Journal
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
Volume 68, Issue 9-12, Pages 2043-2056Publisher
SPRINGER LONDON LTD
DOI: 10.1007/s00170-013-4819-y
Keywords
Hybrid flow-shop scheduling; Identical parallel machine; Decoding method; Estimation of distribution algorithm; Probability model
Funding
- National Key Basic Research and Development Program of China [2013CB329503]
- National Science Foundation of China [61174189, 61025018]
- Doctoral Program Foundation of Institutions of Higher Education of China [20100002110014]
- National Science and Technology Major Project of China [2011ZX02504-008]
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In this paper, an enhanced estimation of distribution algorithm (EEDA) is proposed to solve the hybrid flow-shop scheduling problem with identical parallel machines to minimize makespan. To evaluate the individuals, some decoding rules including the improved permutation scheduling rule, the improved list scheduling rule and the backward scheduling rule are designed for the permutation-based encoding scheme, and then a hybrid decoding method is proposed. To describe the distribution of the solution space for the EEDA, a probability model is built and used to generate new individuals by sampling. To well trace the region with promising solutions, a mechanism is provided to update the model with the superior sub-population. To enhance the exploitation capability, multiple local search operators are incorporated in the framework of the EEDA. The influence of the parameter setting is investigated based on the Taguchi method of design-of-experiment. Extensive numerical testing results based on sets of the well-known benchmarks and the comparisons with some existing algorithms demonstrate the effectiveness of the proposed algorithm.
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