A successive genetic algorithm for solving the job shop scheduling problem

Abstract

A successive genetic algorithm is proposed for solving job shop scheduling problems in which the total weighted tardiness should be minimized. In each iteration, the following three steps are performed. First, a new subproblem is defined by extracting a subset of operations from the entire operation set. Then, the jobs' bottleneck characteristic values are introduced to depict the criticality of each operation in the current subproblem. Finally, a genetic algorithm is applied to optimize the production sequence of these operations based on the bottleneck information. Numeric computations show that the proposed algorithm is effective for solving the job shop scheduling problem. © 2012 Springer-Verlag.