A job-insertion heuristic for minimizing the mean flowtime in dynamic flowshops

A new adaptive job-insertion based heuristic is presented to minimize the mean flowtime in a dynamic flowshop consisting of m machines. Job orders arrive to the system randomly, and the job arrival or release dates are not known in advance. The heuristic is derived by inserting new jobs into the scheduled sequence as needed when the machine becomes free. Computation results indicate that the proposed heuristic performs 2.7%–10.8% better than the SPT dispatching rule, which is currently one of the most effective methods for minimizing the mean flowtime in dynamic flowshops.

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