Task Scheduling Optimization with Direct Constraints from a Tensor Network Perspective
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Acceso en línea: | |
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| _version_ | 1866911629395886080 |
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| author | Ali, Alejandro Mata Delgado, Iñigo Perez Markaida, Beatriz García de Leceta, Aitor Moreno Fdez. |
| author_facet | Ali, Alejandro Mata Delgado, Iñigo Perez Markaida, Beatriz García de Leceta, Aitor Moreno Fdez. |
| contents | This work presents a novel method for task optimization in industrial plants using quantum-inspired tensor network technology. This method obtains the best possible combination of tasks on a set of machines with directed constraints while minimizing the total execution cost. With this method, an exact and explicit solution of the problem is provided. This algorithm constructs a tensor network representation of the tensor which provides the solution of the problem. This method is improved in order to reduce the computational complexity of the solution computation, using problem preprocessing, new techniques of condensation of logical constraints, optimization of the value determination technique with previously calculated results, reuse of intermediate computations, and iterative relations for constraints. Three algorithms for computation are presented: the main algorithm, the iterative algorithm which adds only the minimal amount of necessary constraints, and the genetic algorithm which combines the iterative algorithm with basic genetic algorithms. Finally, a simple version of both algorithms was implemented, and their performance was tested, all publicly available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_10433 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Task Scheduling Optimization with Direct Constraints from a Tensor Network Perspective Ali, Alejandro Mata Delgado, Iñigo Perez Markaida, Beatriz García de Leceta, Aitor Moreno Fdez. Quantum Physics Emerging Technologies 68Q12, 15A69, 90C27 G.1.3; G.2.1 This work presents a novel method for task optimization in industrial plants using quantum-inspired tensor network technology. This method obtains the best possible combination of tasks on a set of machines with directed constraints while minimizing the total execution cost. With this method, an exact and explicit solution of the problem is provided. This algorithm constructs a tensor network representation of the tensor which provides the solution of the problem. This method is improved in order to reduce the computational complexity of the solution computation, using problem preprocessing, new techniques of condensation of logical constraints, optimization of the value determination technique with previously calculated results, reuse of intermediate computations, and iterative relations for constraints. Three algorithms for computation are presented: the main algorithm, the iterative algorithm which adds only the minimal amount of necessary constraints, and the genetic algorithm which combines the iterative algorithm with basic genetic algorithms. Finally, a simple version of both algorithms was implemented, and their performance was tested, all publicly available. |
| title | Task Scheduling Optimization with Direct Constraints from a Tensor Network Perspective |
| topic | Quantum Physics Emerging Technologies 68Q12, 15A69, 90C27 G.1.3; G.2.1 |
| url | https://arxiv.org/abs/2311.10433 |