Dynamic Intelligent Scheduling for Production Lines
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Solution Overview
Problem
In large-scale conventional industries producing small-volume and large-variety products, unexpected situations like machine failures occur frequently, leading to difficulties in generating updated production schedules quickly, resulting in unnecessary costs and damage due to excessive selectable order pools and complex constraint conditions.
Innovation Solution
A dynamic intelligent scheduling method and apparatus that collects resource constraints and decision data, uses a mathematical model with multi-objective weights, and trains a learning model to generate recommended schedules by cross-enumerating schedule combinations with penalty conditions, enabling rapid adaptation to changing production conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If experienced employees perform manual scheduling when unexpected situations occur, then scheduling decisions can be made with expertise, but the scheduling time becomes excessively long and cannot meet urgent production needs
Solution Approach 1:
The system copies the decision-making patterns of experienced schedulers by collecting their decision data and training a learning model to replicate their expertise. This allows the system to make scheduling decisions with quality comparable to experienced employees while dramatically reducing the time required, as the trained model can evaluate schedules instantly without manual intervention.
Solution Approach 2:
The patent replaces the mechanical manual scheduling process with an automated electronic system that uses learning models and mathematical optimization. The system substitutes human cognitive processing with computational algorithms that can evaluate multiple scheduling options simultaneously, achieving both high decision quality through learned patterns and rapid response through automated calculation.
2Adaptability or versatility
If the number of selectable order pools is increased to meet diverse production requirements, then production flexibility improves, but the complexity of scheduling increases making it difficult to determine favorable schedules quickly
Solution Approach 1:
The system dynamically adjusts scheduling strategies based on the specific situation by using a learning model that has been trained on diverse scheduling scenarios. The model adapts to different production requirements, constraint combinations, and unexpected situations, selecting appropriate scheduling approaches from the large order pool without requiring manual analysis of each complex scenario.
Solution Approach 2:
The patent changes the parameters of the scheduling problem by transforming it into a mathematical optimization problem with defined objective functions and constraints. The learning model learns optimal parameter settings for different situations, and the system uses penalty conditions to manage the complexity of large order pools by prioritizing based on learned criteria rather than exhaustive analysis.
3Manufacturing precision
If manual scheduling with extensive experience is used to handle complex constraint conditions, then scheduling accuracy can be maintained, but the cost of relying heavily on experienced personnel increases and creates vulnerability
Solution Approach 1:
The system copies the expertise of experienced schedulers by collecting their decision data and training a learning model to replicate their decision-making patterns. This preserves the high scheduling accuracy that experienced personnel provide while eliminating the vulnerability of dependence on individual employees, as the knowledge is captured in the trained model that can be consistently applied without human fatigue or absence.
Solution Approach 2:
The scheduling system serves itself by using the learned model to automatically generate and evaluate schedules without requiring continuous human intervention. The system captures and utilizes the expertise embedded in historical decision data, allowing it to maintain high accuracy independently while reducing the need for experienced personnel to manually handle each scheduling situation.
Data Source
AI summary
A method for dynamic intelligent scheduling includes following steps: collecting and recording resource constraints of multiple schedules on a production line and decision data of changes made to the schedules by a scheduler; cross-enumerating schedule combinations by using multiple production goals as penalty conditions; establishing a mathematical model based on the resource constraints and multi-objective weights corresponding to each schedule combination and importing the resource constraints to calculate schedule results; recording the penalty condition corresponding to the schedule combination matching the decision data as a valid penalty; using values of parameters corresponding to the valid penalty and values of the penalty conditions respectively as inputs and outputs to train a learning model; and responding to a scheduling request, finding a weight of each schedule combination by using the learning model according to the resource constraint of the current schedule and the production goals, and generating a recommended schedule accordingly.