Algorithms Performance and Problem Structure for Flow-Shop Scheduling

Proceedings of the AAAI Conference on Artificial Intelligence, 16

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 16

Scheduling

Test suites for many domains often fail to model features present in real-world problems. For the permutation flow-shop sequencing problem (PFSP), the most popular test suite consists of problems whose features are generated from a single uniform random distribution. Synthetic generation of problems with characteristics present in real-world problems is a viable alternative. We compare the performance of several competitive algorithms on problems produced with such a generator. We find that, as more realistic characteristics are introduced, the performance of a state-of-the-art algorithm degrades rapidly: faster and less complex stochastic algorithms provide superior performance. Our empirical results show that small changes in problem structure or problem size can influence algorithm performance. We hypothesize that these performance differences may be partially due to differences in search space topologies; we show that structured problems produce topologies with performance plateaus. Algorithm sensitivity to problem characteristics suggests the need to construct test suites more representative of real-world applications.