Multi-objective Optimization of Reverse Logistics Network Based on Random Weights and Genetic Algorithm

Optimizing reverse logistics network is important to the sustainable development of logistics. In this paper, we fully considered environment effect and the waste recycling factors, such as locations, frequency, quality and quantity, and established a reverse logistics network multi-objective optimi...

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Published in:2008 IEEE International Conference on Networking, Sensing and Control pp. 1196 - 1200
Main Authors: Yanchao Lu, Pengchao Lu, Litao Liang
Format: Conference Proceeding
Language:English
Published: IEEE 01.04.2008
Subjects:
Cost function
Frequency
Genetic algorithms
Linear programming
Petroleum
Recycling
Reverse logistics
Software algorithms
Software packages
Sustainable development
ISBN:142441685X, 9781424416851
Online Access:Get full text
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Abstract Optimizing reverse logistics network is important to the sustainable development of logistics. In this paper, we fully considered environment effect and the waste recycling factors, such as locations, frequency, quality and quantity, and established a reverse logistics network multi-objective optimization model based on them. This model can ensure that both the whole cost of network and the negative impact on the environment are minimum. We improved the genetic algorithm (GA) by combining it with random weight method, and then apply it to solve the model. The simulation results show that solution is the global optimization, and the method we proposed is simpler and more effective than traditional algorithms.
AbstractList Optimizing reverse logistics network is important to the sustainable development of logistics. In this paper, we fully considered environment effect and the waste recycling factors, such as locations, frequency, quality and quantity, and established a reverse logistics network multi-objective optimization model based on them. This model can ensure that both the whole cost of network and the negative impact on the environment are minimum. We improved the genetic algorithm (GA) by combining it with random weight method, and then apply it to solve the model. The simulation results show that solution is the global optimization, and the method we proposed is simpler and more effective than traditional algorithms.
Author Pengchao Lu
Litao Liang
Yanchao Lu
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Snippet Optimizing reverse logistics network is important to the sustainable development of logistics. In this paper, we fully considered environment effect and the...
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SubjectTerms Cost function
Frequency
Genetic algorithms
Linear programming
Petroleum
Recycling
Reverse logistics
Software algorithms
Software packages
Sustainable development
Title Multi-objective Optimization of Reverse Logistics Network Based on Random Weights and Genetic Algorithm
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