本研究將介紹行動車輛途程問題(Mobile Vehicle Routing Problem; MVRP)，行動車輛途程問題為移動設施途程問題(Mobile Facility Routing Problem; MFRP)與車輛途程問題之結合(Vehicle Routing Problem; VRP)，包含行動車輛途程問題的變動需求時間、可移動式特色以及車輛途程問題的路線規則，使得行動車輛途程問題更具有彈性。而本研究根據台灣在地商業模式，也就是行動車輛攤商為範本，選擇人口密度之冠的台北市為區域，透過對現行行動車輛攤商運行模式及在地限制之資料收集，以及對人口密度最高之城市進行研究，使本研究在此問題能有更完善之規劃。另外，本研究根據現況以及所收集資訊建立適用本問題之題庫，同時也使用經典題庫修改成符合此問題之題庫，使本研究能有更高的完整性。在演算法部分，本研究以貪婪演算法(Greedy Algorithm)為基礎發展一啟發式演算法求解此行動車輛途程問題，從測試結果可以得知，啟發式演算法具有優越的解題能力及穩定的運算速度，尤其在解題速度上更有其絕對的優勢。

This research introduces the Mobile Vehicle Routing Problem (MVRP), which is a combination of Mobile Facility Routing Problem (MFRP) and Vehicle Routing Problem (VRP). The demand serviced by a mobile facility depends on the arrival and departure times and this combine with the VRP route rules make MVRP more flexible. This study is based on Taiwan's local business model, which is a model for mobile vehicle vendors. Taipei City, which is the crown of population density, is selected as the region. Data collection and research through the operation mode and local restrictions of mobile vehicle vendors. This study can have more detailed and complete planning. In addition, this study builds data set of questions that apply to this issue based on current status and information collected. At the same time, the classic data set is also used to modify the database to meet this problem. It gives the study a higher level of integrity. In this study, the greedy algorithm (Greedy Algorithm) is used to solve this problem. It can be known from the test results that GA has superior problem-solving ability and computing speed, especially in solving the problem speed.

參考文獻
Albareda-Sambola, M., Fernández, E., Hinojosa, Y., Puerto, J. J. C., & Research, O. (2009). The multi-period incremental service facility location problem. 36(5), 1356-1375.
Allahviranloo, M., Chow, J. Y., Recker, W. W. J. T. R. P. E. L., & Review, T. (2014). Selective vehicle routing problems under uncertainty without recourse. 62, 68-88.
Antunes, A., Berman, O., Bigotte, J., Krass, D. J. E., & A, P. (2009). A location model for urban hierarchy planning with population dynamics. 41(4), 996-1016.
Baita, F., Pesenti, R., Ukovich, W., Favaretto, D. J. C., & Research, O. (2000). A comparison of different solution approaches to the vehicle scheduling problem in a practical case. 27(13), 1249-1269.
Ballou, R. H. J. J. o. M. R. (1968). Dynamic warehouse location analysis. 5(3), 271-276.
Benton, W. J. I. t. (1986). Evaluating a modified heuristic for the multiple-vehicle scheduling problem. 18(1), 70-78.
Brauer, S. J. T. C. S. (2019). Complexity of single-swap heuristics for metric facility location and related problems. 754, 88-106.
Bräysy, O., & Gendreau, M. J. T. s. (2005). Vehicle routing problem with time windows, Part II: Metaheuristics. 39(1), 119-139.
Canel, C., & Khumawala, B. M. J. I. J. o. P. R. (1997). Multi-period international facilities location: An algorithm and application. 35(7), 1891-1910.
Cardoso, P. J., Schütz, G., Mazayev, A., Ey, E., & Corrêa, T. J. P. C. S. (2015). A solution for a real-time stochastic capacitated vehicle routing problem with time windows. 51, 2227-2236.
Chen, Q., Li, K., Liu, Z. J. T. R. P. E. L., & Review, T. (2014). Model and algorithm for an unpaired pickup and delivery vehicle routing problem with split loads. 69, 218-235.
Cheung, S.-S., & Williamson, D. P. J. O. R. L. (2017). Greedy algorithms for the single-demand facility location problem. 45(5), 452-455.
Church, R., & ReVelle, C. J. P. i. r. s. (1974). The maximal covering location problem. 32(1), 101-118.
Contreras, I., Cordeau, J.-F., & Laporte, G. J. T. S. (2011). The dynamic uncapacitated hub location problem. 45(1), 18-32.
Current, J., Ratick, S., & ReVelle, C. J. E. J. o. O. R. (1998). Dynamic facility location when the total number of facilities is uncertain: A decision analysis approach. 110(3), 597-609.
Dantzig, G. B., & Ramser, J. H. J. M. s. (1959). The truck dispatching problem. 6(1), 80-91.
