Hybrid Workforce Task Assignment Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current industrial systems are inefficient in optimizing the combination of human and robotic workforces to complete tasks, as they primarily focus on robotic capabilities without considering the complementary skills and availability of human workers, leading to suboptimal productivity and increased costs.
Innovation Solution
A computer-implemented method that identifies tasks suitable for human or robotic workers, creates profiles for each worker, determines capabilities, and associates tasks with the most capable workers, using digital twin simulations and machine learning models to optimize task assignment and instruction delivery within an industrial environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If tasks are assigned based solely on robotic capabilities, then robotic workers can perform tasks independently, but human workers with complementary skills are underutilized, reducing overall productivity
Solution Approach 1:
The system creates a universal task assignment framework that considers both robotic and human worker capabilities. The capability determination module evaluates multiple worker types against task requirements, and the assignment module distributes tasks across the hybrid workforce based on comprehensive capability profiles, enabling each worker type to perform tasks where they provide the most value.
Solution Approach 2:
The system changes the parameter used for task assignment from solely robotic capability metrics to a composite evaluation that includes human worker attributes such as analytical skills, strength, and availability. The capability score is dynamically calculated based on multiple parameters including worker profiles, task classifications, and real-time availability data.
2Measurement precision
If comprehensive worker profiles and capability assessments are conducted, then task assignment accuracy improves, but system complexity and time required for optimization increases
Solution Approach 1:
The system performs preliminary actions by pre-establishing worker profiles that capture capability attributes before task assignment occurs. Historical performance data and worker characteristics are stored in advance, enabling rapid capability determination when tasks arise without requiring complex real-time assessments.
Solution Approach 2:
The system creates digital representations of worker capabilities through standardized profiles that can be quickly referenced during task assignment. These profile copies contain pre-processed capability data, allowing the assignment module to rapidly match tasks with appropriate workers without re-evaluating all worker attributes from scratch.
3Productivity
If task classification and capability determination are performed for all workers, then optimal task distribution is achieved, but computational resources and processing time are consumed
Solution Approach 1:
The system segments the task assignment process into distinct modular components: task identification, task classification, capability determination, and assignment. Each module processes specific information independently, allowing the system to handle complex evaluations efficiently by dividing the computational workload into manageable segments that can be processed in parallel.
Data Source
AI summary
A computer-implemented method, a computer system and a computer program product optimize a hybrid workforce to complete tasks in an environment. The method includes identifying a task to be completed by the hybrid workforce in the environment, where the hybrid workforce includes both humans and robots. The method also includes classifying the task according to suitability for human completion or robot completion. The method further includes obtaining a profile for each worker in the hybrid workforce and determining a capability of each human and each robot to perform the task from the profile. In addition, the method includes associating the task with a robot having the highest capability when the task is suitable for robot completion. Lastly, the method includes displaying an assignment of the task on a device associated with the environment, where the assignment includes a classification of the task and an association with the hybrid workforce.