偏鄉運輸的發展一直是近幾年政府以及社會各界所關注的議題，然而由於偏鄉運輸本身的特殊條件，供給稀少、需求零散、資源有限，使的在推動的過程中受到種種的限制，傳統公共運輸或需求反應式服務，難以符合民眾期望與達到永續發展的可能性。為了降低營運成本、提高偏鄉車輛的利用率，本研究以偏鄉所有可做為交通載具的運輸供給者為研究對象，依照多車種fleet size and mix vehicle routing problem(FSMVRP)問題特性，並同量考量模型引入複合式載具民眾會遇到的轉乘需求，設計出符合偏鄉居民交通出行的演算法。
本研究將分兩個階段來規劃所有路線，第一階段先依照Transit Capacity and Quality of Service Manual (TCQSM)將所有出行旅次做是否轉乘以及轉乘載具的評估，計算出預期等待時間以及轉乘前後搭乘的載具後將資料傳入後端的演算法模型，進行車輛路線的安排，第二階段透過禁忌演算法將物流業者送貨點與民眾需求點做整合，納入需由復康巴士接送的輪椅需求後，將剩餘的需求點由其他載具做接送。此模型將以Python開發環境來撰寫程式，並針對(Yu-Hsun, Tsai, 2020)、(Jorgensen, et al., 2007)研究模型進行改良，調整載客的機制以及時窗限制等數學式，最後針對VRP例題網站之公開標準例題進行測試與比較。結論方面，根據本次實驗的數據顯示，此轉乘模型將增加17%的總旅行時間，主要來自於兩次跨運具轉乘產生的時間間隙，另外可減少30%營運成本並可以多服務25%乘客，這也讓偏鄉珍貴的運輸資源能更有效地利用。

Recently, thanks to advance in regulatory and the fully developed of MaaS concept, rural transportation has become one of the most critical issue. However, due to its specific constraint such as the provision of transport services is relatively low and infrequent, the scattered passenger demands and the limitation of the transportation resources, all make the process of promotion getting harder than normal condition. Besides, existing public transportation and dial- a-ride system are no longer living up to people expectations. Current transportation facilities in rural area are strongly rely on government subsidies. Without public sectors support, the possibility of achieving sustainable development is very little. In order to decrease the operation costs and increase the utility of rural transportation services. This study put emphasis on integrating all kinds of potential vehicles, and then coming up with an algorithm that meets the demands of Taiwan’s rural residents based on fleet size and mix vehicle routing problem(FSMVRP).
This study will divide into two parts. In the first part we take possibility of Multi-Carrier Transit into consideration, according to Transit Capacity and Quality of Service Manual (TCQSM), we would assess which vehicles are suitable for rural areas also calculate the transit waiting time and the Seamless Transfer Index. In second part, through Tabu search Algorithm we can optimize the result that generate from first part and then compared this data with reality one. Based on the result of this study, the operation cost could significantly cut down 30% while just increase 17% of average traveling time due to the transfer time gap. Moreover, under the same amount of the Handicap Service bus, this study could satisfy 25% more customers than in real situation.

Chapter 1. Introduction 1
Chapter 2. Literature review 4
Chapter 3. Methodology 11
Chapter 4. Computational experiment and analysis 23
Chapter 5. Conclusion and Further research 35

