Learn how we model closed loop vehicle systems to determine the time spent at a station, vehicle spacing, assembly sequence, battery charge, and rework versus rebuild decisions.
We have many names for autonomous Closed Loop Vehicle Systems that are used for assembly, material handling, and logistics in today’s manufacturing facilities. These include Automatic Guided Vehicles (AGVS), Rail Guided Vehicles (RGC), and Automatic Guided Carts (AGC). Today, we’ll focus on using these carts in a closed loop as part of an assembly process where the vehicles move between stations where the vehicle stops for an assembly process.
Typically, we are trying to determine the optimal number of vehicles and the expected throughput of the system. The vehicles can be quite expensive so finding the right number can dramatically impact the ROI of a system.
Many factors need to be considered when modeling these closed loop systems such as the vehicle parameters, the time spent at each station, vehicle spacing, assembly sequence, battery charge, and rework versus rebuild decisions.
These vehicle systems are very sensitive to speed, spacing, and the timing between locations. It is essential to use simulations that are at the proper scale and to model the actual vehicle speed, accelerations, and deceleration rather than just assuming an index time between stations. The reality of how these systems are installed introduces variable spacing, curves, and vehicle design changes that will impact how the vehicle moves through the system. The image below shows a typical vehicle and some of the parameters used when modeling the vehicle. Having a scaled drawing of the facility layout coupled with these parameters allows for an accurate model of the system.
|Max Straight||28.44 in/sec|
|Max Curve||13 in/sec|
|Creep Speed||14.22 in/sec|
Cycle Time & Process Exceptions
The amount of time that vehicle needs to stop in each station is also critical. This time can be impacted by process and by random exceptions that occur. The process time may vary based on the type of part being built. Often in automotive applications the production lines may process a dozen different types of parts down the line in any order. This variation in cycle time can be dramatic and will impact the overall speed of the line. The chart below shows the gross capabilities of a line that has 13 different families of parts being produced.
Some of the later families have half of the total throughput of others. The production mix will dictate how often each family is produced. It is critical to understand how that product mix impacts the ability of the line to deliver at the required rate. It is often necessary to move content between stations to balance lines. In cases where the volume is very low, it may be necessary to accept a longer cycle time and the impact it will have production to avoid increasing the size of the line dramatically.
If everything goes as planned, these systems are not complicated to plan and operate. This is never the case! Manual assembly processes will vary and often take longer than planned. Material deliveries can be late and cause the line to pause. Vehicles can break down and stop the line momentarily. Parts can be assembled incorrectly causing a need to repair or rebuild the assembly. All of these “exceptions” need to be estimated as part of the modeling. These estimated can come from prior assembly lines or the estimates can be derived from initial part builds done by the engineers creating the process. Either way, these variations will impact production rates. While the goal will be to reduce these issues, the simulation can point engineers to the bottleneck of the line and where they need to focus their efforts to have an impact on overall production.
Another wrinkle that can exist with these systems is sequence. Parts are often assembled for Just In Time (JIT) delivery. This means that if there are parallel operations or if a part needs to be reworked, the system needs to maintain a specific sequence. This can stop the line or cause manual intervention to fix the sequence issue prior to shipping. Either of these can be costly and the simulation can help define and quantify the problem.
These vehicles are almost always battery powered to allow for the flexibility in layout and ease of changing vehicles. Simulations are excellent at estimating the range of requirements for the vehicles and assessing where and when charging makes sense. You can also use the model to evaluate different charging strategies. Should you have dedicated charging locations and have vehicles rotate through these charge locations? Should you “opportunity charge” during your process and gain a certain amount of charging as part of the process. The graph below shows the amount of charge an average vehicle gets for each lap of the assembly process. If you can opportunity charge, it will often reduce the number of vehicles required and cut down on the footprint and cost of the system.
Closed Loop Vehicle System Analysis
When analyzing these systems, we can vary the number of vehicles and the location of buffer stations to achieve the stated goals of the system. One targeted way to identify the bottleneck in these systems is to measure the available time at each station. By assessing the composite cycle time for each station when considering process specific variations and exceptions at each station, it is easy to focus on the area where the system is restricted. This is confirmed by analyzing the buffer stations before and after the bottleneck to confirm it is the bottleneck.
The chart below shows how throughput varies as more carts are introduced into the loop. The system basically produces the same number of parts with anywhere from 14-17 vehicles. This helps engineers budget effectively and lets operations know how many vehicles need to be online at any point in time to make production. These analyses usually do not include the preventative maintenance required for the vehicles. Typically, one vehicle is out of service for maintenance for each 15 to 20 vehicles in a fleet. In the case below, having 15 or 16 vehicles would be optimal depending on the plan for preventative maintenance.
Closed loop vehicle systems can be quite complex. Modeling them in 3D and at-scale is critical to getting the system right. If you would like to continue this discussion, please contact us.
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