A multi-objective reverse logistics network for product returns

Lo, Chien-Chung; Chen, H.M.; Huang, Hua

doi:10.1109/ieem.2008.4738149

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…Markov chain [85] Object-oriented Petri nets [86] Bayesian network modelling [87] Fuzzy logic [88] Rough sets [89] Neighbourhood rough sets [90] Metaheuristic Genetic Algorithm Multi-objective evolutionary Algorithm Multi-objective differential evolution algorithm Particle swarm optimisation Ant Colony Optimization [91][92][93][94][95][96]…”

Section: Simple Heuristicmentioning

confidence: 99%

Case Study of Municipal Waste and Its Reliance on Reverse Logistics in European Countries

2022

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The authors have examined municipal waste, its components and their integration with reverse logistics processes. Background: The theoretical part begins with a definition of municipal waste. Later, the integration between municipal waste and reverse logistics is provided, including presentation of the hierarchy of qualitative methods and models. Methods: The authors constructed a correlation matrix and applied a dynamic regression model to identify that the level of municipal waste impacts recycling of biowaste which demands reverse logistics. Results: The authors provided a dynamic regression model which could be applied for forecasting the size of recycled municipal waste into biowaste indicated in European Union countries. Conclusions: The variety of components in municipal waste prevents the increase of the recycling rates and has to be changed to ones that have higher recycling rates.

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