| 1. |
- Eiter, Thomas, et al.
(författare)
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ALASPO : An Adaptive Large-Neighbourhood ASP Optimiser
- 2022
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Ingår i: KR 2022. - : IJCAI Organization. - 9781956792010 ; , s. 565-569
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Konferensbidrag (refereegranskat)abstract
- We present the system ALASPO which implements Adaptive Large-neighbourhood search for Answer Set Programming (ASP) Optimisation. Large-neighbourhood search (LNS) is a meta-heuristic where parts of a solution are destroyed and reconstructed in an attempt to improve an overall objective. ALASPO currently supports the ASP solver clingo, as well as its extensions clingo-dl and clingcon for difference and full integer constraints, and multi-shot solving for an efficient implementation of the LNS loop. Neighbourhoods can be defined in code or declaratively as part of the ASP encoding. While the method underlying ALASPO has been described in previous work, ALASPO also incorporates portfolios for the LNS operators along with self-adaptive selection strategies as a technical novelty. This improves usability considerably at no loss of solution quality, but on the contrary often yields benefits. To demonstrate this, we evaluate ALASPO on different optimisation benchmarks.
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| 2. |
- 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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| 3. |
- Eiter, Thomas, et al.
(författare)
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Answer-Set Programming for Lexicographical Makespan Optimisation in Parallel Machine Scheduling
- 2021
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Ingår i: Proceedings of the 18th International Conference on Principles of Knowledge Representation and Reasoning, KR 2021. - : IJCAI Organization. - 9781956792997 ; , s. 280-290
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Konferensbidrag (refereegranskat)abstract
- We deal with a challenging scheduling problem on parallel-machines with sequence-dependent setup times and release dates from a real-world application of semiconductor workshop production. There, jobs can only be processed by dedicated machines, thus few machines can determine the makespan almost regardless of how jobs are scheduled on the remaining ones. This causes problems when machines fail and jobs need to be rescheduled. Instead of optimising only the makespan, we put the individual machine spans in non-ascending order and lexicographically minimise the resulting tuples. This achieves that all machines complete as early as possible and increases the robustness of the schedule. We study the application of Answer-Set Programming (ASP) to solve this problem. While ASP eases modelling, the combination of timing constraints and the considered objective function challenges current solving technology. The former issue is addressed by using an extension of ASP by difference logic. For the latter, we devise different algorithms that use multi-shot solving. To tackle industrial-sized instances, we study different approximations and heuristics. Our experimental results show that ASP is indeed a promising KRR paradigm for this problem and is competitive with state-of-the-art CP and MIP solvers.
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| 4. |
- 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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| 5. |
- 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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| 6. |
- Karlsson, Emil, 1990-
(författare)
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Optimisation methods for solving a large-scale avionics scheduling problem
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Modern computer systems in aircraft are based on an integrated modular avionic architecture. In this architecture, software applications share hardware resources on a common avionic platform. Many functions in an aircraft are controlled by software and a failure in such software can have severe consequences. To avoid malfunction, there are many aspects to consider. One aspect is to ensure that the activities in the system get sufficient computing and network communication resources while being completed in time. This thesis contributes to addressing this challenge by studying an avionics scheduling problem proposed by Saab.In this problem, tasks are performed on modules and the messages are sent on a communication network that links the modules. A schedule specifies start times for tasks on modules and the choices of time slots for messages on the communication network. For a schedule to be feasible, constraints must be respected: precedence constraints between tasks, timing constraints on tasks and messages, and communication network constraints involving both tasks and messages. For future platforms, it is expected that large-scale instances need to be solved. The methods introduced in this thesis solve instances with up to 55,000 tasks and 2500 messages.The thesis includes three papers that introduce methods for solving the avionics scheduling problem and one paper that compares techniques to improve the performance of a specific type of decomposition method. The solution methods for the avionics problem differ in how the decomposition is made, how the subproblems are solved, and if the algorithm is guaranteed to find a feasible solution or not, given enough time. Together, the contributions of these papers give an insight into the structure of this type of avionics scheduling problem and how this structure can be exploited to construct efficient exact and matheuristic scheduling methods.Paper A introduces a constraint generation procedure tailored for the avionics scheduling problem. In the constraint generation procedure, a preliminary, possibly infeasible, schedule is constructed by solving a master problem. This schedule is used to define a restriction of the problem, which is solved to find a complete schedule. If no schedule is found within a time-limit, constraints are added to the master problem, which is then solved again. This procedure relies on solving Mixed-Integer Programming (MIP) models. In Paper B, the constraint generation procedure is extended into a matheuristic, where the master problem is solved with a MIP-based adaptive large neighbourhood search. The methods in Paper A and Paper B can solve instances with up to 37,000 tasks and 1700 messages, and 55,000 tasks and 2500 messages, respectively.Logic-Based Benders Decomposition (LBBD) algorithms are treated in Paper C and Paper D. In both papers, a MIP solver is applied to the master problem while a constraint programming solver is used for the subproblem. Paper C addresses the avionics scheduling problem and introduces a new acceleration technique for LBBD. This technique exploits information obtained during cut strengthening to make educated guesses about where feasible solutions might be found. With this technique, the LBBD algorithm outperforms the methods from Paper A and Paper B both with respect to solution time and RAM usage. Paper D investigates cut strengthening for LBBD in a wider context, by evaluating different cut-strengthening algorithms for LBBD on three problems from the literature. The computational evaluation shows that it is advantageous to invest time in finding strong cuts.
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