Shiraishi, T., Nagata, M., Shibata, N., Murata, Y., Yasumoto, K. and Ito, M.: A Personal Navigation System with a Schedule Planning Facility Based on Multiobjective Criteria, Proceedings of the 2nd International Conference on Mobile Computing and Ubiquitous Networking (ICMU2005), pp.104-109, (April 2005) http://ito-lab.naist.jp/themes/pdffiles/icmu05-takayu-s.pdf In our previous work, we have proposed a personal navigation system called P-Tour, which facilitates tourists to compose a schedule to visit multiple destinations taking into account their preferences and time restrictions. In this paper, we extend P-Tour in the following two ways: (1) allowing users to optimize their tour schedules under multiple conflicting criteria such as total expenses and satisfaction degrees; and (2) navigating users to the next destination in more efficient way. We have implemented the above extensions and integrated them into P-Tour. Through some experiments, we show the effectiveness of the proposed extensions.

1. A Personal Navigation System with a Schedule Planning Facility Based on Multi-Objective Criteria Takayuki Shiraishi, Munenobu Nagata, Naoki Shibata*, Yoshihiro Murata, Keiichi Yasumoto , and Minoru Ito Nara Institute of Science and Technology (NAIST) * Shiga University

4. Snapshots of P-Tour Calculation/display of schedule Current position Navigation Time table Route User input at each dest route to next dest

5. System structure of P-Tour

11. How to adjust coefficients Users can find best coefficients by following steps server search engine I want to go to National museum. This kind of trial-and-error interaction may take a lot of time new coefficients (  =-0.2,  =0.8) coefficients (  =-0.5,  =0.5) schedule new schedule New It takes a long way to get there... I won’t go. ×

14. Dominant solutions Normal train Shinkansen time: 1hr, expense: $60 time: 3hr, expense: $30 Airplane and normal train time: 6hr, expense: $200 cost time dominate Solutions which are not dominated by any other: Pareto optimal solutions dominate Not dominate each other

15. Pareto optimal solutions cost time Set of candidate solutions Multi-objective optimization problem optimization optimization Set of pareto optimal solutions

18. How to obtain various solutions? cost time ＋ ＋ ＋ ＋ ＋ We want to uniformly obtain Pareto optimal solutions. Elite preservation strategy of GA preserve candidate solutions far from the nearest neighbor for next generation. cost time ＋ ＋ ＋ ＋ ＋ Only part of Pareto optimal solutions may be obtained. ＋ ＋ ＋ ＋ ＋ undesirable

19. How to obtain high quality solutions with GA? cost time Set of candidates ＋ ＋ ＋ Local search search better solutions in the space around GA solutions ＋ ： solutions by GA With local search, we can obtain high quality solutions difficult to find by GA ＋ ＋ ＋ ＋ ： solutions by local search

26. Variety of obtained schedules Satisfaction (maximize) Expense (minimize) Satisfaction 253 Expense 2620 yen Satisfaction 126 Expense 0 yen Satisfaction 210 Expense 1220 yen

27. Optimality of computed schedules Satisfaction (maximize) Expense (minimize) Number of destinations: 18 optimal solution Approximate solutions (computation time: 15sec) ×