碩士 / 淡江大學 / 運輸管理學系 / 91 / The basis for the application of Commercial Vehicle Operation (CVO) on the freight transportation is that a dispatch center can monitor and control the vehicle and commodity flow by the vehicle location technology and on-board communication equipments. This will reduce time in detour, pickup and delivery. With the improvement of driver and vehicle dispatching, the efficiency of delivery operation will increase significantly which will enhance the service quality and compatibility of the freight transportation.
With real time motorist information provided by CVO, the purpose of this study is to develop a model, which cans adaptive response to the dynamic nature of the motorist information. This model can be used as a decision support tool in the delivery operation plan to determine an adaptive vehicle routes to reduce the delivery cost. This model is derived form the traditional vehicle routing model with time window. Additional constraints are introduced to reflect the environment factors of this problem. Two meta-heuristic solution approaches incorporated were presented at this paper, i.e., Tabu Search (TS) and Genetic Algorithms (GA) as the basis of the proposed solution procedure.
Due to the NP-Hard complexity of this problem, a global search type heuristic solution is developed for this problem. Furthermore, an integration of these meta-heuristic and an adaptive algorithm is conducted to construct an effective adaptive solution procedure for this study. Based on the decision theory under risks, three decision criteria were incorpoeated in this study to reflect the stochastic nature of this problem.
The test of the proposed solution procedure is conducted in two phases. Phase 1 only the link time is treated as the stochastic elements. In the second phases, the link time and demand are both with stochastic nature.
In each phases, a series of combinations of meta-heuristic solution procedures are tested in different sizes of problem. Based on the test results, the maximum regret criterion is proved to be most suitable criterion for those problems addressed. As for solution algorithm, the combination of Tabu Search and Genetic Algorithms provide the best solution.
| Identifer | oai:union.ndltd.org:TW/091TKU00425010 |
| Date | January 2003 |
| Creators | Hui-Yu Tseng, 曾惠鈺 |
| Contributors | Hsien-Ming Chiu, 邱顯明 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 88 |
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