Production Control System Using Tabu Search for Dynamic Scheduling
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Solution Overview
Problem
Conventional methods for controlling production processes on multiple machines require generating new scheduling plans from scratch in response to unplanned events, which can take several hours, leading to inefficiencies and inflexibility.
Innovation Solution
The method considers both order data and real-time state data of production machines, including their mode, production sequences, resource availability, and actual production know-how, to anticipate and account for exceptions proactively when generating control instructions, using a tabu search algorithm to optimize machine utilization and cost management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional disposition methods generate scheduling plans based purely on order data without considering real-time machine state, then the planning process is simpler and faster initially, but the system cannot react flexibly to unplanned events and requires complete regeneration of scheduling plans taking several hours
Solution Approach 1:
The system performs preliminary actions by proactively identifying potential exceptions and preparing alternative scheduling plans before they are needed. The disposition system continuously monitors production processes and pre-calculates alternative schedules, so when an exception occurs, the pre-prepared alternatives can be immediately deployed without requiring complete plan regeneration
Solution Approach 2:
The system transitions from static scheduling plans to dynamic adaptive planning. By continuously integrating real-time machine state data and order data, the disposition system dynamically adjusts scheduling plans to reflect current production conditions, enabling flexible response to exceptions while maintaining up-to-date planning information
2Productivity
If the system continuously monitors and integrates real-time state data of production machines when generating control instructions, then the system can anticipate exceptions and react faster, but the complexity of data processing and plan generation increases
Solution Approach 1:
The disposition system performs multiple functions: it monitors machine state data, processes order data, identifies potential exceptions, generates alternative scheduling plans, and selects optimal plans. This multi-functional approach consolidates what would otherwise require separate systems into a single integrated disposition system, managing complexity through functional consolidation
3Ease of manufacture
If conventional methods use purely additive capacity planning based on order data, then the planning process is more straightforward, but machine utilization cannot be optimized and economic efficiency is reduced
Solution Approach 1:
The system implements feedback by continuously monitoring actual machine state data and comparing it with the scheduling plan. This feedback loop enables the disposition system to identify deviations, anticipate exceptions, and adjust plans accordingly, transforming the previously open-loop additive planning into a closed-loop system that optimizes machine utilization while maintaining planning simplicity
Data Source
AI summary
A method for controlling a plurality of production processes performed on a plurality of production machines is described, wherein order data regarding the production processes to be performed are inputted in a control unit which generates control instructions for the production machines, wherein state data indicating the state of the individual production machines are taken into consideration when generating the control instructions.