由於全球消費行為的轉變，使得電子商務產業蓬勃發展，提供消費者更加快速及便利的服務，也刺激供應面的成長；以餐飲業為例，電商的服務範圍由以往的量販店供應商擴展到各種餐廳和店家，而全球出現了食物外送平台，使用者透過平台訂購外食，餐飲店家在平台上曝光增加更多訂單量，同時以共享機車騎士的概念解決外送員人力的問題，但眾多店家反應平台抽成過高，不符合成本效益，而不考慮與外送平台品牌合作。在2020年初因新冠肺炎的爆發，政府要求民眾在家防疫，減少感染風險，導致餐飲業重創，尤其是內用型餐廳，而以外帶為主的店家不受影響，反而需求暴增，讓外送訂單量爆發式成長，如今疫情趨緩，使用者的消費習慣改變，疫情後提供外帶外送的餐飲業者繼續獲利，而本研究對便當專賣店業者進行分析與深入探討。本研究目的是針對以便當專賣店量大多訂單的餐飲店家設計一款外送服務系統的輔助工具，探討派單人員使用的此系統架構與功能，將其外送服務透過使用者旅行地圖討論，分別為訂餐、備餐、送餐去進行專家訪談，透過使用者研究分析規劃系統流程與介面設計等。本研究運用車輛路徑問題，結合Google Maps API擷取各訂單送達點，雙向之行車時間，並結合演算法產生最佳旅程路線建議分配外送員訂單運送路線，提高執行外送服務的工作效率。經實際案例路徑模擬實驗後，相較實驗組與對照組前後減少26%的時間，顯示其系統提升流程的效率與降低派單人員短時間的派單心理壓力，從主觀感受意見調查中，顯示有75%受訪者願意使用其系統，本研究成果提出相關意見，未來可提供外送系統和智能餐飲相關研究之參考。

The global e-commerce industry has been booming in recent years as it enables consumers to access faster and more convenient services. A variety of food delivery platforms have emerged worldwide. However, Taiwanese stores have indicated that platform commissions are high. As a result, they do not find such platforms to be cost-effective. Meanwhile, restaurants have witnessed sudden losses in their dine-in business due to the recent pandemic and social distancing regulations to reduce infection. However, the demand for delivery services has soared. With customer demand shifting, many restaurants have shifted business models to include the delivery of food. This study developed a delivery service system tool for boxed meal catering. The system connects to Google Maps Application Programming Interface (API) that considers two-way travel time and combines the algorithm to suggest an optimal itinerary route. A simulation demonstrates that the system operates efficiently and reduces 26% delivery time significantly. Continued use of the application would further improve operations in this sector, specifically during peak hours. The subjective opinion survey shows that this research's results still need to be verified, but 75% of the respondents are willing to use its system. The products can be used as a reference for other related food delivery or catering businesses.

Bangor, A., Kortum, P. T., & Miller, J. T. (2008). An empirical evaluation of the system usability scale. Intl. Journal of Human–Computer Interaction, 24(6), 574-594.
Becker, H. S., & Geer, B. (2003). Participant observation: The analysis of qualitative field data. In Field research (pp. 376-394): Routledge.
Belhaiza, S., Hansen, P., & Laporte, G. (2014). A hybrid variable neighborhood tabu search heuristic for the vehicle routing problem with multiple time windows. Computers & Operations Research, 52, 269-281.
Cho, M., Bonn, M. A., & Li, J. (2019). Differences in perceptions about food delivery apps between single-person and multi-person households. International Journal of Hospitality Management, 77, 108-116. doi:https://doi.org/10.1016/j.ijhm.2018.06.019
Church, I. (2000). Using simulations in the optimisation of fast food service delivery. British Food Journal, 102(5/6), 398-405. doi:10.1108/00070700010329308
Correa, J. C., Garzón, W., Brooker, P., Sakarkar, G., Carranza, S. A., Yunado, L., & Rincón, A. (2019). Evaluation of collaborative consumption of food delivery services through web mining techniques. Journal of Retailing and Consumer Services, 46, 45-50. doi:https://doi.org/10.1016/j.jretconser.2018.05.002
Crauser, A., Mehlhorn, K., Meyer, U., & Sanders, P. (1998). A parallelization of Dijkstra's shortest path algorithm. Paper presented at the International Symposium on Mathematical Foundations of Computer Science.
Dianjun, C. (2000). The Development of Electronic Commerce. Taiwan Financial Quarterly, 1(1), 139-144.
