Production Resource Allocation Using Iterative Machine Priorities
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Solution Overview
Problem
Large production facilities face challenges in efficiently allocating resources to machines of different generations, leading to suboptimal on-time delivery and increased maintenance costs due to inefficient capacity utilization.
Innovation Solution
A method utilizing a resource allocation system within a manufacturing execution system that generates resource-machine combinations based on forecast data, demand deviations, and priority rules to create a roll-out plan that minimizes the number of resource-machine combinations needed, optimizing resource allocation and reducing maintenance efforts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If resources are allocated to machines of different generations with different machining capabilities, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent segments the resource allocation problem into multiple iterations, where each iteration assigns resources to a subset of machines. This divides the complex task of allocating resources across all machines of different generations into manageable steps, reducing the overall complexity while maintaining productivity benefits.
Solution Approach 2:
The patent implements dynamic resource allocation where the set of machines considered in each iteration is updated based on previous allocations. This dynamic approach allows the system to adaptively handle machines of different generations and capabilities, optimizing productivity while managing complexity through iterative refinement.
2Reliability
If more resource-machine combinations are deployed, then on-time delivery is improved, but maintenance cost increases
Solution Approach 1:
The patent applies partial action by assigning resources to only those machines that are most needed in each iteration, rather than deploying resources to all machines. This selective approach ensures on-time delivery for critical orders while avoiding unnecessary maintenance costs associated with over-deployment of resource-machine combinations.
Solution Approach 2:
The iterative nature of the allocation method provides feedback mechanisms where previous allocation results inform subsequent decisions. This allows the system to learn from previous assignments and optimize the balance between on-time delivery and maintenance costs by adjusting resource-machine combinations based on accumulated information.
3Productivity
If resources are assigned through multiple iterations, then capacity utilization is maximized, but loss of time increases
Solution Approach 1:
The patent performs preliminary actions by pre-defining the set of machines to be considered in each iteration and pre-establishing allocation priorities. This preliminary structuring allows the iterative process to focus computational efforts on critical decisions, maximizing capacity utilization while reducing the time lost to unnecessary computations.
Data Source
Figure 1
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AI summary
A method for allocating resources (104) to machines (102) of a production facility (100) comprises: receiving forecast data (204) indicating planned demands (214) for the resources (104) and a demand deviation (216) for each resource (104); generating a new demand (220) for each resource (104) from the planned demand (214) and the demand deviation (216) of the resource (104) in several iterations; assigning capacities to the new demands (220) in each iteration by determining resource-machine combinations (106), wherein a priority is determined for each resource (104) and each machine (102) based on a set of priority rules (226; 226a, 226b, 226c, 226d, 226e), wherein the resource-machine combinations (106) are determined by combining the resources (104) and the machines (102) according to the priorities; and generating a roll-out plan (228) assigning resource-machine combinations (106) to be rolled out to future time periods, wherein the roll-out plan (228) is generated from the resource-machine combinations (106) of different iterations and an estimated roll-out time for each resource-machine combination (106) such that a total number of the resource-machine combinations (106) to be rolled-out is minimized.