ACMRIT · Sin-Kid · May 6, 2026
diff --git a/.gitignore b/.gitignore
@@ -0,0 +1,5 @@
+__pycache__/
+*.pyc
+.DS_Store
+output/
+.pytest_cache/
diff --git a/README.md b/README.md
@@ -1,18 +1,48 @@
 # Smart Delivery Dispatch System
 
 ## Team Information
-- **Team Name**: [Team Name]
-- **Year**: [Year]
-- **All-Female Team**: [Yes/No]
+- **Team Name**: TOP-G
+- **Year**: 2026
+- **All-Female Team**: No
 
 ## Architecture Overview
 
-#### Describe your approach here. Keep it short and clear.
-
-    - What is your dispatch strategy?
-    - How do you score agents for incoming orders?
-    - How do you manage SLA deadlines, priority orders, and agent capacity?
-    - What are the main steps in your pipeline?
+Our system implements a **greedy multi-objective dispatch pipeline** designed for high-throughput real-time operations. 
 
+- **Dispatch Strategy**: We use a simulation-clock-driven approach that processes order arrivals in 1-minute ticks. Assignments are made using a priority-first greedy selection.
+- **Scoring Logic**: Agents are scored for each order based on a weighted linear combination of five factors: **Delivery Time** (distance + prep), **SLA Risk** (urgency based on deadline), **Workload Fairness** (cumulative assignments), **Priority Boost** (weighted multipliers), and **Agent Rating**. The system selects the candidate that minimizes this composite cost.
+- **Management**: 
+    - **SLA/Priority**: A min-heap keyed by `(priority, timestamp)` ensures strict tiered processing. 
+    - **Capacity**: A strict concurrency limit of **2 active orders** per agent is enforced via the Agent Registry.
+- **Pipeline**:
+    1. **Ingest**: Orders are loaded into a priority heap upon arrival.
+    2. **Candidate Generation**: Available agents are filtered by graph connectivity.
+    3. **Optimization**: Shortest paths are resolved via **Floyd-Warshall** precomputation for O(1) lookups.
+    4. **Execution**: Atomic state updates move orders from `PENDING` to `DELIVERED`.
 
 **Note:** Please do not change the format or spelling of anything in this README. The fields are extracted using a script, so any changes to the structure or formatting may break the extraction process.
+
+## Issue Resolution Summary
+
+| Issue | Resolution Description |
+| :--- | :--- |
+| **Issue 1** | Implemented `load_orders` in `src/data_loader.py` with full CSV validation and skip-on-error logic. |
+| **Issue 2** | Implemented `load_agents` with rating clamping and graph-location referential integrity checks in `main.py`. |
+| **Issue 3** | Built `EnvironmentGraph` in `src/graph.py` supporting Floyd-Warshall precomputation and Dijkstra for routing. |
+| **Issue 4** | Created `PriorityOrderQueue` using a min-heap keyed by `(priority, timestamp)` for strict tiered dispatch. |
+| **Issue 5** | Developed `AgentRegistry` to track location, workload (max 2), and availability state in O(1). |
+| **Issue 6** | Implemented `generate_candidates` in `src/scorer.py` to filter agents by path connectivity and capacity. |
+| **Issue 7** | Developed a multi-objective `score_candidates` function using weighted normalization of 5 key factors. |
+| **Issue 8** | Added support for tuning objective weights via `constraints.csv` and interactive UI sliders. |
+| **Issue 9** | Orchestrated atomic state updates between queue and registry within `Dispatcher._attempt_assignments`. |
+| **Issue 10** | Implemented `_complete_delivery` to handle flight removal, agent relocation, and SLA verification. |
+| **Issue 11** | Designed `Dispatcher` to auto-retry assignments upon every delivery completion to clear backlogs. |
+| **Issue 12** | Integrated Welford's algorithm in `src/metrics.py` for numerically stable online mean tracking. |
+| **Issue 13** | Implemented SLA violation tracking and average margin calculation reported by priority tier. |
+| **Issue 14** | Added calculation of assignment variance and standard deviation to monitor workload fairness. |
+| **Issue 15** | Developed `MetricsCollector.to_json()` for structured exports and a human-readable summary method. |
+| **Issue 16** | Robust error handling in `data_loader.py` for missing columns, file errors, and malformed numeric fields. |
+| **Issue 17** | Handled disconnected graphs, full agents, and stale deadlines with descriptive logging warnings. |
+| **Issue 18** | Achieved sub-millisecond query time via $O(1)$ routing lookups and implemented latency monitoring. |
+| **Issue 19** | Optimized processing pipeline to exceed 100 orders/min throughput, tracked via wall-clock time. |
+| **Issue 20** | Completed comprehensive documentation of the greedy dispatch strategy and project architecture. |
diff --git a/data/raw/constraints.csv b/data/raw/constraints.csv
@@ -5,4 +5,8 @@ default_sla_minutes,50
 priority_weight_high,1.5
 priority_weight_normal,1.0
 priority_weight_low,0.8
-
+weight_delivery_time,0.35
+weight_sla_risk,0.30
+weight_workload,0.20
+weight_priority,0.10
+weight_rating,0.05
diff --git a/frontend/app.js b/frontend/app.js
@@ -0,0 +1,209 @@
+// app.js — UI controller, charts, canvas grid
+
+// ── Data (embedded from CSVs) ─────────────────────────────────────────────
+const ORDERS_RAW = `O001,2026-05-03 09:00:00,2,3,12,high,45
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+O149,2026-05-03 17:18:00,3,5,6,low,62
+O150,2026-05-03 17:22:00,7,3,12,high,41`;
+
+const AGENTS_RAW = `A001,0,0,4.8
+A002,1,1,4.5
+A003,2,2,4.9
+A004,3,3,4.2
+A005,4,4,4.7
+A006,5,5,4.6
+A007,6,6,4.3
+A008,7,7,4.8
+A009,8,8,4.4
+A010,9,9,4.5
+A011,0,5,4.7
+A012,1,6,4.9
+A013,2,7,4.1
+A014,3,8,4.6
+A015,4,9,4.8
+A016,5,0,4.3
+A017,6,1,4.5
+A018,7,2,4.7
+A019,8,3,4.2
+A020,9,4,4.9
+A021,1,4,4.6
+A022,3,5,4.4
+A023,5,7,4.8
+A024,7,9,4.5
+A025,9,1,4.7`;
+
+// parse
+function parseOrders() {
+  return ORDERS_RAW.trim().split('\n').map(line => {
+    const [id, ts, x, y, prep, priority, sla] = line.split(',');
+    return { id, ts, x: +x, y: +y, prep: +prep, priority, sla: +sla };
+  });
+}
+function parseAgents() {
+  return AGENTS_RAW.trim().split('\n').map(line => {
+    const [id, x, y, rating] = line.split(',');
+    return { id, loc: [+x, +y], rating: +rating };
+  });
+}
+function buildEdges() {
+  const edges = [];
+  for (let x = 0; x <= 9; x++) for (let y = 0; y <= 9; y++) {
+    if (x + 1 <= 9) edges.push({ from: [x, y], to: [x+1, y], dist: 3, delay: 1.0 });
+    if (y + 1 <= 9) edges.push({ from: [x, y], to: [x, y+1], dist: 3, delay: 1.0 });
+  }
+  return edges;
+}
+const CONSTRAINTS = {
+  maxActive: 2,
+  latencyTarget: 5,
+  defaultSLA: 50,
+  priHigh: 1.5,
+  priNormal: 1.0,
+  priLow: 0.8
+};