Timestamp_hr int64 0 5k | Node_Tier stringclasses 1
value | Current_Inventory_Pallets int64 0 18.4k | Inbound_Transit_Pallets int64 0 600 | Lead_Time_hrs float64 1 120 | Stochastic_Demand int64 8 149 | Holding_Cost_USD float64 0 46.1k | Stockout_Penalty_USD int64 0 149k | Episode_ID int64 1 9 |
|---|---|---|---|---|---|---|---|---|
0 | Tier_3 | 145 | 0 | 22.5 | 105 | 362.5 | 0 | 1 |
1 | Tier_3 | 57 | 0 | 28.9 | 88 | 142.5 | 0 | 1 |
2 | Tier_3 | 0 | 0 | 18.1 | 122 | 0 | 65,000 | 1 |
3 | Tier_3 | 0 | 0 | 15.3 | 145 | 0 | 145,000 | 1 |
4 | Tier_3 | 0 | 0 | 24.7 | 91 | 0 | 91,000 | 1 |
5 | Tier_3 | 0 | 0 | 19.9 | 110 | 0 | 110,000 | 1 |
6 | Tier_3 | 0 | 0 | 21.5 | 130 | 0 | 130,000 | 1 |
7 | Tier_3 | 0 | 0 | 26.1 | 101 | 0 | 101,000 | 1 |
8 | Tier_3 | 0 | 0 | 23 | 77 | 0 | 77,000 | 1 |
9 | Tier_3 | 0 | 0 | 17.8 | 95 | 0 | 95,000 | 1 |
10 | Tier_3 | 0 | 0 | 20.5 | 115 | 0 | 115,000 | 1 |
11 | Tier_3 | 0 | 0 | 25.4 | 128 | 0 | 128,000 | 1 |
12 | Tier_3 | 0 | 0 | 19.2 | 103 | 0 | 103,000 | 1 |
13 | Tier_3 | 0 | 0 | 22.8 | 81 | 0 | 81,000 | 1 |
14 | Tier_3 | 0 | 0 | 27.5 | 135 | 0 | 135,000 | 1 |
15 | Tier_3 | 0 | 0 | 18.7 | 118 | 0 | 118,000 | 1 |
16 | Tier_3 | 0 | 0 | 24.1 | 99 | 0 | 99,000 | 1 |
17 | Tier_3 | 0 | 0 | 21 | 125 | 0 | 125,000 | 1 |
18 | Tier_3 | 0 | 18 | 23.3 | 140 | 0 | 122,000 | 1 |
19 | Tier_3 | 0 | 0 | 16.5 | 85 | 0 | 85,000 | 1 |
20 | Tier_3 | 0 | 15 | 20 | 112 | 0 | 97,000 | 1 |
21 | Tier_3 | 0 | 0 | 25.9 | 107 | 0 | 107,000 | 1 |
22 | Tier_3 | 0 | 16 | 19.5 | 133 | 0 | 117,000 | 1 |
23 | Tier_3 | 0 | 0 | 22.1 | 93 | 0 | 93,000 | 1 |
24 | Tier_3 | 0 | 0 | 26.8 | 79 | 0 | 79,000 | 1 |
25 | Tier_3 | 239 | 0 | 18.3 | 11 | 597.5 | 0 | 2 |
26 | Tier_3 | 224 | 0 | 25.1 | 15 | 560 | 0 | 2 |
27 | Tier_3 | 205 | 0 | 20.5 | 19 | 512.5 | 0 | 2 |
28 | Tier_3 | 192 | 0 | 26.8 | 13 | 480 | 0 | 2 |
29 | Tier_3 | 176 | 0 | 19.9 | 16 | 440 | 0 | 2 |
30 | Tier_3 | 158 | 0 | 24.5 | 18 | 395 | 0 | 2 |
31 | Tier_3 | 146 | 0 | 22.1 | 12 | 365 | 0 | 2 |
32 | Tier_3 | 131 | 0 | 25.9 | 15 | 327.5 | 0 | 2 |
33 | Tier_3 | 114 | 0 | 21.3 | 17 | 285 | 0 | 2 |
34 | Tier_3 | 100 | 0 | 23 | 14 | 250 | 0 | 2 |
35 | Tier_3 | 87 | 0 | 20.8 | 13 | 217.5 | 0 | 2 |
36 | Tier_3 | 71 | 0 | 27.5 | 16 | 177.5 | 0 | 2 |
37 | Tier_3 | 53 | 0 | 22.9 | 18 | 132.5 | 0 | 2 |
38 | Tier_3 | 38 | 0 | 24.1 | 15 | 95 | 0 | 2 |
39 | Tier_3 | 21 | 0 | 19.5 | 17 | 52.5 | 0 | 2 |
40 | Tier_3 | 9 | 0 | 25.2 | 12 | 22.5 | 0 | 2 |
41 | Tier_3 | 0 | 0 | 21.7 | 14 | 0 | 5,000 | 2 |
