ºÝºÝߣshows by User: RobertRichardsPhD / http://www.slideshare.net/images/logo.gif ºÝºÝߣshows by User: RobertRichardsPhD / Mon, 01 Aug 2016 20:36:19 GMT ºÝºÝߣShare feed for ºÝºÝߣshows by User: RobertRichardsPhD 2010 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’s 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’s 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/64594564 RobertRichardsPhD@slideshare.net(RobertRichardsPhD) 2010 06-07-sto-2010-intelligent-resource-scheduling-for-reduced-turnaround-durations-as-presented RobertRichardsPhD 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’s 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&amp;height=120&amp;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’s 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.
2010 06-07-sto-2010-intelligent-resource-scheduling-for-reduced-turnaround-durations-as-presented from Robert Richards, Ph.D.
]]> 212 3 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 presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 A 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/63188275 RobertRichardsPhD@slideshare.net(RobertRichardsPhD) A Schedule Optimization Tool for Destructive and Non-Destructive Vehicle Tests RobertRichardsPhD 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. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/aurora-vticaps-spark2016presentation-160617203706-thumbnail.jpg?width=120&amp;height=120&amp;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.
A Schedule Optimization Tool for Destructive and Non-Destructive Vehicle Tests from Robert Richards, Ph.D.
]]> 240 4 https://cdn.slidesharecdn.com/ss_thumbnails/aurora-vticaps-spark2016presentation-160617203706-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://public.slidesharecdn.com/v2/images/profile-picture.png https://cdn.slidesharecdn.com/ss_thumbnails/2010-06-07-sto-2010-intelligent-resource-scheduling-for-reduced-turnaround-durations-as-presented-160801203619-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/2010-0607sto2010intelligentresourceschedulingforreducedturnarounddurationsaspresented/64594564 2010 06-07-sto-2010-in... https://cdn.slidesharecdn.com/ss_thumbnails/aurora-vticaps-spark2016presentation-160617203706-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/a-schedule-optimization-tool-for-destructive-and-nondestructive-vehicle-tests/63188275 A Schedule Optimizatio...