The buffer allocation problem (BAP) aims to determine the optimal buffer configuration for a production line under the predefined constraints. The BAP is an NP-hard combinatorial optimization problem and the solution space exponentially grows as the problem size increases. Therefore, problem specific heuristic or meta-heuristic search algorithms are widely used to solve the BAP. In this study two population-based search algorithms; i.e. Combat Genetic Algorithm (CGA) and Big Bang-Big Crunch (BB-BC) algorithm, are proposed in solving the BAP to maximize the throughput of the line under the total buffer size constraint for unreliable production lines. Performances of the proposed algorithms are tested on existing benchmark problems taken from the literature. The experimental results showed that the proposed BB–BC algorithm yielded better results than the proposed CGA as well as other algorithms reported in the literature.

缓冲区分配问题（BAP）的目的是在预定义的约束条件下确定生产线的最佳缓冲区配置。BAP是一个NP难题的组合优化问题，并且随着问题大小的增加，解决方案空间呈指数增长。因此，针对问题的启发式或元启发式搜索算法被广泛用于解决BAP。在这项研究中，两种基于人口的搜索算法；在求解BAP时，提出了战斗遗传算法（CGA）和大爆炸算法（BB-BC），以在不可靠生产线的总缓冲区大小约束下最大化生产线的吞吐量。所提出算法的性能在文献中对现有基准问题进行了测试。