(365-2028-1-PB)A TABU SEARCH FOR MULTIPLE MULTI-LEVEL REDUNDANCY ALLOCATION PROBLEM IN SERIES-PARALLEL SYSTEMS

摘要

The traditional RAP (Redundancy Allocation Problem) is to consider only the component redundancy at the lowest-level. A system can be functionally decomposed into system, module, and component levels. Modular redundancy can be more effective than component redundancy at the lowest-level. We consider a MMRAP (Multiple Multi-level Redundancy Allocation Problem) in which all available items for redundancy (system, module, and component) can be simultaneously chosen. A tabu search (TS) of memory-based mechanisms that balances intensification with diversification via the short-term and long-term memory is proposed for its solution. To the best of our knowledge, this is the first attempt to use a TS for MMRAP. Our algorithm is compared with the existing genetic algorithm(GA) for MMRAP on the new composed test problems as well as the benchmark problems in the literature. Computational results show that the TS outstandingly outperforms the GA for all test problems.