| Project/Area Number |
13650584
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
交通工学・国土計画
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| Research Institution | KYOTO UNIVERSITY |
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Principal Investigator |
TANIGUCHI Eiichi Kyoto University, Faculty of Engineering, Professor, 工学研究科, 教授 (70252468)
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| Co-Investigator(Kenkyū-buntansha) |
YAMADA Tadashi Hiroshima University, Faculty of Engineering, Associate Professor, 工学研究科, 助教授 (80268317)
KURAUCHI Fumitaka Kyoto University, Faculty of Engineering, Research Associate, 工学研究科, 助手 (10263104)
UNO Nobuhiro Kyoto University, Faculty of Engineering, Associate Professor, 工学研究科, 助教授 (80232883)
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| Project Period (FY) |
2001 – 2002
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| Project Status |
Completed (Fiscal Year 2002)
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| Budget Amount *help |
¥3,300,000 (Direct Cost: ¥3,300,000)
Fiscal Year 2002: ¥1,900,000 (Direct Cost: ¥1,900,000)
Fiscal Year 2001: ¥1,400,000 (Direct Cost: ¥1,400,000)
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| Keywords | Freight transport / Transport planning / ITS / Travel time information / Vehicle routing and scheduling / 確率論 |
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| Research Abstract |
This study presented probabilistic vehicle routing and scheduling with time windows and dynamic vehicle routing and scheduling with time windows.
We developed probabilistic vehicle routing and scheduling with time windows model taking into account the variable travel times on network links. The model can calculate penalty of early arrival and delay at customers by estimating the distribution of arrival at customers. The model minimizes the total costs. This model was applied to a test road network. The results showed that probabilistic vehicle routing and scheduling model reduced the total costs of truck operations as well as CO_2 emissions. Therefore. probabilistic vehicle routing and scheduling can be a useful method for efficient and environmentally friendly urban logistics systems.
We also developed dynamic vehicle routing and scheduling with time windows model using real time information on travel times. The model can adjust the allocation of trucks to customers and route for visiting customers. Genetic algorithms were used for identifying rapidly the approximate solutions. The model was applied to a test road network. The results showed that probabilistic vehicle routing and scheduling model reduced the total costs of truck operations as well as the total running times. Therefore, the model can contribute to generate benefits not only for freight carriers in terms of cost reduction but also for society at large in terms of alleviating congestion and improving the environment.
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