Production Site Layout Planning for Exhaustive Machine Combination Search
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Solution Overview
Problem
Existing methods for planning production facilities simplify simulations based on expert experience, neglecting variants that experts initially reject, leading to suboptimal solutions.
Innovation Solution
An algorithm that reads in orders, selects sheet metal processing systems, compiles all possible system combinations, and ranks them using a Combinatory Logic Synthesizer (CLS) and Satisfiability Modulo Theory (SMT) framework to find the best combination based on predefined optimization criteria, considering all potential configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If experts simplify simulation based on experience, then planning efficiency is improved, but completeness of variant consideration deteriorates
Solution Approach 1:
The planning process is segmented into two distinct phases: an automated exhaustive generation phase that creates all possible system combinations without bias, and a subsequent evaluation phase where expert knowledge is applied to rank and select optimal variants. This segmentation allows complete variant exploration while maintaining planning efficiency.
Solution Approach 2:
The algorithm performs preliminary action by automatically generating and pre-evaluating all possible system combinations before final expert review. This preliminary exhaustive exploration ensures no promising variants are missed, while the subsequent expert ranking maintains efficiency by focusing human judgment on already-filtered options.
2Adaptability or versatility
If all possible system combinations are compiled and simulated, then completeness of solution space is improved, but computational effort increases
Solution Approach 1:
The method applies partial action by compiling all possible system combinations (excessive) but then filtering and ranking them to focus computational resources on the most promising variants. This approach ensures complete solution space coverage while managing computational effort through systematic elimination of inferior options.
Solution Approach 2:
The algorithm changes parameters by evaluating system combinations based on multiple optimization criteria (productivity, cost, space utilization). By varying evaluation parameters and ranking systems, the method efficiently narrows down the complete solution space to optimal variants without exhaustive simulation of every possibility under all conditions.
3Productivity
If expert experience guides variant selection, then planning speed is improved, but discovery of unexpected optimal configurations deteriorates
Solution Approach 1:
The method inverts the traditional approach by having the algorithm generate all variants first (including unexpected ones) rather than having experts filter variants first. This inversion ensures unexpected optimal configurations are not rejected from the outset, while subsequent expert ranking maintains planning speed by evaluating a manageable set of pre-generated options.
Solution Approach 2:
The system performs self-service by automatically generating, simulating, and preliminary evaluating all system combinations without expert intervention. This autonomous exhaustive exploration discovers unexpected variants that would otherwise be missed, while the system then presents refined options for efficient expert selection.
Data Source
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AI summary
The invention relates to a method for determining an optimum combination of sheet metal working installations (12a-12d) on a production site (10). The method involves multiple, in particular a multiplicity of, preferably all, possible installation combinations being created. This creation is preferably performed by a CLS module (CLS solver) (24). The created installation combinations are then ranked on the basis of at least one predefined optimization criterion (32). The optimization criterion (32) can be available in the form of the total installation costs and/or the total production time of the respective installation combination. The ranking can be carried out by means of an SMT module (SMT framework) (28). The SMT module (28) can additionally perform filtering on the basis of specific constraints (30), for example on the basis of a total production time that must not be exceeded for the respective installation combination. This allows well-trodden paths for the planning of a production site (10) to be left and unexpected optimum installation combinations to be found. The invention also relates to a computer program product for carrying out the method.