邀请人：许项东 副教授
时间：2017年4月25日（周二）10:00-11:30
地点：36365线路检测中心no1103报告厅
主讲人简介
    Dr Wai Yuen Szeto is an Associate Professor at the Department of Civil Engineering at The University of Hong Kong, and the Deputy Director of Institute of Transport Studies at that university. He is also a Top 1 % Scholars 2015 and 2016 (according to ISI's Essential Science Indicators). His current h-index is 32 (Google scholars). Dr Szeto is an author of more than 100 refereed journal papers. The papers are related to public bike, dynamic traffic assignment, transport network design, public transport, network reliability, transport big data, taxi, game theoretic approaches to transport and logistic problems, modeling land use, transport and environment interaction, and sustainable transport. Currently, Dr Szeto is an Editor of Transportmetrica B and Open Engineering, the Editor in Asian Region of International Journal of Transportation, an Area Editor of Networks and Spatial Economics, an Associate Editor of Journal of Intelligent Transportation Systems, Transportmetrica A, Travel Behaviour and Society, and an Editorial Board Member of Transportation Research Part B, Transportation Research Part C, Journal of Advanced Transportation, International Journal of Sustainable Transportation, and International Journal of Traffic and Transportation Engineering. He is also a Guest Editor of 8 journals and a reviewer of about 60 international journals.
主讲内容简介
    A bike repositioning problem (BRP) that simultaneously considers total demand dissatisfaction and service time is investigated. Given the conditions of each bike station before the repositioning, the problem aims to determine the routes of the repositioning vehicles that minimize the service time while the total demand dissatisfaction should be kept below an overall tolerable limit. This study proposes two service times to be minimized: the total service time of the fleet and the maximum route duration. To solve the BRP, this study develops an efficient solution method that employs the enhanced artificial bee colony (ABC) algorithm to determine the routing sequences. To improve the effectiveness of the solution process, an enhanced version is proposed to improve the solution quality of the original version. The performance of the modified heuristic was evaluated and compared with the original heuristic and the Genetic Algorithm (GA). The computational results showed that the enhanced heuristic outperforms both the original ABC algorithm and the GA with similar computation time. These results therefore demonstrate that the modified heuristic can be an alternative to solve the BRP. The numerical studies demonstrate that an increase in fleet size may not lead to a lower service time. The studies also illustrate the trade-offs between each objective with tolerance of total demand dissatisfaction, the trade-off between the two service time objectives, and the effect of fleet size. The practical implications of the trade-offs are discussed.

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