Wave Planning Optimizer
SkillDev toolsAutomated wave planning and pick path optimization skill to maximize warehouse throughput and order accuracy
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the Wave Planning Optimizer skill
What this skill tells your AI
The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/domains/business/logistics/skills/wave-planning-optimizer/SKILL.md and read by ahel’s review.
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
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
Signals
- GitHub stars
- 2k
- Forks
- 112
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
wave-planning-optimizer- Source
- github.com/a5c-ai/babysitter
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