Two-Phase Job Scheduling to Avoid Combinatorial Explosion
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Solution Overview
Problem
Job scheduling systems face computational inefficiencies due to combinatorial explosion, particularly when scheduling jobs with location-agnostic and location-specific constraints, and there is a need to optimize job selection and scheduling without administrative changes.
Innovation Solution
A two-phase job scheduling method (JSEF) that forms a subset of jobs using filters, submits them to a scheduler, and updates the schedule to include withheld jobs, balancing workload between constraint satisfaction and other mechanisms, and distinguishing between location-agnostic and location-specific jobs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all jobs are submitted to the constraint satisfaction solver for scheduling, then complete job coverage is achieved, but computational complexity increases due to combinatorial explosion
Solution Approach 1:
The scheduling process is segmented into two distinct phases: Phase 1 uses a constraint satisfaction solver for a subset of jobs, while Phase 2 handles the remaining jobs using a different approach. This segmentation divides the computationally expensive task into manageable portions, reducing the combinatorial explosion in the constraint solver while ensuring all jobs are eventually scheduled.
Solution Approach 2:
Jobs are pre-filtered and categorized into different phases before scheduling. The system performs preliminary actions by identifying which jobs should be handled in Phase 1 versus Phase 2, allowing the constraint satisfaction solver to focus only on the most critical or complex jobs, thereby reducing overall computational complexity.
2Adaptability or versatility
If location-agnostic jobs are included in the constraint satisfaction solver, then scheduling flexibility is maintained, but computational efficiency decreases
Solution Approach 1:
Location-agnostic jobs are extracted from the constraint satisfaction solver and handled separately in Phase 2. This extraction removes the computationally burdensome element from the solver while preserving scheduling flexibility, as these jobs can be assigned to any location without constraining the overall schedule.
Solution Approach 2:
Different scheduling approaches are applied to different job types: location-specific jobs receive the rigorous constraint satisfaction treatment, while location-agnostic jobs receive a simpler scheduling approach. This local quality differentiation optimizes computational efficiency without sacrificing overall scheduling flexibility.
3Manufacturing precision
If multiple filtering passes are applied to jobs, then job selection optimization improves, but processing time increases
Solution Approach 1:
The filtering process is organized into periodic phases rather than continuous sequential filtering. Phase 1 applies filters to identify candidate jobs, then Phase 2 applies additional filters to the remaining jobs. This periodic structure allows the system to balance filtering thoroughness with processing time by distributing filter application across phases.
Data Source
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AI summary
A job scheduling technology computationally selects jobs from a set, excluding at least one job by applying at least one job filter, submits the selected jobs to a scheduler mechanism, receives in response a first phase schedule, and produces a second phase schedule updating the first phase schedule to include at least one excluded job. The update avoids computational costs of combinatorial explosion. In some scenarios, a location-agnostic job is excluded from the first phase schedule and then included in the updated schedule. In some scenarios, applying the filters sorts the jobs for submission to the scheduler mechanism.