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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
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