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Key issue, challenges, and status quo of models for biofuel supply chain design 299

provided information of biomass availabilities that were used as biomass
resource constraints in optimization models (H€ohn et al., 2014; Parker
et al., 2010).
A few studies used GIS alone as a decision supporting tool for the BSC
design. Beccali et al. (2009) used GIS to identify the most exploitable
biomass resources in Sicily by analyzing regional economic, agriculture,
climate, and infrastructure data. Thomas et al. (2013) employed GIS to
assess the spatial supply and demand balance in England at a national scale.

4.2 Simulation-based BSC models
SC simulation is a basic method for SC prediction, management, and
improvement (Zhao et al., 2011). Simulation models can support BSC
design by employing different performance indicators such as available
regional forest fuel potential (NAP), GHG emissions, and SC energy con-
sumption (Zhang et al., 2012; Gronalt and Rauch, 2007). Unlike optimi-
zation that determines the optimal values of a set of decision variables,
simulation is to model the presence of a system in order to predict the
behavior of the system under a given set of conditions (Wurbs, 1993). In
some cases, simulation can work as a means of BSC optimization by con-
ducting a large number of BSC design scenarios (Agusdinata et al., 2014).
Compared to BSC optimization models, simulation models can provide
a better understanding of the impacts of specific design parameters and strat-
egies. For example, Gronalt and Rauch (2007) proposed a scenario evalu-
ation model to study the regional forest BSC in Austria, where different
scenario parameters such as varied transportation distances and varied
demands were evaluated by simulating a number of system configurations.
Zhang et al. (2012) established a simulation model of converting low-value
pulpwood into biofuels and evaluated the impacts of spring break-up on
delivered biomass cost, GHG emissions, and energy consumption. They also
included other varied design parameters such as cost coefficients, energy
coefficients (Btu/ton-mile), biofuel facility locations, and size options. By
changing these parameters, a case study was conducted in the lower penin-
sula of Michigan and showed that simulation model was useful in BSC man-
agement, the selection of facility mode, logistic design, inventory
management, and information exchange (Zhang et al., 2012). Sokhansanj
et al. (2006) simulated the dynamic biomass logistics to predict the transpor-
tation cost between operations such as collection, storage, and biorefinery to