Genetic Algorithm Scheduling with Fixed-Task Pre-Screening

Solution Overview

Problem

Current genetic-algorithm-based scheduling systems are wasteful of computing resources due to considering all possible tasks and resources without initial consideration of their scheduling flexibility, leading to inefficiencies in computational resources and time.

Innovation Solution

A computing device identifies and removes fixed tasks with little or no scheduling flexibility before executing the genetic algorithm, generating genomes from the remaining tasks, and schedules these tasks using heuristics to optimize resource utilization.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If all possible tasks are added to the genome without initial consideration of scheduling flexibility, then the genetic algorithm can consider all tasks and resources, but computing resources are wasted considering tasks with no scheduling flexibility

Engineering Contradiction:

Improvescheduling flexibilityVSAvoidcomputing resources

Core Design Contradiction:

Adaptability or versatilityVSLoss of energy

Solution Approach 1:

The system performs preliminary identification and removal of fixed tasks before executing the genetic algorithm. By pre-processing the task list to separate fixed tasks from flexible tasks, the system avoids wasting computing resources on tasks that cannot be optimized through genetic algorithm variations.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system extracts and removes fixed tasks from the pool of tasks that will be processed by the genetic algorithm. This extraction is based on identifying tasks with no scheduling flexibility (fixed tasks) and separating them from flexible tasks, thereby reducing the search space for the genetic algorithm.

Inventive Principle:

Principle #2Taking out (Extraction)

2Productivity

If all tasks are considered in the genetic algorithm, then comprehensive schedule optimization is achieved, but time is wasted considering tasks with fixed schedules

Engineering Contradiction:

Improveschedule generation efficiencyVSAvoidcomputational time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system performs preliminary identification and removal of fixed tasks before executing the genetic algorithm. By pre-processing the task list to separate fixed tasks from flexible tasks, the system avoids wasting computing resources on tasks that cannot be optimized through genetic algorithm variations.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system extracts and removes fixed tasks from the pool of tasks that will be processed by the genetic algorithm. This extraction is based on identifying tasks with no scheduling flexibility (fixed tasks) and separating them from flexible tasks, thereby reducing the search space for the genetic algorithm.

Inventive Principle:

Principle #2Taking out (Extraction)

3Loss of energy

If fixed tasks are identified and removed before genome generation, then computing resources are optimized, but the process complexity increases due to additional identification steps

Engineering Contradiction:

Improvecomputing resourcesVSAvoidprocess complexity

Core Design Contradiction:

Loss of energyVSDevice complexity

Solution Approach 1:

The system performs preliminary identification and removal of fixed tasks before executing the genetic algorithm. By pre-processing the task list to separate fixed tasks from flexible tasks, the system avoids wasting computing resources on tasks that cannot be optimized through genetic algorithm variations.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system extracts and removes fixed tasks from the pool of tasks that will be processed by the genetic algorithm. This extraction is based on identifying tasks with no scheduling flexibility (fixed tasks) and separating them from flexible tasks, thereby reducing the search space for the genetic algorithm.

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS20250348354A1Pre-scheduling optimization for computer-implemented genetic algorithms

Publication Date: 2025.11.13 NMETRIC LLC

AI summary

A system for increasing the computational efficiency of a scheduling system applying genetic algorithms. A computing device of the scheduling system collects a plurality of jobs, each having a plurality of tasks. The computing device then proceeds to locate one or more fixed tasks among all of the collected tasks and removes the fixed tasks from the pool of tasks to be scheduled. The computing device blocks off time and resources associated with the fixed tasks, and then proceeds to generate a genome from the remaining tasks. The genomes are then used by a genetic algorithm executed by the computing device to generate a schedule.