Duchoň, F., Babinec, A., Kajan, M., Beňo, P., Florek, M., Fico, T., & Jurišica, L. (2014). Path Planning with Modified a Star Algorithm for a Mobile Robot. Procedia Engineering, 96, 59-69. doi:https://doi.org/10.1016/j.proeng.2014.12.098
Dumas, Y., Desrosiers, J., & Soumis, F. (1991). The pickup and delivery problem with time windows. European Journal of Operational Research, 54(1), 7-22.
Gibbons, S. (2017). Service Design 101. Retrieved from https://www.nngroup.com/articles/service-design-101/
Golden, B. L., & Wong, R. T. (1981). Capacitated arc routing problems. Networks, 11(3), 305-315.
Goldstein, S. M., Johnston, R., Duffy, J., & Rao, J. (2002). The service concept: the missing link in service design research? Journal of Operations Management, 20(2), 121-134. doi:https://doi.org/10.1016/S0272-6963(01)00090-0
Hanington, B., & Martin, B. (2012). Universal methods of design: 100 ways to research complex problems, develop innovative ideas, and design effective solutions: Rockport Publishers.
He, Z., Han, G., Cheng, T. C. E., Fan, B., & Dong, J. (2019). Evolutionary food quality and location strategies for restaurants in competitive online-to-offline food ordering and delivery markets: An agent-based approach. International Journal of Production Economics, 215, 61-72. doi:https://doi.org/10.1016/j.ijpe.2018.05.008
Holdings, C. F. (2020). June 2020 National Economic Confidence Survey Results. Retrieved from https://www.cathayholdings.com/holdings/information-centre/intro/latest-news/detail?news=7IYyIy0SsUucrFr73o2a0Q&page=18
Jixian, H., & Jianxiong, C. (2019). Accessible Tourism from the Perspective of Service Design. Industrial Desgin(139), 49-54.
Jorgensen, D. L., & Russell, S. E. (1999). American Neopaganism: The participants' social identities. Journal for the Scientific Study of Religion, 325-338.
Karakatič, S., & Podgorelec, V. (2015). A survey of genetic algorithms for solving multi depot vehicle routing problem. Applied Soft Computing, 27, 519-532. doi:https://doi.org/10.1016/j.asoc.2014.11.005
Laporte, G. (1992). The vehicle routing problem: An overview of exact and approximate algorithms. European Journal of Operational Research, 59(3), 345-358.
Liu, W.-Y., Lin, C.-C., Chiu, C.-R., Tsao, Y.-S., & Wang, Q. (2014). Minimizing the carbon footprint for the time-dependent heterogeneous-fleet vehicle routing problem with alternative paths. Sustainability, 6(7), 4658-4684.
Nielsen, J. (2003). Usability 101: Introduction to usability. In.
Norman, D. A. (1999). Affordance, conventions, and design. interactions, 6(3), 38-43.
Rosson, M. B., & Carroll, J. M. (2009). Scenario-based design. In Human-computer interaction (pp. 161-180): CRC Press.
Statistics Division, M. o. E. A. (2020). Food Delivery and Home Delivery Catering Sector Fighting the Epidemic. Retrieved from https://www.moea.gov.tw/MNS/dos/bulletin/Bulletin.aspx?kind=9&html=1&menu_id=18808&bull_id=6878
Strong, G. W., & O'Neill Strong, K. E. (1991). Visual guidance for information navigation: a computer-human interface design principle derived from cognitive neuroscience. Interacting with Computers, 3(2), 217-231.
Suhartanto, D., Helmi Ali, M., Tan, K. H., Sjahroeddin, F., & Kusdibyo, L. (2019). Loyalty toward online food delivery service: the role of e-service quality and food quality. Journal of Foodservice Business Research, 22(1), 81-97. doi:10.1080/15378020.2018.1546076
Xiuting, Z. (2019). A Study of Consumers' Values on the Use of Food Delivery Platforms - Using Convenience as a Mediating Variable. Chenggong University, Available from Airiti AiritiLibrary database. (2019年)
Yeo, L. (2021). Which company is winning the restaurant food delivery war? Second Measure.
Yupin., X. (2019). Food Delivery App Earns 22.5 Billion "Lazy Fortune". Retrieved from https://www.etf168.com.tw/etf168/Article/Article?articleID=775
Zhao, M., & Stank, T. (2003). Interactions between operational and relational capabilities in fast food service delivery. Transportation research. Part E, Logistics and transportation review, 39(2), 161-173.
Zhao, X., & Ma, S. (2014). Application of Man–Machine–Environment System Engineering in Coal Mine Accident. Paper presented at the International Conference on Man-Machine-Environment System Engineering.
Zhongping, Z. (2018). Research on the Service Design of Wedding Photography Studio - Taking Anshe Wedding Dress as an Example. National. Chengchi University, Available from Airiti AiritiLibrary database. (2018)