Autonomous Labor Assignment Across People, Robots, and MHE

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Solution Overview

Problem

Warehouse management systems face challenges in efficiently managing and integrating tasks among robots, material handling equipment, and human workers due to complexity and high costs, requiring manual decisions and complicated integrations.

Innovation Solution

The Autonomous Labor Intelligent Dynamic Assignment (ALIDA) system autonomously determines and distributes work assignments across people, robots, and material handling equipment using real-time factors such as credentials, proximity, and task availability, eliminating the need for manual decisions and integrating these resources for improved efficiency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual decisions and complicated integrations are used to manage tasks among robots, material handling equipment, and human workers, then system complexity and integration costs increase, but task coordination and resource allocation remain inefficient

Engineering Contradiction:

Improvetask coordination efficiencyVSAvoidsystem integration complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent introduces an autonomous labor intelligent dynamic assignment system as an intermediary layer between human workers, robots, and material handling equipment. This system uses machine learning models to automatically allocate tasks and coordinate resources, eliminating the need for manual decision-making while reducing integration complexity through standardized APIs and interfaces.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces manual mechanical coordination systems with an automated intelligent assignment system. The machine learning-based platform automatically processes task allocations, resource scheduling, and coordination decisions, substituting human manual operations with algorithmic automation that reduces both complexity and improves efficiency.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If autonomous intelligent assignment systems are implemented to manage work tasks, then task allocation efficiency and resource utilization improve, but system implementation complexity and computational requirements increase

Engineering Contradiction:

Improveresource utilization efficiencyVSAvoidsystem implementation complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent segments the autonomous assignment system into modular components including task management modules, resource tracking modules, machine learning model modules, and communication interfaces. Each module performs a specific function and can be independently developed, tested, and deployed, reducing overall implementation complexity while maintaining high resource utilization efficiency.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent designs a universal autonomous assignment platform that can manage multiple types of resources (human workers, robots, material handling equipment) and perform multiple functions (task allocation, scheduling, tracking, optimization) through a single integrated system, reducing implementation complexity compared to multiple separate systems.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Speed

If real-time monitoring and dynamic reassignment capabilities are added to the system, then task completion speed and adaptability improve, but computational load and processing time increase

Engineering Contradiction:

Improvetask completion speedVSAvoidcomputational energy consumption

Core Design Contradiction:

SpeedVSUse of energy by moving object

Solution Approach 1:

The patent implements periodic monitoring and evaluation cycles where the system assesses task progress and resource status at predetermined intervals rather than continuously. This allows real-time responsiveness while reducing computational energy consumption by processing data only when necessary to maintain optimal task completion speed.

Inventive Principle:

Principle #19Periodic action

Solution Approach 2:

The patent pre-calculates and stores optimal task allocation strategies and resource assignment rules before runtime. When tasks need to be assigned or reassigned, the system retrieves pre-computed solutions rather than performing full optimization calculations in real-time, reducing computational energy consumption while maintaining fast task completion speeds.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20250315750A1Systems and methods for autonomous labor intelligent dynamic assignment

Publication Date: 2025.10.09 JASCI LLC

AI summary

The present solution in which some embodiments is referred to as Autonomous Labor Intelligent Dynamic Assignment (ALIDA) solves the logistics industry challenges by smartly managing work assignments and distributing that work to people, robots and material handling equipment “MHE” for improved efficiency and utilization. The systems eliminates the need for manual decisions and complicated integrations. The system can also be applied to but not limited to manufacturing operations and healthcare facilities.