Drezner, Z., & Wesolowsky, G. J. N. R. L. (1991). Facility location when demand is time dependent. 38(5), 763-777.
Drezner, Z. J. L. S. (1995). Dynamic facility location: The progressive p-median problem. 3(1), 1-7.
Golden, B. L., Raghavan, S., & Wasil, E. A. (2008). The vehicle routing problem: latest advances and new challenges (Vol. 43): Springer Science & Business Media.
Halper, R., & Raghavan, S. J. T. S. (2011). The mobile facility routing problem. 45(3), 413-434.
Laporte, G. J. E. j. o. o. r. (1992). The vehicle routing problem: An overview of exact and approximate algorithms. 59(3), 345-358.
Laporte, G. J. T. S. (2009). Fifty years of vehicle routing. 43(4), 408-416.
Lei, C., Lin, W.-H., Miao, L., & Qi, M. (2013). Stochastic mobile facility routing and scheduling problem. Paper presented at the 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).
Lei, C., Lin, W.-H., Miao, L. J. C., & Research, O. (2016). A two-stage robust optimization approach for the mobile facility fleet sizing and routing problem under uncertainty. 67, 75-89.
Lei, C., Lin, W.-H., & Miao, L. J. E. J. o. o. r. (2014). A multicut L-shaped based algorithm to solve a stochastic programming model for the mobile facility routing and scheduling problem. 238(3), 699-710.
Lei, C., Lin, W.-H., & Miao, L. J. I. T. o. I. T. S. (2014). A stochastic emergency vehicle redeployment model for an effective response to traffic incidents. 16(2), 898-909.
Lin, C., Choy, K. L., Ho, G. T., Chung, S. H., & Lam, H. J. E. s. w. a. (2014). Survey of green vehicle routing problem: past and future trends. 41(4), 1118-1138.
Litvinchev, I., & Ozuna, E. L. J. I. P. V. (2013). Lagrangian Heuristic for the Facility Location Problem. 46(24), 107-113.
Murray, A. T. J. I. R. S. R. (2016). Maximal coverage location problem: impacts, significance, and evolution. 39(1), 5-27.
Orloff, C. S. J. T. S. (1976). Route constrained fleet scheduling. 10(2), 149-168.
Parragh, S. N., Doerner, K. F., & Hartl, R. F. J. J. f. B. (2008). A survey on pickup and delivery problems. 58(1), 21-51.
Russell, R. A., & Urban, T. L. J. J. o. t. O. R. S. (2008). Vehicle routing with soft time windows and Erlang travel times. 59(9), 1220-1228.
Şahinyazan, F. G., Kara, B. Y., & Taner, M. R. J. E. J. o. O. R. (2015). Selective vehicle routing for a mobile blood donation system. 245(1), 22-34.
Taş, D., Dellaert, N., van Woensel, T., & De Kok, T. J. T. R. P. C. E. T. (2014). The time-dependent vehicle routing problem with soft time windows and stochastic travel times. 48, 66-83.
Taş, D., Gendreau, M., Dellaert, N., Van Woensel, T., & De Kok, A. J. E. J. o. O. R. (2014). Vehicle routing with soft time windows and stochastic travel times: A column generation and branch-and-price solution approach. 236(3), 789-799.
Torres-Soto, J. E., & Üster, H. J. I. J. o. P. R. (2011). Dynamic-demand capacitated facility location problems with and without relocation. 49(13), 3979-4005.
Toth, P., & Vigo, D. (2014). Vehicle routing: problems, methods, and applications: SIAM.
Victoria, J. F., Afsar, H. M., & Prins, C. J. I.-P. (2016). Column generation based heuristic for the vehicle routing problem with time-dependent demand. 49(12), 526-531.
Wesolowsky, G. O., & Truscott, W. G. J. M. S. (1975). The multiperiod location-allocation problem with relocation of facilities. 22(1), 57-65.
Wesolowsky, G. O. J. M. S. (1973). Dynamic facility location. 19(11), 1241-1248.
Xu, Y., Xu, D., Du, D., & Wu, C. J. T. C. S. (2017). Improved approximation algorithm for universal facility location problem with linear penalties.
吳克屏，(2007)。行動咖啡館創業家的社會流動與其自主性空間建構之研究，1-162。
林儒辰，(2012)。以Kano及TRIZ探討行動餐車之服務品質策略。(碩士)，朝陽科技大學，台中市。
張凱瑄，(2015)。從政府法規角度探討臺灣行動餐車發展之研究。(碩士)，淡江大學，新北市。
梁海，(2003)。連鎖加盟關鍵成功因素之研究─以移動式販賣為例。(碩士)，大葉大學，彰化縣。
傅婉禎，(2005)。日本「歐夏蕾」新興「行動攤販」。