Operator State-Aware Scheduling for Fatigue Reduction
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Solution Overview
Problem
Current information processing devices for operation scheduling cannot consider the physical and skill states of operators, leading to inefficient allocation of operations.
Innovation Solution
An information processing device that acquires operation cost information and state information from sensors and historical data to predict the time required for operators to perform tasks and allocate operations based on these factors, using a hardware processor with modules for acquisition, prediction, and allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If operation scheduling is performed without considering operator states, then scheduling can be done simply and quickly, but operator fatigue increases and productivity decreases
Solution Approach 1:
The system performs preliminary acquisition and analysis of operator state information (physical condition, skill level, fatigue degree) before scheduling operations. This allows the scheduling to be based on pre-analyzed data, making the process efficient while still considering operator states.
Solution Approach 2:
The system introduces an intermediary scheduling management unit that acts between the operation requirements and operator allocation. This intermediary analyzes operator states and matches them with suitable operations, resolving the contradiction by adding intelligence without requiring complete system redesign.
2Productivity
If operation scheduling considers operator states, then productivity and task completion efficiency improve, but the complexity of the scheduling system increases
Solution Approach 1:
The scheduling system is segmented into distinct functional modules: operator state acquisition unit, state analysis unit, operation scheduling unit, and monitoring unit. Each module handles a specific aspect of the problem, making the overall complex system manageable and maintainable while achieving high task completion efficiency.
Solution Approach 2:
The system changes the parameter of operator state (from unconsidered to considered) by acquiring and analyzing physical condition, skill level, and fatigue degree. This parameter change enables optimized scheduling that improves task completion efficiency without requiring complete system replacement.
3Ease of operation
If uniform scheduling is applied to all operators, then scheduling management is simplified, but operator fatigue increases and operating efficiency decreases
Solution Approach 1:
The system applies local quality by tailoring scheduling to individual operator characteristics (physical condition, skill level, fatigue degree) rather than applying uniform scheduling to all. The scheduling management unit analyzes each operator's specific state and allocates operations accordingly, achieving both ease of management through automation and high operating efficiency through personalized matching.
Data Source
AI summary
According to one embodiment, an information processing device includes a hardware processor configured to acquire operation cost information indicative of a relationship between a state of an operator and a period of time required for the operator to perform an operation from a storage that stores the operation cost information, acquire state information indicative of a state of a target operator, and calculate a period of time required for the target operator to perform a target operation based on the operation cost information and the state information.