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Hybrid evolutionary algorithm with marriage of genetic algorithm and extremal optimization for production scheduling

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Abstract

This paper presents a hybrid evolutionary algorithm with marriage of genetic algorithm (GA) and extremal optimization (EO) for solving a class of production scheduling problems in manufacturing. The scheduling problem, which is derived from hot rolling production in steel industry, is characterized by two major requirements: (i) selecting a subset of orders from manufacturing orders to be processed; (ii) determining the optimal production sequence under multiple constraints, such as sequence-dependant transition costs, non-execution penalties, earliness/tardiness (E/T) penalties, etc. A combinatorial optimization model is proposed to formulate it mathematically. For its NP-hard complexity, an effective hybrid evolutionary algorithm is developed to solve the scheduling problem through combining the population-based search capacity of GA and the fine-grained local search efficacy of EO. The experimental results with production scale data demonstrate that the proposed hybrid evolutionary algorithm can provide superior performances in scheduling quality and computation efficiency.

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Abbreviations

GA:

genetic algorithm

MGA:

modified genetic algorithm

EO:

extremal optimization

GEO:

genetic extremal optimization

E/T:

earliness/tardiness

PCTSP:

prize collecting traveling salesman problem

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Authors and Affiliations

  1. Department of Automation, Shanghai Jiaotong University, Shanghai, 200240, People’s Republic of China

    Yu-Wang Chen, Yong-Zai Lu & Gen-Ke Yang

Authors
  1. Yu-Wang Chen
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  2. Yong-Zai Lu
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  3. Gen-Ke Yang
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Correspondence to Yu-Wang Chen.

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Chen, YW., Lu, YZ. & Yang, GK. Hybrid evolutionary algorithm with marriage of genetic algorithm and extremal optimization for production scheduling. Int J Adv Manuf Technol 36, 959–968 (2008). https://doi.org/10.1007/s00170-006-0904-9

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  • DOI: https://doi.org/10.1007/s00170-006-0904-9

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