Job shop scheduling by taboo search with fuzzy reasoning

In the last two decades, various approximation approaches, such as dispatching rules, shifting bottleneck heuristic and local search methods, are proposed for solving the job shop scheduling problem. As one of the local search methods, taboo search provides a promising alternative for the job shop scheduling problem; however, it has to be tailored each time with respect to parameters for every instance in order to produce desirable solution. In order to improve its search efficiency, an approach is proposed for the job shop scheduling problem by using taboo search with fuzzy reasoning. There are two parts in this approach: taboo search module and fuzzy reasoning module that performs the function of adaptive parameter adjustment in taboo search. The performance issues of this approach are also discussed by means of commonly used benchmarks.