Multi-objective route planning for the transportation of dangerous goods: Hong Kong as a case study.
A real-life application in optimal route planning for the transportation of liquefied petroleum gas (LPG) in Hong Kong was performed to implement the proposed framework. A set of criteria fitting the context of Hong Kong were defined, and various optimal routing solutions with diverse compromise in...
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| Format: | Others |
| Language: | English Chinese |
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2010
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| Online Access: | http://library.cuhk.edu.hk/record=b6075002 http://repository.lib.cuhk.edu.hk/en/item/cuhk-344635 |
| Summary: | A real-life application in optimal route planning for the transportation of liquefied petroleum gas (LPG) in Hong Kong was performed to implement the proposed framework. A set of criteria fitting the context of Hong Kong were defined, and various optimal routing solutions with diverse compromise in different objectives were generated. The implementation of the proposed methodologies enables the avoidance of the pitfalls of preference-based techniques and the burden of generating a complete set of possible solutions, and provides decision-makers with an overview of the solution space and the possible trade-offs among the conflicting objectives. The application study demonstrated the effectiveness of the proposed methodologies. In light of the study results and limitations, some recommendations are provided for future research. === Effective and rational routing of DGs is one of the powerful means to mitigate the DG transportation risk. DG transportation involves multiple stakeholders playing different roles and having different objectives that are generally conflicting. The solution of such problem is to search for one or a set of "compromise" solutions rendering the best possible trade-offs for conflict resolution among different objectives. Given the multi-objective nature of the DG routing problem, multi-objective optimization (MOP) becomes a sound framework for analysis and decision-making. === The transportation of dangerous goods (DGs) can significantly affect human life and the environment if accidents occur during the transportation process. Such accidents can result in traffic disruption, fatalities, property and environmental damages. Therefore, safe DG transportation is of paramount importance, especially in high-density-living environments where population and socioeconomic activities are densely distributed over the transportation network. === This research establishes a general framework for optimal route planning for DG transportation in a high-density-living environment. Within the framework, multi-criteria risk assessment and multi-objective route planning can be efficiently solved by novel compromise programming models and high performance algorithms. Non-linearity and non-convexity often exist in the optimal DG routing problem which cannot be solved appropriately by conventional models such as the weighed sum approach. This research has proposed three novel methods to facilitate the generation of a set of optimal solutions on the Pareto front representing various trade-offs among the conflicting objectives. The proposed methodologies give full consideration to decision-makers' inclination and capability in determining the weights for different criteria. The compromise programming procedure allows decision-makers to exercise their preference structures in pursuing desired solutions rendering good compromises among different objectives. The adaptive weighting method approximates the Pareto front with a few suitable solutions to help decision-makers select the most satisfactory route without generating all of them. The genetic-algorithm-based approach uses a set of specifically designed genetic operators to efficiently capture a wide range of Pareto-optimal and near-optimal solutions, from which a decision-maker can choose the most preferred or best compromise one to implement. The diversity of methodologies provides decision-makers with more flexibility in choosing appropriate MOP methods to route DG shipments. === Li, Rongrong. === Adviser: Yee Leung. === Source: Dissertation Abstracts International, Volume: 73-01, Section: A, page: . === Thesis (Ph.D.)--Chinese University of Hong Kong, 2010. === Includes bibliographical references (leaves 189-203). === Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. === Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. === Abstract also in Chinese. |
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