Production Sequence Optimization Using Annealing Under Delivery Constraints
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Solution Overview
Problem
Existing manufacturing systems face challenges in determining an optimal processing sequence for multiple workpieces to minimize changeover time, which is crucial for improving productivity, due to the exponential increase in calculation time for determining such sequences as the number of workpieces increases.
Innovation Solution
A production plan optimization apparatus and method that formulates the processing sequence as a traveling salesman problem, incorporating delivery date and sequence constraints, and uses simulated or quantum annealing to calculate an optimal solution, thereby reducing changeover times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional calculation methods are used to determine optimal processing sequence, then manufacturing cost can be reduced by minimizing changeover times, but calculation time increases exponentially with the number of workpieces
Solution Approach 1:
The patent transforms the original combinatorial optimization problem into a Traveling Salesman Problem (TSP) formulation, changing the problem parameters and structure to enable efficient solution. By mapping workpiece changeover sequences to TSP routes and changeover times to travel distances, the problem becomes solvable with polynomial-time algorithms rather than exponential-time brute force methods
Solution Approach 2:
The patent replaces traditional mechanical/combinatorial optimization approaches with a mathematical modeling approach using TSP formulation. Instead of directly optimizing workpiece sequences through computationally intensive methods, the system substitutes this with an equivalent TSP problem that can be solved more efficiently using established algorithms
2Quantity of substance
If the number of workpieces to be processed increases, then production volume increases, but the calculation time for determining optimal sequence increases exponentially
Solution Approach 1:
The patent changes the fundamental parameters of the optimization problem by transforming it into a TSP formulation. This transformation allows the system to handle larger numbers of workpieces efficiently, as TSP algorithms have polynomial time complexity compared to the exponential complexity of direct combinatorial optimization
Solution Approach 2:
The TSP formulation serves as a universal framework that can handle varying numbers of workpieces through the same algorithmic approach. The patent creates a multi-functional system where the same TSP-based optimization method works for small and large production volumes, providing a scalable solution
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for the determination of an optimal processing sequence that reduces manufacturing costs by minimizing changeover times, enhancing productivity and reducing reliance on human intuition.
Implementation Method 1
by using annealing processing means for searching for an optimal solution to a combinatorial optimization problem by simulated annealing or quantum annealing
Implementation Method 2
by using annealing processing means for searching for an optimal solution to a combinatorial optimization problem by simulated annealing or quantum annealing
Data Source
AI summary
A production plan optimization apparatus includes a traveling salesman problem formulation unit for formulating a problem regarding determining a sequence in which a plurality of workpieces are processed as a traveling salesman problem that satisfies a delivery date constraint based on a delivery date specified for at least one of the plurality of workpieces and a sequence constraint specified for at least one of combinations of the plurality of workpieces and also minimizes a sum of changeover times associated with switching of the workpieces and a processing sequence determination unit for calculating a solution to the traveling salesman problem, by using an annealing processing unit for searching for an optimal solution to a combinatorial optimization problem by simulated annealing or quantum annealing, and determining the sequence in which the plurality of workpieces are processed based on a result of the calculation.