Augerat, P., Belenguer, J. M., Benavent, E., Corbéran, A., & Naddef, D. (1998). Separating Capacity Constraints in the CVRP using Tabu Search. European Journal of Operational Research, 106(2-3), 546-557.
Balbiani, P., & Jean-François, C. (2002, April). Computational Complexity of Propositional Linear Temporal Logics Based On Qualitative Spatial or Temporal Reasoning. In International Workshop on Frontiers of Combining Systems (pp. 162-176). Springer, Berlin, Heidelberg.
Brandão, J. (2011). A Tabu Search Algorithm for the Heterogeneous Fixed Fleet Vehicle Routing Problem. Computers & Operations Research, 38(1), 140-151.
Carić, T., Galić, A., Fosin, J., Gold, H., & Reinholz, A. (2008). A Modelling and Optimization Framework for Real-World Vehicle Routing Problems (p. 142). IntechOpen.
Cordeau, J. F., & Laporte, G. (2003). A Tabu Search Heuristic for the Static Multi-Vehicle Dial-A-Ride Problem. Transportation Research Part B: Methodological, 37(6), 579-594.
Crevier, B., Cordeau, J. F., & Laporte, G. (2007). The Multi-Depot Vehicle Routing Problem with Inter-Depot Routes. European journal of operational research, 176(2), 756-773.
Deflorio, F. P., Dalla Chiara, B., Murro, A., & SpA, M. A. (2002, June). Simulation and Performance of DRTS in a Realistic Environment. In Proceedings of the 13th Mini-Euro Conference Handling uncertainty in the analysis of Traffic and Transportation systems and the 9th Meeting of the Euro Working Group on Transportation Intermodality, Sustainability and Intelligent transport systems, 622-628.
Delmelle, E. C., & Casas, I. (2012). Evaluating the Spatial Equity of Bus Rapid Transit-Based Accessibility Patterns in a Developing Country: The case of Cali, Colombia. Transport Policy, 20, 36-46.
Desaulniers, G., Desrosiers, J., Erdmann, A., Solomon, M. M., & Soumis, F. (2002). VRP with Pickup and Delivery. The vehicle routing problem, 9, 225-242.
Dror, M. (1994). Note on the Complexity of the Shortest Path Models for Column Generation in VRPTW. Operations Research, 42(5), 977-978.
Eckhardt, J., Nykänen, L., Aapaoja, A., & Niemi, P. (2018). MaaS in Rural Areas-Case Finland. Research in Transportation Business & Management, 27, 75-83.
Gambardella, L. M., Taillard, É., & Agazzi, G. (1999). Macs-rptw: A Multiple Colony System for Vehicle Routing Problems with Time Windows. In New ideas in optimization.
Ganesh, K., & Narendran, T. T. (2007). CLOVES: A Cluster-and-Search Heuristic to Solve the Vehicle Routing Problem with Delivery and Pick-up. European Journal of Operational Research, 178(3), 699-717.
Goetschalckx, M., & Jacobs-Blecha, C. (1989). The Vehicle Routing Problem with Backhauls. European Journal of Operational Research, 42(1), 39-51..
Golden, B., Assad, A., Levy, L., & Gheysens, F. (1984). The Fleet Size and Mix Vehicle Routing Problem. Computers & Operations Research, 11(1), 49-66.
Hu, Y. Z., & Xu, B. G. (2015). Differential Approximation Algorithm of FSMVRP. Acta Mathematicae Applicatae Sinica, English Series, 31(4), 1091-1102.
Jie-Yun Chen. (2020). The Effects of Taxi-Bus and Taxi-pool on the Tourist’s Mode Choice Behavior – A Case Study of Yilan area. National Taiwan Normal University Master Thesis.
Jin-Yuan Wang. (2020). Multi-carrier with seamless integration of the system architecture of advanced public transport system.
Jorgensen, R. M., Larsen, J., & Bergvinsdottir, K. B. (2007). Solving the dial-a-ride problem using genetic algorithms. Journal of the operational research society, 58(10), 1321-1331.
Kamargianni, M., Matyas, M., Li, W., & Schäfer, A. (2015). Feasibility Study for “Mobility as a Service Concept” in London. FS-MaaS Project-Final Deliverable.
Koloch, G., & Kaminski, B. (2010). Nested vs. Joint Optimization of Vehicle Routing Problems with Three-dimensional Loading Constraints. Engineering Letters, 18(2).
Longhi, D. (2020). Mobility as a Service (MaaS) in Suburban and Rural areas: Concept Design and Challenges.
Min, H., Current, J., & Schilling, D. (1992). The Multiple Depot Vehicle Routing Problem with Backhauling. Journal of Business Logistics, 13(1), 259.
Mitra, S. (2008). A Parallel Clustering Technique for the Vehicle Routing Problem with Split Deliveries and Pickups. Journal of the operational Research Society, 59(11), 1532-1546.
Mosheiov, G. (1998). Vehicle Routing with Pick-up and Delivery: Tour-Partitioning Heuristics. Computers & Industrial Engineering, 34(3), 669-684.
Mostafa, N., & Eltawil, A. (2017). Solving the Heterogeneous Capacitated Vehicle Routing Problem using K-means Clustering and Valid Inequalities. In Proceedings of the international conference on industrial engineering and operations management..
Narupiti, S. (2019). Exploring the Possibility of MaaS Service in Thailand, Implications from the Existing Conditions and Experts' Opinions on “Who Should be the MaaS Provider in Bangkok?”. IATSS research, 43(4), 226-234.
Nowicki, E., & Smutnicki, C. (1996). A Fast Taboo Search Algorithm for the Job Shop Problem. Management science, 42(6), 797-813.
Petch, R. J., & Salhi, S. (2003). A Multi-Phase Constructive Heuristic for the Vehicle Routing Problem with Multiple Trips. Discrete Applied Mathematics, 133(1-3), 69-92.
Pureza, V., Morabito, R., & Reimann, M. (2012). Vehicle Routing with Multiple Deliverymen: Modeling and Heuristic Approaches for the VRPTW. European Journal of Operational Research, 218(3), 636-647.
Renaud, J., & Boctor, F. F. (2002). A Sweep-Based Algorithm for the Fleet Size and Mix Vehicle Routing Problem. European Journal of Operational Research, 140(3), 618-628.
Ropke, S., & Pisinger, D. (2006). An Adaptive Large Neighborhood Search Heuristic for the Pickup and Delivery Problem with Time Windows. Transportation science, 40(4), 455-472.
Taillard, É. D. (1999). A Heuristic Column Generation Method for the Heterogeneous Fleet VRP. RAIRO-Operations Research-Recherche Opérationnelle, 33(1), 1-14.
Wu, B. M., & Hine, J. P. (2003). A PTAL Approach to Measuring Changes in Bus Service Accessibility. Transport Policy, 10(4), 307-320.
Wu, T. H., Low, C., & Bai, J. W. (2002). Heuristic Solutions to Multi-Depot Location-Routing Problems. Computers & Operations Research, 29(10), 1393-1415.
Yen-Hung Chou & Jen-Wei Tu & Mu-Han Wang & Chien-Pang Liu & Chiu-Yen Peng & Tzu-Yin LIN. (2020). The Study of Multiple Vehicle Sharing Service in Rural Areas. Urban Traffic, 35(2), 11-27.
Yu-Hsun, Tsai. (2020). Solving Combined Passenger-Cargo Dial-a-Ride Problem by Variable Neighborhood Search Algorithm.