Investigating a Hybrid Metaheuristic for Job Shop Rescheduling
| Autor: | Aniza Mohamed Din, Rong Qu, Salwani Abdullah, Uwe Aickelin, Edmund K. Burke |
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| Rok vydání: | 2007 |
| Předmět: |
FOS: Computer and information sciences
Schedule Mathematical optimization Job shop scheduling biology Artificial immune system Computer science Job shop Computer Science - Neural and Evolutionary Computing Great Deluge algorithm Computational Engineering Finance and Science (cs.CE) Antigen Backup Genetic algorithm Simulated annealing biology.protein Neural and Evolutionary Computing (cs.NE) Antibody Computer Science - Computational Engineering Finance and Science Metaheuristic |
| Zdroj: | Progress in Artificial Life ISBN: 9783540769309 Scopus-Elsevier |
| DOI: | 10.1007/978-3-540-76931-6_31 |
| Popis: | Previous research has shown that artificial immune systems can be used to produce robust schedules in a manufacturing environment. The main goal is to develop building blocks (antibodies) of partial schedules that can be used to construct backup solutions (antigens) when disturbances occur during production. The building blocks are created based upon underpinning ideas from artificial immune systems and evolved using a genetic algorithm (Phase I). Each partial schedule (antibody) is assigned a fitness value and the best partial schedules are selected to be converted into complete schedules (antigens). We further investigate whether simulated annealing and the great deluge algorithm can improve the results when hybridised with our artificial immune system (Phase II). We use ten fixed solutions as our target and measure how well we cover these specific scenarios. |
| Databáze: | OpenAIRE |
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