Optimization of buffer allocations in stochastic flow lines
Date of defense:
October 2, 2015
The allocation of buffers in a flow line with stochastic processing times has an important impact on the system's performance. Because stochastic processing times may lead to blocking or starvation, an efficient allocation is essential. My Ph.D. thesis deals with a mixed-integer programming approach to optimize the buffer allocation in flow lines with stochastic processing times. The objective is to minimize the overall number of buffer spaces reaching a goal production rate. To solve this model, the stochastic processing times are replaced by samples. The selection of the sampling method has a great influence on the sample size and, therefore, on the robustness and the computation time. Hence, several numerical studies are carried out to compare the different approaches. As the mixed-integer program is hard to solve, new solution approaches are developed to exploit the special structure of the model. Moreover, in many approaches, real-world features such as limited supply are not considered. The model considers this limited supply applying order policies for the replenishment of the first machine in the line. A numerical study evaluates the impact of the supply on the optimal buffer allocation.