Machine Tool Sequencing With Combinatorial Setup Optimization
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Solution Overview
Problem
Manufacturing industries face significant challenges due to lengthy setup times in machine tool workstations, which lead to increased inventory, reduced efficiency, and poor on-time delivery, especially in high-mix low-volume production environments.
Innovation Solution
The system employs combinatorial optimization problem solvers to group machine tool workstations into functional groups, determine optimal sequences of parts processing, and reduce setup times by utilizing data from sensors and applying Little's Law to ensure timely delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine tools are frequently reconfigured to process different part types, then product variety and adaptability increase, but setup time and production cost increase
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing optimal processing sequences for different part combinations before actual production. The combinatorial optimization solver pre-determines the best processing order for groups of parts, so when production begins, the machine tools can immediately execute the pre-planned sequence without time-consuming setup calculations during operation.
Solution Approach 2:
The system implements dynamic sequencing where the processing order of parts is not fixed but adaptively determined based on current machine tool configurations, part requirements, and production priorities. The combinatorial optimization solver dynamically generates different processing sequences for different functional groups of machine tools, allowing the system to adapt to changing conditions while minimizing setup time.
2Loss of time
If batch size is increased to reduce setup frequency, then setup time per unit decreases, but finished goods inventory increases
Solution Approach 1:
The system segments the production process by dividing parts into different functional groups that can be processed by specific groups of machine tools with similar configurations. This segmentation allows for smaller, more targeted batches of parts to be processed efficiently together, reducing the need for large inventory builds while still achieving economies of scale by minimizing setup frequency within each functional group.
3Loss of time
If combinatorial optimization solver is used to determine processing sequences, then setup time is reduced, but computational complexity and system complexity increase
Solution Approach 1:
The system segments the overall optimization problem by dividing machine tools into functional groups and parts into corresponding groups that require similar processing. This segmentation transforms a single complex combinatorial optimization problem involving all machine tools and parts into multiple smaller, independent optimization problems for each functional group, significantly reducing computational complexity while still achieving optimal sequences.
Solution Approach 2:
The system implements a dynamic, iterative optimization approach where the combinatorial solver continuously refines processing sequences based on feedback from actual production data, setup time measurements, and changing part priorities. This dynamic adjustment allows the system to adapt to real-world variations while maintaining optimal performance without requiring complete re-optimization of the entire production system.
Data Source
AI summary
Methods, systems and apparatus, including computer programs encoded on computer storage medium, for processing parts using machine tool workstations. The machine tool workstations are grouped into multiple groups, where each group includes workstations of the same type. For each of the multiple groups, data representing parts to be processed by the type of workstations included in the group are transmitted to a combinatorial optimization problem solver. Data representing a sequence of ordered subsets of the multiple parts that produce a reduced setup time for the workstations in the group are received from the solver. An average delay time associated with the generated sequence of ordered subsets of the collection of parts is computed. If the average delay time is less than the predetermined threshold, parts are released according to the generated sequence of ordered subsets of the collection of parts to the group for processing.