistics decision makers in refining their preparedness planning process. Design/methodology/approach – The paper presents a framework proposition with illustrative case study. Findings – The use of simulation modelling can help enhance the reliability and validity of developed emergency response model. Research limitations/implications – The emergency response model outcomes are still based on simulated outputs and would still need to be validated in a real-life environment. Proposing a new or revised emergency logistics response model is not sufficient. Developed logistics response models need to be further validated and simulation modelling can help enhance validity. Practical implications – Emergency logistics decision makers can make better informed decisions based on simulation model output and can further refine their decision-making capability. Originality/value – The paper posits the contribution of simulation modelling as part of the framework for developing and refining emergency logistics response.

Codebooks
SLR Criteria
Summary

Simulation of environment with Arena. ( experimental model by placing modules that represent processes or logic.

SLR Criteria
Summary

Simulation

Summary

Response times for certain activitiesEx. Clearance activity, vehicle speed, aid distribution in prone area, …

Summary

The probability distribution for the Monte Carlo simulation was based on a triangular distribution. “Fuzzy” information had to be transformed into a triangular distribution. Conventional quantitative transformation techniques are not well suited for dealing with decision problems involving fuzziness

SLR Criteria
Summary

Simulation

SLR Criteria
Summary

Provide a framework for the development of emergency logistics response models

Summary

The emergency response model outcomes are still based on simulated outputs and would still need to be validated in a real-life environment. Proposing a new or revised emergency logistics response model is not sufficient.

SLR Criteria
Summary

Emergency logistics decision makers can make better informed decisions based on simulation model output and can further refine their decision-making capability

SLR Criteria
Summary

Monte Carlo simulation is a method that evaluates iteratively a deterministic model using sets of random numbers as inputs.Response simulation for the Thailand tsunami in 2004

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