Author(s)

Zhenbo Wu ¹

Affiliation(s)

¹ Business School, University of Shanghai for Science and Technology, Shanghai, 200093, China

Corresponding Author

Zhenbo Wu

ABSTRACT

In response to the challenges of high-mix low-volume production driven by personalized customization demands, existing research has predominantly focused on single-dimensional improvements, with insufficient comprehensive consideration of dynamic disturbances such as process variations and machine failures derived from customization requirements. To address this gap, this study develops a multi-objective mathematical model that simultaneously minimizes total production costs and makespan by integrating critical dynamic disturbance factors. An improved hybrid algorithm H-IPNSGA-II combining particle swarm optimization (PSO) and non-dominated sorting genetic algorithm-II (NSGA-II) is proposed to solve the model. A case study involving an automotive parts manufacturing enterprise is conducted to validate the proposed methodology. Comparative experiments and sensitivity analysis demonstrate the superior performance of the model and algorithm, providing theoretical support for personalized production scheduling. This research contributes to advancing multi-objective optimization approaches in customized manufacturing environments with complex uncertainties.

KEYWORDS

Personalized Requirements; Flexible Job Shop; Integrated Scheduling; H-IPNSGA-II

CITE THIS PAPER

Zhenbo Wu, Integrated Scheduling Method for Flexible Job Shops Considering Personalized Requirements. Industrial Engineering and Innovation Management (2025) Vol. 8: 135-151. DOI: http://dx.doi.org/10.23977/ieim.2025.080218.

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