Two-Phase Job Scheduling With Filters for Combinatorial Load
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Solution Overview
Problem
Existing 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 sorting and workload balancing between different scheduling mechanisms.
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, reducing computational load and mitigating combinatorial explosion by withholding location-agnostic jobs for later addition.
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 divided into two distinct phases: Phase 1 processes location-specific jobs through the constraint satisfaction solver, while Phase 2 handles location-agnostic jobs separately by inserting them into gaps in the Phase 1 schedule. This segmentation reduces the computational burden on the solver while ensuring all jobs are scheduled.
Solution Approach 2:
Location-agnostic jobs are extracted from the main scheduling problem and handled separately in Phase 2. By removing these jobs from the constraint satisfaction solver's workload and processing them independently through gap insertion, the system reduces combinatorial explosion while maintaining complete job coverage.
2Manufacturing precision
If location-agnostic jobs are included in the first phase scheduling, then comprehensive optimization is achieved, but computational efficiency decreases
Solution Approach 1:
Phase 1 scheduling is performed first with location-specific jobs to establish a baseline optimized schedule. This preliminary action creates a foundation that can be efficiently enhanced in Phase 2 by inserting location-agnostic jobs into identified gaps, avoiding the need to re-optimize the entire schedule computationally.
Solution Approach 2:
Different scheduling approaches are applied to different job types: location-specific jobs receive comprehensive constraint satisfaction optimization in Phase 1, while location-agnostic jobs receive targeted gap insertion optimization in Phase 2. This local quality approach optimizes computational resources by applying appropriate methods to each job category.
3Productivity
If jobs are filtered to reduce the subset size, then computational load is reduced, but some jobs may be excluded from optimal scheduling
Solution Approach 1:
Location-agnostic jobs are temporarily discarded from the main scheduling process in Phase 1 to reduce computational load, then recovered and inserted into the schedule in Phase 2 by placing them in identified time gaps. This ensures no jobs are permanently excluded while maintaining computational efficiency during the complex optimization phase.
Data Source
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.