The University of Sussex

A comparison of optimization techniques for integrated manufacturing planning and scheduling

M. McIlhagga, P. Husbands, R. Ives

We describe a comparison between Simulated Annealing, Dispatch Rules, and a Coevolutionary Distributed Genetic Algorithm solving a random sample of integrated planning and scheduling problems. We found that for a wide range of optimization criteria the Distributed Genetic Algorithm consistently outperformed Simulated Annealing and Dispatch Rules. The Distributed Genetic Algorithm finds 8-9 unique high quality solutions per run, whereas the other techniques find one. On average, each Distributed Genetic Algorithm solution is 10-15% better than Simulated Annealing solutions and 30-35% better than Dispatch Rules solutions.


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