Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/16527
Title: A Genetic Algorithm-based optimization model for supporting green transportation operations
Authors: Lin, C
Choy, KL 
Ho, GTS
Ng, TW
Keywords: Emission
Genetic Algorithm
Recycle
Reverse logistics
Vehicle routing
Issue Date: 2014
Publisher: Pergamon Press
Source: Expert systems with applications, 2014, v. 41, no. 7, p. 3284-3296 How to cite?
Journal: Expert systems with applications 
Abstract: Green Logistics (GL) has emerged as a trend in the management of the distribution of goods and the collection of end-of-life products. With its focus on maximizing the economic and environmental value by means of recycling and emission control, GL contributes to the sustainable development of industry but also requires a more comprehensive transportation scheme when conducting logistics services. This study is motivated by the practice of delivering and collecting water carboys. In this paper, a Genetic Algorithm-based optimization model (GOM) is proposed for designing a green transportation scheme of economic and environmental cost efficiency in forward and reverse logistics. Two vehicle routing models with simultaneous delivery and pickup (full or partial pickup) are formulated and solved by a Genetic Algorithm. A cost generation engine is designed to perform a comprehensive cost comparison and analysis based on a set of economic and environmental cost factors, so as to examine the impact of the two models and to suggest optimal transportation schemes. The computational experiments show that the overall cost is evidently lower in the full pickup model. Notably, the impact of product cost after recycling and reusing empty carboys on total cost is more significant than the impact of transportation cost and CO2 emission cost. In summary, the proposed GOM is capable of suggesting a guidance for the logistics service providers, who deal with green operations, to adopt a beneficial transportation scheme so as to eventually achieve a low economic and environmental cost.
URI: http://hdl.handle.net/10397/16527
ISSN: 0957-4174
EISSN: 1873-6793
DOI: 10.1016/j.eswa.2013.11.032
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