A filter-and-fan approach to the job shop scheduling problem

The Job Shop Scheduling Problem (JSSP) is a notorious difficult problem in combinatorial optimization for which extensive investigation has been devoted to the development of efficient algorithms to find optimal or near-optimal solutions. This paper proposes a new heuristic algorithm for the JSSP that effectively combines the classical shifting bottleneck procedure (SBP) with a dynamic and adaptive neighborhood search procedure based on a filter and fan method (F&F). In the algorithm, SBP is used to generate a starting solution as well as to re-optimize the best schedules produced by the F&F method. The F&F is a local search procedure that generates compound moves in a tree search fashion. Computational results carried out on a standard set of 43 benchmark problems shows that the F&F algorithm performs more robustly and effectively than a number of leading metaheuristic algorithms and rivals the best. * Corresponding author. E-mail: [email protected]