Genetic Algorithm Task Scheduling Engine for Resource Optimization
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Solution Overview
Problem
Current scheduling systems face inefficiencies due to the large number of combinations of task allocations and priorities, which often require manual tracking and introduce human error, and lack the ability to automatically manage resource attributes beyond simple availability.
Innovation Solution
A task-oriented computer system utilizing genetic algorithms to recombine and mutate task schedules based on resource attributes, allowing for the optimization of task allocations and priorities to increase overall efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual tracking and allocation of employees to tasks is performed, then task allocation can be visualized and organized, but human error is introduced and efficiency is reduced
Solution Approach 1:
The system enables automatic self-scheduling by allowing the computer system to autonomously allocate tasks to employees and resources based on predefined attributes and constraints, eliminating the need for manual human intervention in the scheduling process while maintaining high accuracy through automated decision-making algorithms
Solution Approach 2:
The patent replaces the mechanical human manual tracking system with an automated computer-based system that uses algorithms to evaluate resource attributes, generate schedules, and allocate tasks automatically, thereby eliminating human error while improving scheduling efficiency through rapid computational processing
2Productivity
If the number of task allocation combinations is increased to maximize efficiency, then better scheduling solutions can be found, but the complexity of selecting the optimal combination increases
Solution Approach 1:
The system changes the approach from evaluating individual task allocation combinations to evaluating resource attributes as key parameters. By defining specific resource attributes (skills, availability, location, etc.) as evaluation criteria, the system simplifies the selection process while still exploring multiple allocation possibilities through structured attribute-based matching
Solution Approach 2:
The patent segments the complex scheduling problem into distinct components: resource attribute definition, task requirement specification, constraint setting, and automated evaluation. This segmentation allows the system to handle multiple task allocation combinations systematically by evaluating them against predefined attribute criteria rather than requiring manual assessment of each combination
3Productivity
If resource attributes beyond simple availability are considered, then more effective allocation can be achieved, but the complexity of tracking and managing resource attributes increases
Solution Approach 1:
The system implements a universal attribute management framework where all resource attributes (skills, availability, location, equipment, etc.) are handled through a single integrated computer-based platform. This multi-functional system can store, retrieve, evaluate, and update any type of resource attribute uniformly, eliminating the need for separate tracking mechanisms for different attribute types while enabling comprehensive resource evaluation
Data Source
AI summary
In a method for handling a plurality of heuristics for task selection in a genetic algorithm, a task scheduling engine generates a population of tasks associated with an overall objective, identifies multiple jobs associated with an overall objective, compiles the multiple jobs into a genome, and assigns one or more tasks to each job of the multiple jobs. The task scheduling engine also assigns a task heuristic byte defining multiple task heuristics that can be applied to the each job of the genome, randomly assigns a task heuristic from the multiple task heuristics to the each job, and determines a value score for the genome.