Abstract
Differential Evolution(DE) has been employed to solve numerously continuous combinatorial problems. However, because of its evolution strategy, DE is unlikely to be effective in a discrete combinatorial problem unless discrete variables can be reformulated into a continuous vector. This paper focuses on solution to Level Scheduling Problems (LSP). First, an encoding rule, named SPV-MMAL, suitable for Mixed-model Assembly Sequencing (MMAS) is presented; an appropriate evolution strategy as well as control parameters, are selected on the basis of LSP characteristics; next, an Improved DE Algorithm (IDEA) for Mixed-model Assembly Line (MMAL) LSP is proposed; furthermore, computation examples are given to validate the IDEA . Comparison between computation results obtained using IDEA and those acquired using Genetic Algorithm (GA) proves that the IDEA has a significant advantage over GA in terms of optimal solution and convergence efficiency in solving MMAL LSP, especially in solving large-scale ones.
Supported by National Natural Science Foundation of China (No.50775089, 50875101).
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Gang, H., Shaolei, L., Jinhang, L., Bo, F. (2011). An Improved Differential Evolution Algorithm for Mixed-Model Assembly Sequencing. In: Zhang, J. (eds) Applied Informatics and Communication. ICAIC 2011. Communications in Computer and Information Science, vol 227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23226-8_76
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DOI: https://doi.org/10.1007/978-3-642-23226-8_76
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