Julia optimizer capacity planning scenario routing recovery posture

Optimize constrained capacity before backlog turns into customer-facing drag.

A Julia reference implementation for Kinetic Gain OS: allocate limited facility units across higher-priority lanes, quantify unserved demand, and publish a buyer-readable operator report from the same model.

Route: /capacity-lane/ Route: /constraint-matrix/ Route: /allocation-posture/

Operator KPIs

dashboard summary

assigned units

Units allocated to live lanes in the best plan.

19050.0

weighted margin

Gross margin protected by the chosen routing plan.

1798.0

weighted risk

Accumulated risk exposure still carried by the assigned plan.

active facilities

Plants and cross-docks represented in the optimizer sweep.

Facility posture

where capacity is tight

FC-1

Northeast Packaging Cell

Assigned 15 of 18 units with 3 units left and stability posture of 96.0%.

YELLOW

FC-2

Midwest Assembly Cluster

Assigned 14 of 14 units with 0 units left and stability posture of 91.0%.

RED

FC-3

South Port Cross-Dock

Assigned 11 of 11 units with 0 units left and stability posture of 83.0%.

RED

Lane allocation

priority vs constraint

Lane	Assigned	Unserved	Score	Status
Retail replenishment sprint LN-11 · Northeast Packaging Cell	9 / 9	0	3805.2	GREEN
Med-device promo recovery LN-14 · Northeast Packaging Cell	6 / 6	0	2817.0	GREEN
Field service parts relay LN-21 · Midwest Assembly Cluster	7 / 8	1	2423.4	YELLOW
Hospitality opening packet LN-27 · Midwest Assembly Cluster	7 / 7	0	2546.6	GREEN
Port diversion buffer LN-34 · South Port Cross-Dock	6 / 10	4	1534.8	RED
Cold-chain exception sweep LN-39 · South Port Cross-Dock	5 / 5	0	2410.0	GREEN

why this matters

Kinetic Gain Embedded tie-back: this repo proves the portfolio can carry optimization and planning logic in Julia while still publishing the same buyer-readable operator surface language.