This article describes the solving process of the High School Scheduling problem. Hybrid heuristic algorithms are applied to the constraint modeled problem. constraint Satisfaction is used to model the problem by the ...
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This article describes the solving process of the High School Scheduling problem. Hybrid heuristic algorithms are applied to the constraint modeled problem. constraint Satisfaction is used to model the problem by the classes of the constraint Solving Engine (CSE) and a constraint programming Library (CPL) which we previously developed. The main hybrid algorithm used was based on Simulated Annealing, Greedy Search, Guided Search, and memory from Tabu Search.
The dataset presented in this paper introduces 384 new instances for the feasibility version of a multiprocessor scheduling problem with multiple time windows, positive time lags and exact time lags. The instances are...
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The dataset presented in this paper introduces 384 new instances for the feasibility version of a multiprocessor scheduling problem with multiple time windows, positive time lags and exact time lags. The instances are constructed from subproblems in a logic-based Benders decomposition scheme introduced in "Logic-based Benders decomposition with a partial assignment acceleration technique for an avionics scheduling problem" (Karlsson, E., Ronnberg, E., Computers & Operations Research, 2022) [1]. A key aspect of the dataset is that even if two instances are highly similar, the computational performance of solving them with an IBM ILOG CP Optimizer model can be vastly different. There exists for example 44 pairs of instances with the same number of tasks and exact time lags, and the number of positive time lags differs with at most two, where one instance can be solved within 5 minutes and the other instance cannot be solved within 24 hours. Such differences make the instance dataset useful for investigating differences in computational performance of constraint programming solvers. The dataset can also be used to benchmark methods for multiprocessor scheduling. The dataset has been released under the Creative Commons Attribution 4.0 International license and can be used as it is or be adapted. (c) 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://***/licenses/by/4.0/)
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