Genetic Algorithm for Automated Room Object Distribution
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Solution Overview
Problem
Current room and surface planning processes are inefficient and time-consuming, involving multiple technicians and lengthy meetings to optimize space utilization, especially in dynamic environments like factories and offices, where sudden changes require rapid solutions.
Innovation Solution
An automated system using a genetic algorithm processes data on rooms, surfaces, and objects to generate and refine solutions for optimal object distribution, incorporating AI and neural networks to evaluate and recombine solutions based on multiple parameters, facilitating real-time optimization and implementation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple technicians are involved in room planning to consider various aspects (aesthetic, process sequence, supply lines, ventilation, etc.), then the quality and comprehensiveness of the planning solution is improved, but the time required and complexity of the planning process increases significantly
Solution Approach 1:
The planning system segments the complex room planning task into distinct modules: data input modules for room/surface characteristics, object requirements, and boundary conditions; a genetic algorithm module for generating and evaluating layouts; and an output module for presenting optimized solutions. Each module handles specific aspects independently, allowing parallel processing and reducing overall planning time while maintaining comprehensive consideration of all requirements.
Solution Approach 2:
The patent replaces the mechanical system of human technicians deliberating in meetings with an automated computer-based genetic algorithm system. The algorithm automatically generates, evaluates, and optimizes room layouts by processing boundary conditions and object requirements, eliminating the need for lengthy human discussions while producing comprehensive solutions that consider all specified constraints.
2Reliability
If traditional manual planning methods are used with multiple technicians discussing in meetings, then various aspects can be thoroughly reviewed, but the ability to provide rapid solutions when boundary conditions change suddenly is reduced
Solution Approach 1:
The planning system is designed to be dynamic and adaptable. When boundary conditions change suddenly (e.g., during a pandemic), users can input new conditions into the data input modules, and the genetic algorithm rapidly generates new optimized layouts. The system continuously evaluates layouts against updated constraints and can provide multiple alternative solutions, enabling quick adaptation to changing requirements while maintaining thorough review of all aspects through automated evaluation criteria.
3Productivity
If detailed planning is performed to optimize space utilization in enclosed spaces, then the efficiency of space use is improved, but the complexity of the planning process and number of parameters to consider increases
Solution Approach 1:
The genetic algorithm system performs self-service optimization by automatically generating and evaluating numerous layout configurations without requiring manual intervention for each evaluation. The algorithm independently processes boundary conditions, evaluates space utilization efficiency for each generated layout, and identifies optimal solutions, thereby achieving detailed optimization of space usage while keeping the user interface simple and the planning process straightforward.
Data Source
AI summary
Various embodiments of the teachings herein include a system for automated distribution of a number of predetermined objects in a room and/or on a surface. The system may include: a module for inputting and/or generating data; a module for outputting and/or presenting solutions; and interfaces for transmitting the data to a storage unit connected to a processor configured to carry out a genetic algorithm. The genetic algorithm uses the data and initially provides a generation of solutions. The processor evaluates and selects among the solutions based on their progressiveness then recombines the selected solutions. The procedure repeats and provides the most progressive solutions to an artificial intelligence and/or to a neural network which uses the solutions to generate new rules and transfers the most progressive solutions to the processor for refinement of the algorithm.