Genetic Algorithm Resource Allocation via Priority Encoding
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Solution Overview
Problem
Existing resource allocation methods using genetic algorithms often result in inexecutable solutions due to conflicts in resource allocation and order relationships among processes, leading to inefficiencies in finding feasible and optimal solutions, especially when dealing with complex time intervals and resource constraints.
Innovation Solution
A method that prioritizes processes and allocates resources in descending order of priority, using priority information to determine the allocation order instead of direct allocation, ensuring that only executable processes with available resources are selected and allocated resources sequentially.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If direct allocation information is used for genes in GA, then the representation is straightforward, but inexecutable solutions occur due to resource conflicts and process order violations
Solution Approach 1:
The patent introduces priority values as an intermediary representation instead of direct allocation information. Genes store priority values that determine the order of process execution, while the actual resource allocation is derived indirectly through evaluation. This mediator approach ensures that all generated solutions are executable by design, eliminating infeasible solutions caused by resource conflicts or order violations.
2Device complexity
If fixed time slots are used for process allocation, then the schedule structure is simple, but the linear representation of time and time slot is insufficient
Solution Approach 1:
The patent transitions from static fixed time slot allocation to dynamic priority-based scheduling. Instead of assigning processes to predetermined time slots, the system uses priority values that dynamically determine execution order and resource allocation. This dynamic approach maintains simple gene representation while significantly improving adaptability to different time requirements and resource availability.
3Reliability
If all combinations of gene arrays are evaluated, then complete search is performed, but calculation efficiency is low due to rarity of feasible solutions
Solution Approach 1:
The patent applies preliminary anti-action by designing the gene representation and evaluation function to prevent infeasible solutions from being generated in the first place. Through priority-based encoding and constrained evaluation, the system proactively eliminates the possibility of resource conflicts and order violations, making all searched solutions feasible by design rather than filtering them afterward.
4Device complexity
If resource allocation is optimized without considering process order, then calculation is simpler, but practical applicability is reduced
Solution Approach 1:
The patent incorporates process order constraints preliminary into the evaluation function itself. Rather than adding complex constraints during optimization, the evaluation function pre-establishes the correct execution order based on process dependencies and resource availability. This allows the GA to focus on priority optimization while automatically respecting process order requirements, maintaining simplicity while ensuring practical applicability.
Data Source
AI summary
To provide a more efficient resource allocation method and system using a genetic algorithm (GA). The present technology includes a method for allocating resources to a production process including a plurality of processes, the method including allocating priorities to the plurality of processes, selecting processes executable at a first time among the plurality of processes and capable of allocating necessary resources, allocating the necessary resources to the selected processes in descending order of priorities, selecting processes executable at a second time that is later than the first time among the plurality of processes and capable of allocating necessary resources, and allocating the necessary resources to the selected processes in descending order of priorities. The present technology also includes, as a method of expressing genes of GA, not having direct allocation information for genes but having information (priority) for determining an order for allocation.