Hybrid Simulation Modeling for Humanitarian Relief Chain Coordination
Abstract
**Please note that the full text is embargoed** 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. [This is an accepted version of an article published by Emerald in "Journal of Humanitarian Logistics and Supply Chain Management" on 7 December 2015, available online: https://www.emerald.com/insight/content/doi/10.1108/JHLSCM-07-2015-0033/full/html]