Investigating a Hybrid Metaheuristic For Job Shop Rescheduling

Details Investigating a Hybrid Metaheuristic For Job Shop Rescheduling Investigating a Hybrid Metaheuristic For Job Shop Rescheduling

2008-03-12

arxiv.org/abs/0803.1728v1

Proceedings of the 3rd Australian Conference on Artificial Life (ACAL07), Lecture Notes in Computer Science 4828, pp 357-368,

Computer Science

Neural and Evolutionary Computing

Scientific paper

10.1007/978-3-540-76931-6_31

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.

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