Dock Assignment
Trucks are arriving faster than doors are freeing up.
The decision
Three trucks are inbound within the hour, two doors are open, labor is thin, and one load is temperature-sensitive. Which truck gets which door, and which waits?
Why it's hard today
Assign wrong and you idle a reefer, bottleneck the dock, or strand labor. The right assignment depends on load type, door capability, labor, and downstream priority — all changing minute to minute.
Your yard/WMS shows door status; it doesn't sequence arrivals against labor, load sensitivity, and downstream need. It shows availability, not an assignment.
What AgentForgeOS does
It assembles the picture you can't see, and makes the call under your rules.
Send the reefer to the capable door, the urgent-pick load to the second, and hold the third until labor frees — under your dock policy.
- Inbound ETAs and load types
- Door capability and status
- Labor availability
- Temperature / handling needs
- Downstream pick priority
- Cold-chain protection
- Labor constraints
- Door-capability rules
- Downstream-priority weighting
⚠ 3 inbound, 2 doors — facility 7, one reefer
Evidence assembled
- Reefer load can't wait long
- Door 3 has reefer capability, free now
- Labor covers two unloads, not three
- One load feeds an urgent pick
Recommendation · reefer → door 3, urgent-pick → door 5, third holds
Policy: cold-chain protected · labor within limit · awaiting dock lead
What improves
- No spoiled cold-chain loads
- Higher dock throughput
- Labor used where it counts
Each assignment and how the dock actually flowed is kept, so the model learns this facility's real door and labor rhythms.
Underneath, this is the same operating model the rest of the platform runs: verified context is assembled, options are weighed and adversarially challenged, the decision is governed by your policy, and the outcome is learned. The decision changes from one of these to the next. The architecture does not.
See how it worksThis is exactly how your team works today.
Only now the decision is assembled, governed, and remembered — instead of made from memory and lost.