Abstract
This paper presents an enhanced migrating birds optimization (enMBO) for the flexible job shop scheduling problem with the consideration of lot streaming and the goal is minimizing total flowtime. In enMBO, to explore the solution space efficiently, we design a search scheme which is capable of adjusting the search radius with the increase of iteration. In addition, MBO concentrates too much on local search and hence is easily trapped in local optimum. To handle this, a special mechanism that based on precedence operation crossover is developed and incorporated into the evolutionary framework. We conduct simulations on well-known benchmarks with different scales and results verify the significance of schemes designed above. Moreover, by comparing with recent algorithms, the proposed enMBO shows its high performance for the considered problem.
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This research is partially supported by the National Science Foundation of China 51575212 and 61174187, and Shanghai Key Laboratory of Power station Automation Technology.
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Meng, T., Pan, Qk., Chen, Qd. (2018). An Enhanced Migrating Birds Optimization for the Flexible Job Shop Scheduling Problem with Lot Streaming. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10954. Springer, Cham. https://doi.org/10.1007/978-3-319-95930-6_78
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