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Hybrid simulation modeling for humanitarian relief chain coordination

Hybrid simulation modeling for humanitarian relief chain coordination Purpose – The purpose of this paper is to present a conceptual framework for a hybrid simulation model that can be used to study the decision making and behaviors of humanitarian logistics actors to determine how/whether certain coordination mechanisms enable better relief chain efficiency and effectiveness over time. Design/methodology/approach – The agent-based portion of the model is used to represent human decision making and interactions in a more realistic way than has been done previously, and the discrete-event simulation (DES) portion of the model allows the movement of vehicles, materials, and information throughout a supply network to be represented in a way that allows for dynamic and stochastic behavior. Findings – Coordinated efforts by actors in humanitarian logistics operations involve complex interactions and adaptations over time, which can be capture and explored via hybrid agent-based model (ABM)-DES modeling. Research limitations/implications – This paper describes a framework for a hybrid ABM-DES model. The actual development and implementation of the model, including input data collection and analysis, model development, experimentation, and output data collection and analysis, will be the subject of future work. Practical implications – The hybrid model framework provides other researchers with a starting point for model development. Social implications – This paper provides a basis for future modeling and assessment of coordination in humanitarian logistics, an area that is in need of research. Originality/value – The hybrid simulation modeling framework presented in this paper is a novel application of a new modeling methodology to the problem of coordination in humanitarian logistics. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Humanitarian Logistics and Supply Chain Management Emerald Publishing

Hybrid simulation modeling for humanitarian relief chain coordination

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
2042-6747
DOI
10.1108/JHLSCM-07-2015-0033
Publisher site
See Article on Publisher Site

Abstract

Purpose – The purpose of this paper is to present a conceptual framework for a hybrid simulation model that can be used to study the decision making and behaviors of humanitarian logistics actors to determine how/whether certain coordination mechanisms enable better relief chain efficiency and effectiveness over time. Design/methodology/approach – The agent-based portion of the model is used to represent human decision making and interactions in a more realistic way than has been done previously, and the discrete-event simulation (DES) portion of the model allows the movement of vehicles, materials, and information throughout a supply network to be represented in a way that allows for dynamic and stochastic behavior. Findings – Coordinated efforts by actors in humanitarian logistics operations involve complex interactions and adaptations over time, which can be capture and explored via hybrid agent-based model (ABM)-DES modeling. Research limitations/implications – This paper describes a framework for a hybrid ABM-DES model. The actual development and implementation of the model, including input data collection and analysis, model development, experimentation, and output data collection and analysis, will be the subject of future work. Practical implications – The hybrid model framework provides other researchers with a starting point for model development. Social implications – This paper provides a basis for future modeling and assessment of coordination in humanitarian logistics, an area that is in need of research. Originality/value – The hybrid simulation modeling framework presented in this paper is a novel application of a new modeling methodology to the problem of coordination in humanitarian logistics.

Journal

Journal of Humanitarian Logistics and Supply Chain ManagementEmerald Publishing

Published: Dec 7, 2015

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