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300 Biofuels for a More Sustainable Future
understand the impacts of biomass availability, moisture content, weather
factors, transportation equipment performance, and dry matter loss on the
transportation cost.
A powerful simulation tool for BSC design emerged in recent decades is
agent-based modeling (ABM). ABM is capable of modeling a system with
individuals who have autonomous decision-making abilities (Bonabeau,
2002). Wooldridge (1997) and Jennings (2000) defined ABM as, “an agent
is an encapsulated computer system that is situated in some environment and
that is capable of flexible, autonomous action in that environment in order to
meet its design objectives” ( Jennings, 2000). ABM has been largely used in
SC management and design (Zhao et al., 2011; Giannakis and Louis, 2011;
Nissen, 2001; Lou et al., 2004; Kaihara, 2003; Julka et al., 2002a; Garcia-
Flores et al., 2000; Gjerdrum et al., 2001). For example, Zhao et al.
(2011) established an ABM model for multistage SC where SC members
(e.g., order agent, inventory agent, and distribution agent) were operating
autonomy cooperations with each other. The information flow, product
flow, and SC member relationship were modeled to observe the dynamics
of the SC system. The simulation results were helpful in understanding the
impacts of individual members’ behavior and organization strategies on the
SC. Julka et al. (2002b) applied the ABM technique in traditional refinery
SC to support decision-making of refinery manager. Five departments in the
refinery were modeled as agents: procurement, sales, operations, storage,
and logistics. “What-if” scenarios were developed to understand the behav-
ior rules for each department. Another advantage of ABM in modeling SC is
the capability of modeling emergent phenomena under extreme or disrup-
tive events, which can enhance risk management of SC. Giannakis and Louis
(2011) investigated the risk management of SC and developed a multiagent
model to simulate the operational level SC. Five different agents played var-
ied roles in information integration, coordination, monitoring, and risk
management.
In recent decades, ABM has been applied to BSC design to analyze dif-
ferent policy options, design strategies, as well as many “what-if” scenarios.
Moncada et al. (2017) developed an agent-based model to study the impacts
of agriculture and bioenergy policy in Germany, and they investigated the
BSC design strategies such as liberalization of the farmer EU agricultural
market, energy tax act, and biofuel quota act. Beck et al. (2008) established
a model combining ABM and optimization to study the bioenergy network
in South Africa with different policies to understand the trade-offs among
economic, social, and environmental aspects. Shastri et al. (2011) applied