| Research > Scheduling > SimYard
SimYard is a stochastic shipyard
simulation tool that can model a wide variety of shipyard
production conditions, including problems that arise and how the
shipyard reacts to those problems. It was built to evaluate ARGOS under actual production conditions to validate theoretical labor savings
The main challenge in SimYard to
performance data describing how a particular shipyard performs and tune
SimYard to behave accordingly; this requires a model of SimYard
itself. This multivariate
calibration problem is tackled using inverse regression methods.
There are two main inputs to SimYard:
involves two main steps. In the first, hundreds of simulations
are done with the historical project, using a wide range of possible
input parameters (how often workers are sick, how often durations are
under-estimated, etc). Using inverse regression, the historical
performance data and the SimYard performance data (how things went
during these simulations) can be combined to determine appropriate
input parameters for the shipyard in question.
- Historical data:
A completed project as well as performance data describing how things
went (how often constraints were broken, tasks were delayed, etc.).
- New project and schedule:
These two pieces of data represent the particular situation of interest to be simulated.
In the second step, the appropriate input parameters discovered in the
first step are used to tune SimYard; simulations are then done with the
new project and schedule. The output data from these simulations
describe how the shipyard can be expected to behave on that project
with the given schedule. For example, outputs include the
likelihood that deadlines will be missed and the fraction of
tasks likely to be paused. Output data also includes cost
information; therefore, by simulating both a shipyard schedule and an
ARGOS schedule and compariing the resulting costs, labor savings likely
to result from ARGOS can be obtained.
approach used by SimYard has been validated in a number of ways,
including internal consistency tests (comparing it with itself on
simple problems), external consistency tests (running SimYard
against production shipyard data), and evaluation by outside experts.
a large shipyard project (7000 tasks), while an ARGOS schedule saves
percent of labor costs in theory, the following SimYard graph
shows that, in reality, that schedule can be expected to save between 5
and 15%, with savings greater than 9% on average (more than 50% of the
provides a number of ways to present the data to help understand how
the simulations behave and how that behavior is affected by various
parameters. The following is a simple example:
graph illustrates that, as the mean manpower paremeter (how many people
are needed to do a task, on average, relative to the number of people
predicted) increases, the average savings of the ARGOS schedule
decrease (but only slightly).