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A genetic algorithm for fuzzy random and low-carbon integrated forward/reverse logistics network design
- Source :
- Neural Computing and Applications. 32:2005-2025
- Publication Year :
- 2019
- Publisher :
- Springer Science and Business Media LLC, 2019.
-
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.
- Subjects :
- 0209 industrial biotechnology
Government
Operations research
Computer science
Total cost
02 engineering and technology
Reverse logistics
Fuzzy logic
Stochastic programming
Network planning and design
020901 industrial engineering & automation
Artificial Intelligence
Greenhouse gas
Genetic algorithm
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Limit (mathematics)
Software
Subjects
Details
- ISSN :
- 14333058 and 09410643
- Volume :
- 32
- Database :
- OpenAIRE
- Journal :
- Neural Computing and Applications
- Accession number :
- edsair.doi...........e5816db25be9b7698a0de41b64cc7fd5