Workflow Scheduling with Hidden Constraints and Segmentation
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Solution Overview
Problem
Current workforce management systems face challenges in optimizing employee schedules to achieve workflow goals due to constraints such as time, resource availability, and workflow complexities, often resulting in suboptimal scheduling and potential bottlenecks.
Innovation Solution
A method that defines work queues and processes, uses graphical interfaces to create and link work queues, applies optimization algorithms like Hill-Climbing, and introduces hidden constraints to modify scheduling parameters, ensuring that schedules meet performance metrics and achieve overarching goals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional workforce management systems are used to create employee schedules, then scheduling can be performed with basic constraints, but the schedules fail to achieve workflow goals and create bottlenecks due to suboptimal resource allocation
Solution Approach 1:
The workflow is segmented into multiple work queues representing different tasks or stages. Each work queue can be independently analyzed and optimized, allowing the scheduling system to manage complexity by breaking down the overall workflow into manageable segments while still achieving global optimization of workflow efficiency
Solution Approach 2:
The system changes scheduling parameters dynamically by introducing hidden constraints that modify traditional scheduling approaches. These parameter changes enable the system to optimize for workflow goals rather than just individual task completion, improving overall productivity while managing complexity through algorithmic optimization
2Reliability
If more constraints are added to the scheduling system to account for workflow complexities, then schedule optimization improves, but the system becomes more complex and difficult to manage
Solution Approach 1:
Hidden constraints act as intermediaries between the visible workflow requirements and the underlying optimization algorithms. These intermediary constraints translate complex workflow goals into manageable scheduling parameters, improving schedule optimization quality while shielding users from the full complexity of the optimization process
Solution Approach 2:
The system uses simulation to provide feedback on schedule performance against workflow goals. This feedback mechanism allows the system to iteratively refine schedules by adjusting hidden constraints based on simulated outcomes, improving reliability while managing complexity through automated optimization loops
3Manufacturing precision
If simulation and iterative optimization are performed to meet acceptance criteria, then schedule quality improves, but the time required to generate schedules increases
Solution Approach 1:
The system performs preliminary simulation and optimization iterations before final schedule generation. By pre-calculating optimal assignments using hidden constraints and simulation feedback, the system achieves high schedule accuracy while reducing the time required for final schedule deployment through automated iterative refinement
Data Source
AI summary
A system and method schedule work within a workflow with defined process goals. A plurality of work queues are defined that comprise work items. The plurality of work queues are associated with one or more links between a parent work queue and at least one child work queue to form at least one work process. At least one work process goal is defined for each work process. A work schedule to achieve the work process goals is generated.