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Adaptive selection multi-objective optimization method for hybrid flow shop green scheduling under finite variable parameter constraints: case study

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journal contribution
posted on 07.06.2021, 06:20 by Zhifeng Liu, Jun Yan, Qiang Cheng, Hongyan Chu, Jigui Zheng, Caixia Zhang

The energy consumption loss is high particularly in manufacturing processes involving heating furnaces. Moreover, the mandatory constraints in continuous heating stage bring difficult challenges to production scheduling. To improve the production efficiency and reduce the energy consumption in a hybrid flow shop with continuous and discrete processing stages, this study developed an adaptive selection multi-objective optimization algorithm with preference (ASMOAP). The mandatory constraints of continuous processing stage are transformed into one of the optimization objectives, which is defined as maximum excess value of adjustment time in this paper. A multi-objective optimization scheduling model with the makespan, energy consumption, and maximum excess of adjustment time is established. The optimization preference is designed in the proposed multi-objective optimization algorithm. The maximum excess of adjustment time is set as the multi-objective optimization preference. Three adaptive selection strategies are designed for the algorithm based on the proportions of outstanding and preference individuals to eliminate constraint conflicts. Presented results prove that the proposed algorithm could effectively solve hybrid flow shop scheduling problem considering discrete and continuous processing stages with limited time. It can be applied to obtain a better feasible solution while improving the efficiency and reducing the energy consumed in practical production processes.

Funding

The authors are grateful for financial support from the National Natural Science Foundation of China (No. 51975019 and No. 51705010), the National Science and Technology Major Project of China (No. 2019ZX04024001), the training program of Rixin talent and outstanding talent from Beijing University of Technology.

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