Abstract
The overall focus of this research is to demonstrate the savings potential
generated by the integration of the design of strategic global supply
chain networks with the determination of tactical production–distribution
allocations and transfer prices. The logistics systems design problem
is defined as follows: given a set of potential suppliers, potential
manufacturing facilities, and distribution centers with multiple
possible configurations, and customers with deterministic demands,
determine the configuration of the production–distribution system
and the transfer prices between various subsidiaries of the corporation
such that seasonal customer demands and service requirements are
met and the after tax profit of the corporation is maximized. The
after tax profit is the difference between the sales revenue minus
the total system cost and taxes. The total cost is defined as the
sum of supply, production, transportation, inventory, and facility
costs. Two models and their associated solution algorithms will be
introduced. The savings opportunities created by designing the system
with a methodology that integrates strategic and tactical decisions
rather than in a hierarchical fashion are demonstrated with two case
studies.
The first model focuses on the setting of transfer prices in a global
supply chain with the objective of maximizing the after tax profit
of an international corporation. The constraints mandated by the
national taxing authorities create a bilinear programming formulation.
We will describe a very efficient heuristic iterative solution algorithm,
which alternates between the optimization of the transfer prices
and the material flows. Performance and bounds for the heuristic
algorithms will be discussed.
The second model focuses on the production and distribution allocation
in a single country system, when the customers have seasonal demands.
This model also needs to be solved as a subproblem in the heuristic
solution of the global transfer price model. The research develops
an integrated design methodology based on primal decomposition methods
for the mixed integer programming formulation. The primal decomposition
allows a natural split of the production and transportation decisions
and the research identifies the necessary information flows between
the subsystems. The primal decomposition method also allows a very
efficient solution algorithm for this general class of large mixed
integer programming models. Data requirements and solution times
will be discussed for a real life case study in the packaging industry.
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