Tagungsband zum Doctoral Consortium der WI 2009

stages. The objective function sums up the total cost incurred for production of a certain set of
products � � with expected demand volumes � ��. The single constituents are
(1) Setup costs, calculated via the average setup cost for each item for the selected
planning buffer and the number of planned production lots
(2) Scrap costs, calculated via the scrap cost rates and the number of production lots
(3) Cycle stock holding costs, calculated via the inventory holding cost rates and the
cycle inventory that results from the decisions about production lots and the demand
quantities
(4) Cost for safety stock incurred by increased planning buffers, either on � �� if �� � �
1 or on a set of downstream stockpoints if �� � �0.
With this objective function, two major subproblems can be identified: First, the setup costs depend
on the choice of the length of the planning buffer. Therefore, we need an estimator function that
determines the average setup cost rate for each product and possible planning buffer. Second, the
costs for safety stock incurred by increasing planning buffers are not readily available. Therefore,
we have to calculate planning buffer penalty costs factors for each product to penalise each increase
of the planning buffer according to the additional safety stock cost it incurs.
Further details on the production parameter optimisation subproblem are beyond the scope of this
paper. For a more detailed description of the optimisation model and solution to some the above
mentioned subproblems regarding the estimation of model parameters, the reader is referred to [3].
3. Implementation status and outlook
WI Doctoral Consortium 2009 33
The entire analysis method is being implemented as a decision support tool that enables the
assessment of large production and distribution networks. Currently, there exists a prototype
version called Complana (COMplexity ANAlyser), which implements the full production and
distribution network model and provides interfaces to the standard ERP system SAP to
automatically generate the network models from existing data to alleviate the effort for model
building. This is of special importance as the dissertation project is carried out in cooperation with
Freudenberg Household Products KG, an international household product manufacturer, whose
production and distribution network for the product category of cloths serves as an example
scenario that is to be analysed for various alternative scenarios with respect to assortment
reductions. The software allows generating and storing models from the imported data which then
serve as baseline models for the comparisons. Assortment scenarios can then be defined and
applied to these models to yield new model instances with the assortment changes incorporated.
Comparisons are then carried out with one baseline model and at least one assortment scenario
model.
The implementation is done almost entirely in Java, making use of existing frameworks like
Hibernate for data persistence as well as OpenTS as the framework for the tabu search procedure.
The main application is developed as a Rich Client Application on the basis of the Eclipse Rich
Client Platform (RCP) to provide a robust user interface. The optimisation model for production
planning parameters is defined using the MathProg modelling language and thus can be solved with
any standard solver able to process this language. Currently, the model is being tested with the
freely available GNU Linear Programming Kit (GLPK) as well as the commercial CPLEX solver.
Currently, the main tasks are the improvement of the interoperation between the single modules, as
well as the development of appropriate presentation and analysis capabilities for the results.
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