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An optimization method for task assignment for industrial manufacturing organizations

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Abstract

An industrial manufacturing organization is an aggregation of collaborative units and employees during the process of product development and production. The rapid growing degree of product complexity has resulted in a rising scale of corresponding manufacturing organizations. An effective and optimal schema is essential for assigning human resources to tasks to save costs. This paper proposes an optimization method for this task assignment issue based on a dynamic industrial manufacturing process model and an improved quantum genetic algorithm (QGA) with heuristic information: the heuristic QGA (HQGA). The dynamic process model adopts a hierarchical network to illustrate task composition in a complex industrial manufacturing process and dynamically describes the task completing process on the basis of individual performance and cooperative performance. To reduce the complexity of the fitness evaluation and assignment optimization in the model, the HQGA is presented for three types of optimization objective functions. The HQGA introduces a heuristic principle to accelerate convergence toward an optimal solution, where quantum bitbased encoding design can reflect the degree of participation of different individuals for different tasks. In four case studies, the HQGA successfully completed task assignment based on our dynamic process model and showed better optimization performance compared with conventional QGA and GA.

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Authors and Affiliations

  1. School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China

    Ni Li, Yuhong Li, Mengyuan Sun, Haipeng Kong & Guanghong Gong

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  1. Ni Li
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  2. Yuhong Li
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  3. Mengyuan Sun
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  4. Haipeng Kong
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  5. Guanghong Gong
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Correspondence to Guanghong Gong.

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Li, N., Li, Y., Sun, M. et al. An optimization method for task assignment for industrial manufacturing organizations. Appl Intell 47, 1144–1156 (2017). https://doi.org/10.1007/s10489-017-0940-1

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