Page 324 - Biofuels for a More Sustainable Future

286 Biofuels for a More Sustainable Future

• Logistic management refers to managing and implementing the sufficient
and effective flows of materials (e.g., raw materials, intermediates, and
products), goods, and information from original suppliers to end users
(Bowersox, 1997; Lambert and Cooper, 2000; Sokhansanj et al., 2006).
• Fleet management decides the movements of materials between different
BSC stages (Awudu and Zhang, 2012). Fleet management plays a crucial
role in BSC as it directly affects the robustness of the transportation
network (Ravula et al., 2008; Eriksson and Bj€orheden, 1989; Van
Wassenhove and Pedraza Martinez, 2012; Thomas and Griffin, 1996).
Depending on the predetermined strategic decisions and the objectives of
BSC, different tactical and operational decisions mentioned were considered
in previous BSC cases (see Table 10.2). Zhang and Hu (2013) built two
models to optimize the strategic decisions of facility locations and capacity,
then tactical and operational decisions such as monthly biorefinery produc-
tion planning and inventory control were investigated and determined.
Some studies developed optimization approaches to make strategic and tac-
tical decisions simultaneously. For example, Lin et al. (2014) established a
model to optimize the large-scale biomass-to-ethanol SC where the strategic
(e.g., farm and facility locations and capacities) and tactical decisions
(e.g., biomass production planning, plant operating schedules, and inventory
control) were optimized simultaneously. An et al. (2011b) established a
model considering multiple types of lignocellulosic biomass and the material
flows in the BSC. This model could be used for both strategic and tactical
decisions including facility locations and capacities, technology types, pro-
g
duction plans, transportation strategies, and storage amount. Ekşio luetal.
(2009) integrated the long-term decisions (e.g., capacity, location, and the
number of biorefineries) and mid-term logistic decisions (e.g., biomass
supply) (Ekşio glu et al., 2009). As a BSC usually has a large number of com-
ponents, determining tactical and operational decisions without considering
the uncertainty related to each component may lead to poor performance of
the whole SC (Awudu and Zhang, 2012). Different approaches (e.g., sto-
chastic programming and fuzzy logic) have been developed to model and
address uncertainties in BSC design, which are further discussed in the
following sections.

4 Modeling approaches for BSC design
Three levels of decisions discussed previously are commonly modeled as deci-
sion variables; effective modeling techniques to identify optimal solutions of