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- W2023555714 abstract "As a typical manufacturing and scheduling problem with strong industrial background, flow shop scheduling with limited buffers has gained wide attention both in academic and engineering fields. With the objective to minimize the total completion time (or makespan), such an issue is very hard to solve effectively due to the NP-hardness and the constraint on the intermediate buffer. In this paper, an effective hybrid genetic algorithm (HGA) is proposed for permutation flow shop scheduling with limited buffers. In the HGA, not only multiple genetic operators based on evolutionary mechanism are used simultaneously in hybrid sense, but also a neighborhood structure based on graph model is employed to enhance the local search, so that the exploration and exploitation abilities can be well balanced. Moreover, a decision probability is used to control the utilization of genetic mutation operation and local search based on problem-specific information so as to prevent the premature convergence and concentrate computing effort on promising neighbour solutions. Simulation results and comparisons based on benchmarks demonstrate the effectiveness of the HGA. Meanwhile, the effects of buffer size and decision probability on optimization performances are discussed." @default.
- W2023555714 created "2016-06-24" @default.
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- W2023555714 date "2006-08-01" @default.
- W2023555714 modified "2023-09-26" @default.
- W2023555714 title "Erratum to “Cycling in linear programming problems” [Computers and Operations Research 31 (2002) 303–311]" @default.
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