An Interactive Two-Stage Framework for Simultaneous Machine Selection and Buffer Allocation

In manufacturing, machine selection and buffer allocation are extremely important and widely studied problems, as both can significantly affect the system performance. Solving either problem alone in a deterministic setting is already challenging. In this study, however, we simultaneously solve a high dimensional machine selection and buffer allocation problem in a highly stochastic and complex production line environment. The objective is to simultaneously select the optimal combination of machine type and buffer size allocation to maximize production throughput under limited cost and total buffer size constraints. We develop an efficient, interactive two-stage optimization method, called Machine Selection and Buffer Allocation algorithm (MSBA), that consists of a rapid machine selection (RMS) procedure and an adaptive global particle and local hyperbox search (AGPLHS) algorithm to solve the problem via simulation optimization. MSBA achieves seamless integration of RMS and AGPLHS so as to enable the simultaneous machine selection and buffer allocation to be solved efficiently. Furthermore, we compare our overall framework with three common existing methods (Genetic Algorithm, Nelder-Mead Algorithm and Adaptive Tabu Search). It is shown that our interactive two-stage framework outperforms the competing algorithms both in terms of effectiveness and efficiency. Note to Practitioners—Two important problems to solve in many production processes such as semiconductor manufacturing are machine selection and buffer allocation. Various methodologies have been utilized to solve each of the two problems individually. This work proposes an interactive two-stage simulation optimization algorithm which simultaneously solves both problems in complex, high dimensional and profoundly stochastic environments. While the most basic problem setting is a serial multi-workstation environment, complexities such as re-work stations, merging, splitting, parallel machine, and multi-product processor can be incorporated into the simulation model and solved using our proposed methodology. The methodology is user-friendly and intuitive yet adaptable enough to handle a wide range of production line environments or solve other stochastic optimization problems which require the simultaneous optimization of a mixture of binary and integer-based solutions. The numerical study, consisting of two different job-shop environments and including three benchmark algorithms, provides evidence for the efficiency of the proposed algorithm. Sensitivity analysis on user-defined parameters is also provided for the benefit of practitioners.