dc.contributor.author | Brown, Andrew Alexander Harold | |
dc.date.accessioned | 2014-11-11 14:16:42 (GMT) | |
dc.date.available | 2014-11-11 14:16:42 (GMT) | |
dc.date.issued | 2014-11-11 | |
dc.date.submitted | 2014-11-06 | |
dc.identifier.uri | http://hdl.handle.net/10012/8940 | |
dc.description.abstract | Motivated by a situation observed by our industry partner, we test if changing
dispatch methods within a job-shop can reduce the percentage of late jobs while
not reducing the maximum lateness across all jobs, the two key performance
indicators (KPIs) of interest. Using data provided by our industry partner, we
show that the earliest operation due date (EODD) dispatch rule is the best rule
for them. In addition, we propose an alternative idea for random job shop data,
the routing distribution, and we compare dispatching rules performance using
KPI frontiers under different routing distributions. We find that EODD is one
of several dispatching rule which consistently lie on the KPI frontier for different
job routing distributions. We further show that using multiple dispatch rules
across several job-shop departments does improve a job-shop’s performance on
the KPIs, though the improvement is small. Lastly, we leave the readers with
some insight into determining which dispatch rules should be considered for
different job-shops. | en |
dc.language.iso | en | en |
dc.publisher | University of Waterloo | en |
dc.subject | Job-Shop | en |
dc.subject | dispatching rule | en |
dc.subject | simulation | en |
dc.title | Dispatching Work: Finding the best dispatching method for real job-shops | en |
dc.type | Master Thesis | en |
dc.pending | false | en |
dc.subject.program | Management Sciences | en |
uws-etd.degree.department | Management Sciences | en |
uws-etd.degree | Master of Applied Science | en |
uws.typeOfResource | Text | en |
uws.peerReviewStatus | Unreviewed | en |
uws.scholarLevel | Graduate | en |