| 1. |
- Eiter, Thomas, et al.
(författare)
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An open challenge for exact job scheduling with reticle batching in photolithography
- 2022
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Konferensbidrag (refereegranskat)abstract
- We consider scheduling solutions for photolithography, an important sub-task in semi-conductor production, where patterns are transferred to wafers using reticles. The problem can be modelled as job scheduling on unrelated parallel machines with sequence-dependent setup times and release dates. The reticles add auxiliary-resource constraints for processing jobs. Equipping machines with the right reticles using transport robots from stockers in time renders this problem extremely difficult for exact solvers that use a declarative model. The latter would be attractive as such models tend to be compact and easy to maintain. We present a solver-independent MiniZinc model and provide 500 new benchmark instances. However, only small instances can be solved with state-of-the-art MIP and CP solvers. Consequently, we present this problem as an open challenge with considerable potential for driving improvements towards industrial applications.
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| 2. |
- Eiter, Thomas, et al.
(författare)
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Large-Neighbourhood Search for ASP Optimisation
- 2022
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Ingår i: Electronic Proceedings in Theoretical Computer Science, EPTCS. - : Open Publishing Association. ; , s. 163-165
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Konferensbidrag (refereegranskat)abstract
- While answer-set programming (ASP) is a successful approach to declarative problem solving optimisation can still be a challenge for it. Large-neighbourhood search (LNS) is a metaheuristic technique where parts of a solution are alternately destroyed and reconstructed, which has high but untapped potential for ASP solving. We present a framework for LNS optimisation for ASP, in which neighbourhoods can be specified either declaratively as part of the ASP encoding, or automatically generated by code. In the full paper, we illustrate the framework on different optimisation problems, some of which are notoriously difficult, including shift planning and a parallel machine scheduling problem from semi-conductor production which demonstrate the effectiveness of the LNS approach.
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| 3. |
- Eiter, Thomas, et al.
(författare)
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Large-Neighbourhood Search for Optimisation in Answer-Set Solving
- 2022
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Ingår i: Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022. - : Association for the Advancement of Artificial Intelligence. - 1577358767 - 9781577358763 ; , s. 5616-5625
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Konferensbidrag (refereegranskat)abstract
- While Answer-Set Programming (ASP) is a prominent approach to declarative problem solving, optimisation problems can still be a challenge for it. Large-Neighbourhood Search (LNS) is a metaheuristic for optimisation where parts of a solution are alternately destroyed and reconstructed that has high but untapped potential for ASP solving. We present a framework for LNS optimisation in answer-set solving in which neighbourhoods can be specified either declaratively as part of the ASP encoding or automatically generated by code. To effectively explore different neighbourhoods, we focus on multi-shot solving as it allows to avoid program regrounding. We illustrate the framework on different optimisation problems some of which are notoriously difficult, including shift planning and a parallel machine scheduling problem from semi-conductor production, which demonstrate the effectiveness of the LNS approach.
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