Carnegie Mellon Robotics Institute
Angelo Oddi, Riccardo Rasconi, Amedeo Cesta, and Stephen Smith
Proceedings 22nd International Joint Conference on Artificial Intelligence, Barcelona, Spain, July, 2011.
, July, 2011.
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| Abstract |
| This paper presents a meta-heuristic algorithm for solving the Flexible Job Shop Scheduling Problem (FJSSP). This strategy, known as Iterative Flatten-ing Search (IFS), iteratively applies a relaxation-step, in which a subset of scheduling decisions are randomly retracted from the current solution; and a solving-step, in which a new solution is incremen-tally recomputed from this partial schedule. This work contributes two separate results: (1) it pro-poses a constraint-based procedure extending an existing approach previously used for classical Job Shop Scheduling Problem; (2) it proposes an origi-nal relaxation strategy on feasible FJSSP solutions based on the idea of randomly breaking the exe-cution orders of the activities on the machines and opening the resource options for some activities se-lected at random. The efficacy of the overall heuris-tic optimization algorithm is demonstrated on a set of well-known benchmarks. |
| Notes |
| Text Reference |
| Angelo Oddi, Riccardo Rasconi, Amedeo Cesta, and Stephen Smith , "Iterative Flattening Search for the Flexible Job Shop Scheduling Problem," Proceedings 22nd International Joint Conference on Artificial Intelligence, Barcelona, Spain, July, 2011. , July, 2011. |
| BibTeX Reference |
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@inproceedings{Oddi_2011_7052, author = "Angelo Oddi and Riccardo Rasconi and Amedeo Cesta and Stephen {Smith }", title = "Iterative Flattening Search for the Flexible Job Shop Scheduling Problem", booktitle = "Proceedings 22nd International Joint Conference on Artificial Intelligence, Barcelona, Spain, July, 2011. ", month = "July", year = "2011", number= "CMU-RI-TR-", } |
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