12. WMS Smart Warehouse Putaway & S-Shape 2-Opt Pick WaveCopy Anchor Link

Production Paradigm: GS1-128 Barcode Parser + Multi-Level Recipe Bill of Materials (BOM) Explosion + First-Expired First-Out (FEFO) Expiry Alert + S-Shape 2-Opt TSP Walking Route Optimizer.

💥 The Real-World BottleneckCopy Anchor Link

In modern logistics fulfillment centers, dark stores, and food production operations, warehouse workers spend up to 60% of their working hours merely walking between storage aisles.

Unoptimized warehouse operations suffer from:

1. Inefficient Pick Paths: Pickers walk back and forth across warehouse aisles in random sequence, wasting hours every shift.
2. Perishable Inventory Spoilage: Failure to enforce strict FEFO (First-Expired, First-Out) logic leads to expired ingredients rotting at the back of shelves.
3. Recipe BOM Discrepancies: In F&B and manufacturing, selling a finished product (e.g. 1 Pizza) requires deducting raw ingredients (flour, cheese, sauce) plus accounting for standard production scrap/waste factor. When done naively, inventory counts drift into fiction.

🏛️ High-Concurrency ArchitectureCopy Anchor Link

This blueprint implements an end-to-end Warehouse Management System (WMS) engine: parsing GS1 barcodes on intake, computing automated putaway bins, exploding complex recipe BOMs, and generating mathematically optimized walking paths.

Key Architectural PhasesCopy Anchor Link

1. GS1-128 Barcode Stream Parser (parseBarcodeGS1):
   • Decodes Application Identifiers (AI): GTIN (01), Batch/Lot Number (10), Expiration Date (17), and Serial Number (21).
   • Handles continuous hardware barcode scanner buffers with automated debounce.
2. Multi-Level Recipe BOM Engine (explodeBomRequirements):
   • Recursively traverses sub-assemblies and raw ingredients.
   • Automatically factors in scrap/waste percentages (deductIngredientsWithWaste) to maintain 100% accurate physical stock counts.
3. FEFO Expiry Alerting (evaluateBatchExpiry):
   • Enforces picking from the earliest expiring lots first, proactively raising alerts for batches nearing expiration.
4. S-Shape 2-Opt TSP Route Optimizer (optimizeSshape2OptRoute):
   • Models the warehouse grid as a Traveling Salesperson Problem (TSP).
   • Combines the classic S-Shape aisle-traversal heuristic with 2-Opt edge swaps to minimize total picker walking distance (calculateWarehouseWalkingDistance).

🧩 Composed Functional SlicesCopy Anchor Link

💻 Code ImplementationCopy Anchor Link

mkdir -p src/lib/wms
cp -r packages/bom-inventory/src/wms-barcode src/lib/wms/
cp packages/bom-inventory/src/recipe-bom.ts src/lib/wms/
cp -r packages/bom-inventory/src/inventory-alert src/lib/wms/

import { parseBarcodeGS1, optimizeSshape2OptRoute, calculateWarehouseWalkingDistance } from '@/lib/wms/wms-barcode';
import { BomInventoryEngine } from '@/lib/wms/recipe-bom';

// 1. Parse hardware barcode scanner input
export function handleBarcodeInput(rawBarcode: string) {
  const parsed = parseBarcodeGS1(rawBarcode);
  return {
    gtin: parsed.gtin,
    lotNumber: parsed.lot,
    expiryDate: parsed.expiryDate,
  };
}

// 2. Plan optimal pick path for a wave of 30 items
export function planWavePickRoute(itemsToPick: { id: string; aisle: number; bay: number; shelf: number }[]) {
  // Coordinates mapped to warehouse aisle grid
  const pickLocations = itemsToPick.map(item => ({
    id: item.id,
    x: item.aisle * 3.5, // 3.5m between aisles
    y: item.bay * 1.2,    // 1.2m between bays
  }));

  // Compute optimized TSP route with S-Shape 2-Opt heuristic
  const optimizedRoute = optimizeSshape2OptRoute(pickLocations, { startAtAisle1: true });
  const totalDistanceMeters = calculateWarehouseWalkingDistance(optimizedRoute);

  return {
    route: optimizedRoute,
    totalDistanceMeters,
  };
}

📊 Production Scale Benchmark & VerificationCopy Anchor Link