Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Industrial and Systems Engineering | en_US |
| dc.contributor.advisor | Lee, Carman (ISE) | - |
| dc.creator | Zhang, Shuzhu | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/8779 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | - |
| dc.rights | All rights reserved | en_US |
| dc.title | Modeling green logistics activities for sustainable development using swarm intelligence | en_US |
| dcterms.abstract | Global warming, environment deterioration and government regulation arouse the awareness of academic researchers and industrial practitioners to consider green strategies in logistics industry, which prompts the research of green logistics. Green logistics involves a number of activities which are operated for the purpose of sustainable development. The performance of green logistics cannot be measured simply in an economic way, but in a more comprehensive and sustainable way by taking account of environmental and social considerations as well. In order to facilitate the development of green logistics, the activities of green logistics shall be analyzed and modeled by incorporating the latest environmental and social requirements. Most of the activities in green logistics can be modelled as combinatorial optimization problems with single or multiple objectives, constraints and decision variables. Exact algorithms are less popular to solve these combinatorial optimization problems due to their high complexity and large scale. In this research, swarm intelligence is employed to solve the combinatorial optimization problems derived from green logistics. The integration of green logistics and swarm intelligence is pioneering, which helps to solve the green logistics problems efficiently and broaden the application scope of swarm intelligence simultaneously. Two typical activities, i.e., vehicle scheduling and network design, are chosen to exemplify the modeling of green logistics activities and the application of swarm intelligence. The first activity is to propose an environmental vehicle routing model, which measures the carbon dioxide emission in addition to the economic cost along with the vehicle travelling. The second activity is to design a supply chain network with multiple distribution channels, which meets the development requirements of e-commerce. Swarm intelligence is employed to solve both problems by integrating with other programming skills. The results show that the modeling of green logistics activities are practicable and necessary, and swarm intelligence is capable and competitive to solve green logistics problems. The contribution of this research is the modeling of green logistics activities by integrating the concept of sustainable development and the design of swarm intelligence into solving combinatorial optimization problems. Both the activity modeling and algorithm design can provide useful insights and guidance for the interdisciplinary research of green logistics and swarm intelligence. Moreover, a unified swarm intelligence algorithm framework is proposed in consideration of the common procedures and operators from different swarm intelligence algorithms and a number of strategies are provided as well for the implementation of this unified algorithm framework. | en_US |
| dcterms.extent | 1 online resource (xiii, 205 pages) : color illustrations | en_US |
| dcterms.extent | xiii, 205 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2016 | en_US |
| dcterms.educationalLevel | All Doctorate | en_US |
| dcterms.educationalLevel | Ph.D. | en_US |
| dcterms.LCSH | Business logistics -- Environmental aspects. | en_US |
| dcterms.LCSH | Swarm intelligence. | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | open access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b29290971.pdf | For All Users | 2.31 MB | Adobe PDF | View/Open |
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