Simulation modeling framework for uncovering system behaviors in the biofuels supply chain network. (September 2014)

• Record Type: Journal Article
• Authors: Agusdinata, Datu B; Lee, Seokcheon; Zhao, Fu; Thissen, Wil
• Is Part Of: Simulation. Volume 90:Number 9(2014:Sep.)
• Page Start: 1103
• Page End: 1116
• Publication Date: 2014-09
• Subjects: Distributed artificial intelligence -- agent-based simulation model -- biofuels supply chain network; Computer simulation -- Periodicals; 003.3
• DOI: 10.1177/0037549714544081
• Languages: English
• ISSNs: 0037-5497

Abstract

A full realization of alternative energy such as biofuels depends on the existence of a viable supply chain (SC) network. An agent-based simulation approach is pursued to understand the dynamics of the biofuels SC network. The interests of three SC actors are represented: users, biorefineries, and farmers. Each actor type has a binary decision option: adoption or non-adoption of biofuels. This SC network model is characterized by distributed control, time asynchrony, and resource contention among actors, who make decisions based on incomplete knowledge and delayed information. The decision dynamics of these actors are modeled using a computational ecosystem construct. A preliminary set of coupled payoff function for each actor type and each decision is developed to represent interdependencies among SC actors. The simulation model was used to evaluate three archetypes of subsidy policy. The SC network behavior is observed in terms of fraction of actors adopting the biofuel option. The SC network shows behaviors ranging from fixed point equilibrium under no delay and perfect knowledge to periodic and chaotic oscillations. The network behavior is very sensitive to the time delay parameter that partly influences the quality of information on which actors' decisions are based. Several regions of SC behavior are identified. In particular, a chaotic behavior was observed. The work provides a methodological basis for further development, including identification of policies to control undesirable system behaviors.