A robust genetic algorithm for scheduling realistic hybrid flexible flow line problems
This article addresses the problem of hybrid flexible flow line where some constraints are considered to alleviate the chasm between the real-world industries scheduling and the production scheduling theories. Sequence-dependent setup times, machine release date and time lags are three constraints deemed to project the circumstances commonly found in real-world industries. To tackle the complexity of the problem at hand, we propose an approach base on genetic algorithm (GA). However, the performance of most evolutionary algorithms is significantly impressed by the values determined for the miscellaneous parameters which these algorithms possess. Hence, response surface methodology is applied to set the parameters of GA and to estimate the proper values of GA parameters in continually intervals. Finally, problems of various sizes are utilized to test the performance of the proposed algorithm and to compare it with some existing heuristic in the literature such as SPT, LPT and NEH.
sequence-dependent setup times time lags response surface methodology hybrid flexible flow shop realistic scheduling