Technical parameters for the rail operations planning example

For simplicity it is assumed that the chemicals consumption/production rates coits o,re constant over the planning horizon. Table 3.16 displays the assumed consumption/production rates uJis as well as stock capacities initial stocks and target stock levels 

Solving this instance of the MC-RTP leads to the optimal solution after 8 seconds with an objective value of 20,8j5. Note that network design problems are MV - hard prob-lems. However, the computational complexity depends on the relation of the parts of the objective function and the size of the network (particularly the number of links). Here, a comparatively small problem instance with a favourable ratio of costs is given which eases solving to optimality. 

In reality, the production networks typically consist of more than three sites whereby a time horizon of one or two weeks is reasonable in most cases. The time horizon depends on the forecasting stability of production/consumption estimates as well as the transport times. Hence, the model s complexity increases with increasing numbers of nodes and periods. In cases of very large instances, a heuristical procedure can be. set up like this  

Build a relaxed MC-RTP by replacing the train cost part T,ttT Vijt cjj in the 

4- Solve the MC-RTP instance with fixed chemical flows. 

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Table 3.15 Technical parameters for the rail operations planning example...

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