A comparison of combat genetic and big bang-big crunch algorithms for solving the buffer allocation problem

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.

Automated summary

The BAP is an NP-hard combinatorial optimization problem with exponentially growing solution space. Two population-based search algorithms, CGA and BB-BC, were proposed to solve the BAP. Experimental results showed that BB-BC algorithm outperformed CGA and other algorithms.