Genetic Algorithm Task Dispatching for Examining Apparatus
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Solution Overview
Problem
Current hospital systems allocate examining apparatuses based on a 'First Come First Serve' model, leading to inefficient operation durations where some apparatuses are overburdened while others remain idle, and fail to consider the examination types of successive patients, resulting in unnecessary switching and waste.
Innovation Solution
A task dispatching method that generates a genetic sequence to optimize the allocation of examination tasks to examining apparatuses by determining apparatus operation duration and task-switching parameters, using iterative updating to select a high-quality sequence that minimizes operation and switching times, thereby improving resource utilization and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a 'First Come First Serve' model is used to allocate examining apparatuses, then the allocation process is simple, but some apparatuses are overburdened while others remain idle, leading to inefficient resource utilization
Solution Approach 1:
The patent transforms the task allocation problem into a parameter optimization problem by encoding task sequences as genetic chromosomes and evolving them through selection, crossover, and mutation operations. The fitness function evaluates apparatus operation duration and task-switching parameters, dynamically adjusting the allocation parameters to optimize resource utilization while maintaining manageable complexity through automated evolution.
Solution Approach 2:
The patent replaces the manual or simple queue-based 'First Come First Serve' mechanical allocation system with a computational genetic algorithm system. This substitution uses information processing and iterative optimization to achieve superior resource utilization, trading operational simplicity for significantly improved productivity and efficiency.
2Productivity
If examining apparatuses process different examination types in sequence, then comprehensive task completion is achieved, but unnecessary switching between task types occurs, resulting in wasted time and reduced efficiency
Solution Approach 1:
The patent optimizes the task sequence parameters by evaluating fitness based on task-switching parameters and apparatus operation duration. The genetic algorithm evolves chromosome representations of task sequences to minimize switching time between different examination types, dynamically adjusting the sequence parameters to reduce wasted time while ensuring all tasks are completed.
Solution Approach 2:
The patent aims to maintain continuous useful action by minimizing idle switching time between tasks. Through genetic optimization, the system generates task sequences that reduce interruptions and transitions between different examination types, keeping apparatuses continuously engaged in productive work and eliminating unnecessary downtime.
3Productivity
If the allocation system considers apparatus operation duration and task-switching parameters, then resource optimization is improved, but the calculation complexity and time required for optimization increase
Solution Approach 1:
The patent employs a dynamic genetic algorithm that adapts through iterative evolution. The system starts with random task sequences and progressively evolves them through selection, crossover, and mutation operations, with the fitness function dynamically evaluating apparatus operation duration and task-switching parameters. This dynamic approach achieves optimal resource allocation without requiring complex upfront calculations.
Solution Approach 2:
The genetic algorithm is self-directed, automatically optimizing task sequences without requiring external intervention or complex control mechanisms. The fitness function guides the evolution process, with the system self-correcting and improving task allocations through iterative generations, reducing the need for complex external calculation and control systems.
Data Source
AI summary
A task dispatching method and apparatus, a computing and processing device, a computer program and a computer-readable medium. The task dispatching method includes: acquiring a plurality of examination tasks to be dispatched and one or more examining apparatuses configured for processing the plurality of examination tasks, wherein the examination tasks have different task identifications; according to the examination tasks and the examining apparatus, generating a genetic sequence, the genetic sequence includes a correspondence relation between the examination tasks and the examining apparatuses processing the examination tasks, and genes in the genetic sequence are the task identifications of the examination tasks; and performing iterative updating to the genetic sequence, till an iteration stopping condition is satisfied, and according to the genetic sequence that has been updated, dispatching the examination tasks.