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Neural Computing and Applications

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23.07.2019 | Deep Learning & Neural Computing for Intelligent Sensing and Control

A genetic algorithm for fuzzy random and low-carbon integrated forward/reverse logistics network design

verfasst von: Yangjun Ren, Chuanxu Wang, Botang Li, Chao Yu, Suyong Zhang

Erschienen in: Neural Computing and Applications | Ausgabe 7/2020

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Abstract

Considering the influence of carbon emissions trading, the fuzzy stochastic programming model was established to cut back the total cost of carbon trading balance. Modeling this chain is carried out by accounting for carbon cap-and-trade considerations and total cost optimization. In this paper, we analyze the low-carbon integrated forward/reverse logistics network and made relevant simulation tests. The results show that the changes of the confidence level and carbon emission limits have obvious influences on logistics costs. If the emission limit is large, carbon trading mechanism has little effect on the total logistics cost in the same scenario. Therefore, the government needs to use the appropriate emission limits to guide enterprises to reduce carbon emissions, and enterprises can make coping strategies according to the different limit at the same time. Therefore, the fuzzy random programming model proposed in this paper is practical. Its decision making applying the proposed algorithm is reasonable and applicable and could provide decision basis for enterprise managers.

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Titel: A genetic algorithm for fuzzy random and low-carbon integrated forward/reverse logistics network design
verfasst von: Yangjun Ren
Chuanxu Wang
Botang Li
Chao Yu
Suyong Zhang
Publikationsdatum: 23.07.2019
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 7/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-019-04340-4

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