wave-planning-optimizer

Automated wave planning and pick path optimization skill to maximize warehouse throughput and order accuracy

509 stars

Best use case

wave-planning-optimizer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Automated wave planning and pick path optimization skill to maximize warehouse throughput and order accuracy

Teams using wave-planning-optimizer should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/wave-planning-optimizer/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/domains/business/logistics/skills/wave-planning-optimizer/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/wave-planning-optimizer/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How wave-planning-optimizer Compares

Feature / Agentwave-planning-optimizerStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Automated wave planning and pick path optimization skill to maximize warehouse throughput and order accuracy

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Wave Planning Optimizer

## Overview

The Wave Planning Optimizer is an automated skill that optimizes wave planning and pick path sequencing to maximize warehouse throughput and order accuracy. It intelligently groups orders into waves, balances workloads, and coordinates with carrier cutoff times to ensure efficient fulfillment operations.

## Capabilities

- **Wave Release Optimization**: Determine optimal wave sizes and release timing based on capacity, demand, and carrier schedules
- **Batch Picking Strategies**: Group orders into efficient batches based on location proximity, order similarity, and resource availability
- **Pick Path Sequencing**: Optimize the sequence of picks within a batch to minimize travel distance
- **Carrier Cutoff Coordination**: Align wave releases with carrier pickup schedules and service commitments
- **Resource Capacity Balancing**: Distribute work evenly across available pickers and zones to prevent bottlenecks
- **Zone Picking Orchestration**: Coordinate picks across multiple zones for efficient zone-based picking strategies
- **Pick Density Optimization**: Maximize picks per travel unit by optimizing batch composition

## Tools and Libraries

- WMS Systems
- Optimization Algorithms
- Scheduling Tools
- Resource Planning Libraries

## Used By Processes

- Pick-Pack-Ship Operations
- Receiving and Putaway Optimization
- Warehouse Labor Management

## Usage

```yaml
skill: wave-planning-optimizer
inputs:
  orders:
    - order_id: "ORD001"
      lines: 3
      priority: "standard"
      carrier_cutoff: "14:00"
      zone_requirements: ["ZONE_A", "ZONE_B"]
    - order_id: "ORD002"
      lines: 5
      priority: "expedited"
      carrier_cutoff: "12:00"
      zone_requirements: ["ZONE_A"]
  resources:
    available_pickers: 10
    picker_capacity_lines_per_hour: 60
  constraints:
    max_wave_size: 200
    batch_size_target: 12
    planning_horizon_hours: 4
outputs:
  waves:
    - wave_id: "WAVE001"
      release_time: "08:00"
      orders: ["ORD002", "ORD003", "ORD004"]
      total_lines: 45
      estimated_completion: "09:30"
      assigned_pickers: 3
      batches:
        - batch_id: "BATCH001"
          orders: ["ORD002"]
          pick_sequence: ["A-01-02", "A-03-05", "A-04-01"]
    - wave_id: "WAVE002"
      release_time: "09:30"
      orders: ["ORD001", "ORD005"]
      total_lines: 38
      estimated_completion: "11:00"
      assigned_pickers: 3
  metrics:
    total_waves: 2
    average_batch_size: 10.5
    estimated_throughput_lines_per_hour: 85
```

## Integration Points

- Warehouse Management Systems (WMS)
- Order Management Systems
- Labor Management Systems
- Transportation Management Systems (TMS)
- Carrier Systems

## Performance Metrics

- Lines picked per hour
- Wave completion rate
- Order cycle time
- Carrier cutoff compliance
- Resource utilization rate

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