The allocation of buffer space in flow lines with stochastic processing times
is an important decision, as buffer capacities influence the performance of these lines.
The objective of this problem is to minimize the overall number of buffer spaces
achieving at least one given goal production rate. We optimally solve this problem
with a mixed-integer programming approach by sampling the effective processing
times. To obtain robust results, large sample sizes are required. These incur large
models and long computation times using standard solvers. This paper presents a
Benders Decomposition approach in combination with initial bounds and different
feasibilitycutsfortheBufferAllocationProblem,whichprovidesexactsolutionswhile
reducing the computation times substantially. Numerical experiments are carried out
to demonstrate the performance and the flexibility of the proposed approaches. The
numerical study reveals that the algorithm is capable to solve long lines with reliable
and unreliable machines, including arbitrary distributions as well as correlations of
processing times