42 | Tier_3 | 0 | 0 | 23.5 | 19 | 0 | 19,000 | 2 |
43 | Tier_3 | 0 | 14 | 20.1 | 16 | 0 | 2,000 | 2 |
44 | Tier_3 | 0 | 0 | 26.3 | 13 | 0 | 13,000 | 2 |
45 | Tier_3 | 0 | 0 | 22.5 | 15 | 0 | 15,000 | 2 |
46 | Tier_3 | 0 | 0 | 24.8 | 17 | 0 | 17,000 | 2 |
47 | Tier_3 | 0 | 11 | 18.7 | 14 | 0 | 3,000 | 2 |
48 | Tier_3 | 0 | 0 | 23.3 | 11 | 0 | 11,000 | 2 |
49 | Tier_3 | 1 | 17 | 25 | 16 | 2.5 | 0 | 2 |
50 | Tier_3 | 0 | 0 | 25.1 | 14 | 0 | 13,000 | 2 |
51 | Tier_3 | 0 | 18 | 23.8 | 18 | 0 | 0 | 2 |
52 | Tier_3 | 0 | 0 | 24.5 | 15 | 0 | 15,000 | 2 |
53 | Tier_3 | 9 | 20 | 26 | 11 | 22.5 | 0 | 2 |
54 | Tier_3 | 18 | 25 | 22.9 | 16 | 45 | 0 | 2 |
55 | Tier_3 | 14 | 15 | 24.1 | 19 | 35 | 0 | 2 |
56 | Tier_3 | 15 | 14 | 25.5 | 13 | 37.5 | 0 | 2 |
57 | Tier_3 | 17 | 19 | 23.2 | 17 | 42.5 | 0 | 2 |
58 | Tier_3 | 18 | 16 | 24.8 | 15 | 45 | 0 | 2 |
59 | Tier_3 | 20 | 16 | 25.9 | 14 | 50 | 0 | 2 |
60 | Tier_3 | 13 | 11 | 23.5 | 18 | 32.5 | 0 | 2 |
61 | Tier_3 | 1 | 0 | 24.3 | 12 | 2.5 | 0 | 2 |
62 | Tier_3 | 0 | 18 | 26.1 | 19 | 0 | 0 | 2 |
63 | Tier_3 | 16 | 31 | 22.7 | 15 | 40 | 0 | 2 |
64 | Tier_3 | 18 | 15 | 25 | 13 | 45 | 0 | 2 |
65 | Tier_3 | 15 | 13 | 24.9 | 16 | 37.5 | 0 | 2 |
66 | Tier_3 | 34 | 30 | 23.1 | 11 | 85 | 0 | 2 |
67 | Tier_3 | 29 | 12 | 25.3 | 17 | 72.5 | 0 | 2 |
68 | Tier_3 | 14 | 0 | 24 | 15 | 35 | 0 | 2 |
69 | Tier_3 | 0 | 0 | 26.5 | 14 | 0 | 0 | 2 |
70 | Tier_3 | 1 | 19 | 22.5 | 18 | 2.5 | 0 | 2 |
71 | Tier_3 | 22 | 34 | 25.2 | 13 | 55 | 0 | 2 |
72 | Tier_3 | 7 | 0 | 24.7 | 15 | 17.5 | 0 | 2 |
73 | Tier_3 | 0 | 0 | 23.9 | 12 | 0 | 5,000 | 2 |
74 | Tier_3 | 0 | 17 | 26.3 | 17 | 0 | 0 | 2 |
75 | Tier_3 | 234 | 0 | 25.1 | 16 | 585 | 0 | 3 |
76 | Tier_3 | 222 | 0 | 22.9 | 12 | 555 | 0 | 3 |
77 | Tier_3 | 207 | 0 | 23.5 | 15 | 517.5 | 0 | 3 |
78 | Tier_3 | 190 | 0 | 24.8 | 17 | 475 | 0 | 3 |
79 | Tier_3 | 177 | 0 | 21.3 | 13 | 442.5 | 0 | 3 |
80 | Tier_3 | 163 | 0 | 23.1 | 14 | 407.5 | 0 | 3 |
81 | Tier_3 | 152 | 0 | 25.5 | 11 | 380 | 0 | 3 |
82 | Tier_3 | 134 | 0 | 22.1 | 18 | 335 | 0 | 3 |
83 | Tier_3 | 119 | 0 | 23.9 | 15 | 297.5 | 0 | 3 |
84 | Tier_3 | 100 | 0 | 21 | 19 | 250 | 0 | 3 |
85 | Tier_3 | 90 | 0 | 24.2 | 10 | 225 | 0 | 3 |
86 | Tier_3 | 74 | 0 | 23.3 | 16 | 185 | 0 | 3 |
87 | Tier_3 | 61 | 0 | 25 | 13 | 152.5 | 0 | 3 |
88 | Tier_3 | 47 | 0 | 22.5 | 14 | 117.5 | 0 | 3 |
89 | Tier_3 | 32 | 0 | 23.7 | 15 | 80 | 0 | 3 |
90 | Tier_3 | 20 | 0 | 21.9 | 12 | 50 | 0 | 3 |
91 | Tier_3 | 3 | 0 | 24.5 | 17 | 7.5 | 0 | 3 |
92 | Tier_3 | 0 | 0 | 23 | 18 | 0 | 15,000 | 3 |
