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Mon, 01 Aug 2016 20:36:19 GMT狠狠撸Share feed for 狠狠撸shows by User: RobertRichardsPhD2010 06-07-sto-2010-intelligent-resource-scheduling-for-reduced-turnaround-durations-as-presented
/slideshow/2010-0607sto2010intelligentresourceschedulingforreducedturnarounddurationsaspresented/64594564
2010-06-07-sto-2010-intelligent-resource-scheduling-for-reduced-turnaround-durations-as-presented-160801203619 In time critical applications such as Turnarounds, resource-loaded schedules have proven beneficial, however, the aerospace and other entities including NASA and Boeing, have learned that much of the benefits can be squandered when resource leveling (RL) is used instead of intelligent resource scheduling (IRS). By applying proven IRS to turnaround projects, flow-time reductions of 30%+ are possible versus RL. RL鈥檚 goal is to resolve over-allocations by delaying tasks to eliminate the over-allocations, but the efficiency of the resulting resource utilization is NOT a primary concern. At first glance this may not seem to be a major issue, however, it has been shown with small to large networks, significant differences (25%+) occur between RL and IRS results. So without adding one extra resource, an entire project can
be shortened significantly just by pressing a different button. Real-world Turnaround examples that have realized such improvements are provided.]]>
In time critical applications such as Turnarounds, resource-loaded schedules have proven beneficial, however, the aerospace and other entities including NASA and Boeing, have learned that much of the benefits can be squandered when resource leveling (RL) is used instead of intelligent resource scheduling (IRS). By applying proven IRS to turnaround projects, flow-time reductions of 30%+ are possible versus RL. RL鈥檚 goal is to resolve over-allocations by delaying tasks to eliminate the over-allocations, but the efficiency of the resulting resource utilization is NOT a primary concern. At first glance this may not seem to be a major issue, however, it has been shown with small to large networks, significant differences (25%+) occur between RL and IRS results. So without adding one extra resource, an entire project can
be shortened significantly just by pressing a different button. Real-world Turnaround examples that have realized such improvements are provided.]]>
Mon, 01 Aug 2016 20:36:19 GMT/slideshow/2010-0607sto2010intelligentresourceschedulingforreducedturnarounddurationsaspresented/64594564RobertRichardsPhD@slideshare.net(RobertRichardsPhD)2010 06-07-sto-2010-intelligent-resource-scheduling-for-reduced-turnaround-durations-as-presentedRobertRichardsPhDIn time critical applications such as Turnarounds, resource-loaded schedules have proven beneficial, however, the aerospace and other entities including NASA and Boeing, have learned that much of the benefits can be squandered when resource leveling (RL) is used instead of intelligent resource scheduling (IRS). By applying proven IRS to turnaround projects, flow-time reductions of 30%+ are possible versus RL. RL鈥檚 goal is to resolve over-allocations by delaying tasks to eliminate the over-allocations, but the efficiency of the resulting resource utilization is NOT a primary concern. At first glance this may not seem to be a major issue, however, it has been shown with small to large networks, significant differences (25%+) occur between RL and IRS results. So without adding one extra resource, an entire project can
be shortened significantly just by pressing a different button. Real-world Turnaround examples that have realized such improvements are provided.<img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2010-06-07-sto-2010-intelligent-resource-scheduling-for-reduced-turnaround-durations-as-presented-160801203619-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> In time critical applications such as Turnarounds, resource-loaded schedules have proven beneficial, however, the aerospace and other entities including NASA and Boeing, have learned that much of the benefits can be squandered when resource leveling (RL) is used instead of intelligent resource scheduling (IRS). By applying proven IRS to turnaround projects, flow-time reductions of 30%+ are possible versus RL. RL鈥檚 goal is to resolve over-allocations by delaying tasks to eliminate the over-allocations, but the efficiency of the resulting resource utilization is NOT a primary concern. At first glance this may not seem to be a major issue, however, it has been shown with small to large networks, significant differences (25%+) occur between RL and IRS results. So without adding one extra resource, an entire project can
be shortened significantly just by pressing a different button. Real-world Turnaround examples that have realized such improvements are provided.
