Using Monte Carlo simulation to refine emergency logistics response models: a case study.
Purpose – The purpose of this paper is to provide a framework for the development of logistics models. The proposition of a conceptual framework is in itself not sufficient and simulation models are further needed in order to help logistics decision makers in refining their 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 model. Research limitations/implications – The emergency response model outcomes are still based on simulated outputs and would still to be validated in a real-life environment. Proposing a new or revised emergency logistics response model is not sufficient. Developed logistics response models 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 . Originality/value – The paper posits the contribution of simulation modelling as part of the framework for developing and refining emergency logistics response.
Simulation of environment with Arena. ( experimental model by placing modules that represent processes or logic.
Simulation
times for certain activitiesEx. Clearance activity, vehicle speed, aid distribution in prone area, …
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
Simulation
Provide a framework for the development of logistics models
The model outcomes are still based on simulated outputs and would still to be validated in a real-life environment. Proposing a new or revised logistics model is not sufficient.
logistics decision makers can make better informed decisions based on simulation model output and can further refine their decision-making
Monte Carlo simulation is a method that evaluates iteratively a deterministic model using sets of random numbers as inputs. simulation for the Thailand tsunami in 2004
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