| dc.description.abstract | Production scheduling, or flexible job-shop scheduling problem in specific, has widely been
a regular practice for most manufacturing firms, it is necessary for businesses to come up
with the best schedules in the most time-efficient manner. An effective schedule will
minimize the amount of unnecessary time for production or operation, increase overall
efficiency, and consequently reduce the costs incurred. The proposed algorithms in this
study integrated the workflow and concepts from conventional genetic algorithm (GA), and
opposition-based learning (OBL) to improve the quality of chromosome population,
eventually enhance the overall converging performance. To further tackle the issue of
premature convergence, a multiple-restart (MR) strategy, as well as a diversity verification
scheme using Shannon’s diversity index were utilized to provide additional support for
algorithms to escape from the local traps. The proposed algorithms were tested on several
infamous benchmark instances to record their performance and solution deviation. And
finally, a sensitivity analysis regarding a few parameters were also performed in order to
validate whether there is any correlation between these parameters and the final accuracy. | en_US |