]]>
2153https://cdn.slidesharecdn.com/ss_thumbnails/2010-06-07-sto-2010-intelligent-resource-scheduling-for-reduced-turnaround-durations-as-presented-160801203619-thumbnail.jpg?width=120&height=120&fit=boundspresentation000000http://activitystrea.ms/schema/1.0/posthttp://activitystrea.ms/schema/1.0/posted0A Schedule Optimization Tool for Destructive and Non-Destructive Vehicle Tests
/slideshow/a-schedule-optimization-tool-for-destructive-and-nondestructive-vehicle-tests/63188275
aurora-vticaps-spark2016presentation-160617203706 Whenever an auto manufacturer refreshes an existing car or
truck model or builds a new one, the model will undergo
hundreds if not thousands of tests before the factory line and
tooling is finished and vehicle production begins. These
tests are generally carried out on expensive, custom-made
prototype vehicles because the new factory lines for the
model do not exist yet. The work presented in this paper
describes how an existing intelligent scheduling software
framework was modified to include domain-specific
heuristics used in the vehicle test planning process. The
result of this work is a scheduling tool that optimizes the
overall given test schedule in order to complete the work in
a given time window while minimizing the total number of
vehicles required for the test schedule. The tool was
validated on the largest testing schedule for an updated
vehicle to date. This model exceeded the capabilities of the
existing manual scheduling process but was successfully
handled by the tool. Additionally the tool was expanded to
better integrate it with existing processes and to make it
easier for new users to create and optimize testing
schedules.]]>
Whenever an auto manufacturer refreshes an existing car or
truck model or builds a new one, the model will undergo
hundreds if not thousands of tests before the factory line and
tooling is finished and vehicle production begins. These
tests are generally carried out on expensive, custom-made
prototype vehicles because the new factory lines for the
model do not exist yet. The work presented in this paper
describes how an existing intelligent scheduling software
framework was modified to include domain-specific
heuristics used in the vehicle test planning process. The
result of this work is a scheduling tool that optimizes the
overall given test schedule in order to complete the work in
a given time window while minimizing the total number of
vehicles required for the test schedule. The tool was
validated on the largest testing schedule for an updated
vehicle to date. This model exceeded the capabilities of the
existing manual scheduling process but was successfully
handled by the tool. Additionally the tool was expanded to
better integrate it with existing processes and to make it
easier for new users to create and optimize testing
schedules.]]>
Fri, 17 Jun 2016 20:37:06 GMT/slideshow/a-schedule-optimization-tool-for-destructive-and-nondestructive-vehicle-tests/63188275RobertRichardsPhD@slideshare.net(RobertRichardsPhD)A Schedule Optimization Tool for Destructive and Non-Destructive Vehicle Tests RobertRichardsPhDWhenever an auto manufacturer refreshes an existing car or
truck model or builds a new one, the model will undergo
hundreds if not thousands of tests before the factory line and
tooling is finished and vehicle production begins. These
tests are generally carried out on expensive, custom-made
prototype vehicles because the new factory lines for the
model do not exist yet. The work presented in this paper
describes how an existing intelligent scheduling software
framework was modified to include domain-specific
heuristics used in the vehicle test planning process. The
result of this work is a scheduling tool that optimizes the
overall given test schedule in order to complete the work in
a given time window while minimizing the total number of
vehicles required for the test schedule. The tool was
validated on the largest testing schedule for an updated
vehicle to date. This model exceeded the capabilities of the
existing manual scheduling process but was successfully
handled by the tool. Additionally the tool was expanded to
better integrate it with existing processes and to make it
easier for new users to create and optimize testing
schedules.<img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/aurora-vticaps-spark2016presentation-160617203706-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Whenever an auto manufacturer refreshes an existing car or
truck model or builds a new one, the model will undergo
hundreds if not thousands of tests before the factory line and
tooling is finished and vehicle production begins. These
tests are generally carried out on expensive, custom-made
prototype vehicles because the new factory lines for the
model do not exist yet. The work presented in this paper
describes how an existing intelligent scheduling software
framework was modified to include domain-specific
heuristics used in the vehicle test planning process. The
result of this work is a scheduling tool that optimizes the
overall given test schedule in order to complete the work in
a given time window while minimizing the total number of
vehicles required for the test schedule. The tool was
validated on the largest testing schedule for an updated
vehicle to date. This model exceeded the capabilities of the
existing manual scheduling process but was successfully
handled by the tool. Additionally the tool was expanded to
better integrate it with existing processes and to make it
easier for new users to create and optimize testing
schedules.