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a network configuration such that the solution values do not substantially vary over
different scenarios. Several authors also included stochastic problem characteristics
in a multi-period setting such as Aghezzaf (
2005
), Pan and Nagi (
2010
), and Nickel
et al. (
2012
).
A further relevant aspect in strategic network design is the integration of location
decisions with inventory management. Demand uncertainty and risk pooling play
an important role in this context. Inventory decisions concern working inventories
at storage locations (i.e., the amounts of products that have been ordered from sup-
pliers but not yet requested by customers) and safety stocks. The latter are intended
as a buffer against stockouts during ordering lead times. Shen (
2005
), Ozsen et al.
(
2009
), and Shu (
2010
) study the trade-off between inventory, transportation, and
fixed costs to locate warehouses and allocate customers. Combining inventory man-
agement and location decisions into a single model often results in mixed-integer
non-linear programming formulations that can only be solved for small problem
instances. Recently, Tancrez et al. (
2012
) developed a heuristic procedure that is able
to solve large-scale multi-echelon location-inventory problems comprising plants,
distribution centers, and customers.
Finally, the growth in globalization has led to the emergence of global supply
chains, that is, worldwide networks of suppliers, manufacturers, distribution cen-
ters, and retailers. Consequently, the integration of financial considerations with
location and logistics decisions has gained increasing importance in network design.
Financial factors comprise, among others, taxes, duties, tariffs, exchange rates, and
transfer prices. Meixell and Gargeya (
2005
) discuss various contributions in this
area while Wilhelm et al. (
2005
) propose a comprehensive model for the design of
a logistics network under the North American Free Trade Agreement (NAFTA).
16.3
A General Reverse Logistics Network Design Model
Reverse logistics refers to all operations involved in the return of products and
materials from a point of use to a point of recovery or proper disposal. The purpose
of recovery is to recapture value through options such as reusing, repairing, refur-
bishing, remanufacturing, and recycling. Reverse logistics includes the management
of the return of end-of-use or end-of-life products as well as defective and damaged
items, or packaging materials, containers, and pallets.
Major driving forces behind reverse logistics activities include economical
factors, legislations, and environmental consciousness. As stated by De Brito and
Dekker (
2004
), companies become active in reverse logistics because they can make
a profit and/or because they are forced to focus on such functions, and/or because
they feel socially motivated. These factors are usually intertwined. For example, a
company can be compelled to reuse a certain percentage of components in order to
achieve a recovery target set by the legislation. This will lead to a decrease in the
cost of purchasing components and in waste generation. Jayaraman and Luo (
2007
)
suggest that proper management of reverse logistics operations can lead to greater