93 | Tier_3 | 0 | 0 | 22.8 | 11 | 0 | 11,000 | 3 |
94 | Tier_3 | 0 | 0 | 24.1 | 14 | 0 | 14,000 | 3 |
95 | Tier_3 | 0 | 0 | 21.5 | 19 | 0 | 19,000 | 3 |
96 | Tier_3 | 0 | 0 | 23.8 | 13 | 0 | 13,000 | 3 |
97 | Tier_3 | 0 | 0 | 25.2 | 16 | 0 | 16,000 | 3 |
98 | Tier_3 | 0 | 0 | 22.3 | 15 | 0 | 15,000 | 3 |
99 | Tier_3 | 1 | 18 | 24.7 | 17 | 2.5 | 0 | 3 |
Defense Logistics Simulation Suite (5,000-Hour Sample)
Mission-critical supply chain telemetry engineered for zero-drift mathematical precision and high-stakes stochastic chaos.
Overview
This is a high-fidelity synthetic dataset representing a Tier-3 End Unit (Forward Operating Base) operating under continuous stress and operational surges. It is designed to bridge the gap between "clean" academic datasets and the messy reality of edge-node logistics.
This 5,000-hour sample is a subset of the AI Mind Teams 50,000-Hour Premium Suite, engineered to stress-test Reinforcement Learning (RL) agents and forecasting models.
Key Features
- 0.0 Mathematical Drift: Verified flow conservation physics: $I_t = \max(0, I_{t-1} + R_t - D_t)$.
- Route Severance Physics: Dynamic lead-time spikes (24h to 150h+).
- Stochastic Demand: Poisson-distributed consumption with periodic operational surges.
- Transit Pipeline Queue: Real-time tracking of orders in the "void."
Data Dictionary
| Column Name | Description |
|---|---|
| Current_Inventory_Pallets | Net physical inventory at the start of the hour. |
| Inbound_Transit_Pallets | Pipeline Arrival: Total supply physically arriving this hour. |
| Lead_Time_hrs | Current expected transit time for newly placed orders. |
| Stochastic_Demand | End-user consumption for the hour. |
| Holding_Cost_USD | $2.50 per pallet per hour. |
| Stockout_Penalty_USD | $1,000.00 per pallet shortfall. |
Licensing & Commercial Use
This 5,000-hour sample is provided under the Creative Commons Attribution-NonCommercial 4.0 (CC BY-NC 4.0) license. It is intended for academic research and non-commercial exploration.
Upgrade to the Full Suite (Commercial Ready)
For commercial research, interactive RL training, and full-scale benchmarking, the AI Mind Teams Enterprise Suite includes:
- 50,000-Hour Full Dataset (No Sampling Noise).
- Interactive Farama Gymnasium Environment (Plug-and-play RL training).
- Pre-tuned PPO & (s, S) Baseline Scripts.
- Full Commercial Usage License.
Get the Professional/Enterprise Suite on Gumroad
Contact: aimindteams@gmail.com
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