近年來隨著物流以及共享經濟的快速發展，設計有效且快速的最後一哩路配送成為重要的課題，在需求增加且重視時效的前提下，群眾配送成為一種新興的服務模式。本研究提出具降價機制之競標配對，亦即偶然配送員在競標配對失敗後，可於下次競標配對時降低出價，以提升競標成功機率。此外，本研究亦分析若同時將自有車隊的配送成本納入考量，將如何影響整體的系統表現。本研究採用Netlogo模擬軟體，訂單位置除按隨機分布產生外，亦採用標準案例(Solomon benchmark)進行分析。結果顯示，具降價機制之競標配對可以大幅提升配送員使用率以及訂單配送率，並同時改善總成本和總延遲率。而若將自有車隊同時納入偏好競標配對，則配送員使用率和訂單由偶然配送員配送比率微幅下降，但系統總成本可進一步下降，此外，若配對時間間隔遞增，則總成本呈現先降後升的趨勢，兩次競標配對時間間隔在10分鐘時成本最低。

In recent years, with rapid development if logistics and sharing economic, people’s demand for the last mile delivery has increased significantly. Since logistics companies cannot afford considerable demand of delivery, crowdsourcing is a method that can help companies relief the stress.
Based on the above statement in light of these facts, this study applied incorporating occasional driver's preference into crowdsourcing delivery and incorporating fleet into auction and preference matching mechanism. The former is to allow occasional driver change their bid price dynamically, and the latter is to incorporating delivery cost of fleet to the matching, so that the bidding of the order isn’t limited to the comparison between occasional drivers only.
This study uses Netlogo software for coding, and uses randomly generated cases of location of occasional drivers and orders, also uses standard instances (Solomon benchmark) as simulation data. The result shows that the first mechanism we applied not only improves occasional driver matched rate and orders matched rate, also decreases total cost and lateness rate. The second mechanism makes total cost decrease further, and in the sensitivity analysis of time horizon, the total cost presents a U-shaped trend, and the lowest point of cost can be found. So that this study not only has a dynamic agent-based model, but also has the characteristics of optimizing the total cost.

Agatz, N., Erera, A.L., Savelsbergh, M.W., & Wang, X. (2011). Dynamic ride-sharing: A simulation study in metro Atlanta. Procedia-Social and Behavioral Sciences, 17, 532-550.
Allahviranloo, M., & Baghestani, A. (2019). A dynamic crowdshipping model and daily travel behavior. Transportation Research Part E: Logistics and Transportation Review, 128, 175-190.
Archetti, C., Savelsbergh, M., & Speranza, M.G. (2016). The vehicle routing problem with occasional drivers. European Journal of Operational Research, 254(2), 472-480.
Arslan, A.M., Agatz, N., Kroon, L., & Zuidwijk, R. (2019). Crowdsourced delivery—a dynamic pickup and delivery problem with ad hoc drivers. Transportation Science, 53(1), 222-235.
Belk, R. (2014). You are what you can access: Sharing and collaborative consumption online. Journal of business research, 67(8), 1595-1600.
Buldeo Rai, H., Verlinde, S., Merckx, J., & Macharis, C. (2017). Crowd logistics: an opportunity for more sustainable urban freight transport? European Transport Research Review, 9(3), 1-13.
Chau, S.C.-K., Shen, S., & Zhou, Y. (2020). Decentralized ride-sharing and vehicle-pooling based on fair cost-sharing mechanisms. IEEE Transactions on Intelligent Transportation Systems.
Dahle, L., Andersson, H., & Christiansen, M. (2017). The vehicle routing problem with dynamic occasional drivers. Paper presented at the International conference on computational logistics.
Dayarian, I., & Savelsbergh, M. (2020). Crowdshipping and same‐day delivery: Employing in‐store customers to deliver online orders. Production and Operations Management, 29(9), 2153-2174.
Deng, D., Shahabi, C., & Zhu, L. (2015). Task matching and scheduling for multiple workers in spatial crowdsourcing. Paper presented at the Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems.
Fauadi, M.H.F.b.M., Yahaya, S.H., & Murata, T. (2013). Intelligent combinatorial auctions of decentralized task assignment for AGV with multiple loading capacity. IEEJ Transactions on electrical and electronic Engineering, 8(4), 371-379.
Gdowska, K., Viana, A., & Pedroso, J.P. (2018). Stochastic last-mile delivery with crowdshipping. Transportation research procedia, 30, 90-100.
Gujar, S., & Faltings, B. (2015). Auction based mechanisms for dynamic task assignments in expert crowdsourcing. In Agent-Mediated Electronic Commerce. Designing Trading Strategies and Mechanisms for Electronic Markets (pp. 50-65): Springer.
Howe, J. (2006). The rise of crowdsourcing. Wired magazine, 14(6), 1-4.
Kadadha, M., Otrok, H., Singh, S., Mizouni, R., & Ouali, A. (2021). Two-sided preferences task matching mechanisms for blockchain-based crowdsourcing. Journal of Network and Computer Applications, 191, 103155.
Li, B., Krushinsky, D., Reijers, H.A., & Van Woensel, T. (2014). The share-a-ride problem: People and parcels sharing taxis. European Journal of Operational Research, 238(1), 31-40.
Little, J.D., Murty, K.G., Sweeney, D.W., & Karel, C. (1963). An algorithm for the traveling salesman problem. Operations research, 11(6), 972-989.
Llorca, C., & Moeckel, R. (2021). Assesment of the potential of cargo bikes and electrification for last-mile parcel delivery by means of simulation of urban freight flows. European Transport Research Review, 13(1), 1-14.
Nourinejad, M., & Roorda, M.J. (2016). Agent based model for dynamic ridesharing. Transportation Research Part C: Emerging Technologies, 64, 117-132.
Saisubramanian, S., Basich, C., Zilberstein, S., & Goldman, C.V. (2019). Satisfying social preferences in ridesharing services. Paper presented at the 2019 IEEE Intelligent Transportation Systems Conference (ITSC).
Shen, C.-W., Hsu, C.-C., & Tseng, K.-H. (2022). An Auction-Based Multiagent Simulation for the Matching Problem in Dynamic Vehicle Routing Problem with Occasional Drivers. Journal of Advanced Transportation, 2022.
Stiglic, M., Agatz, N., Savelsbergh, M., & Gradisar, M. (2016). Making dynamic ride-sharing work: The impact of driver and rider flexibility. Transportation Research Part E: Logistics and Transportation Review, 91, 190-207.
Wang, F., Zhu, Y., Wang, F., & Liu, J. (2018). Ridesharing as a service: Exploring crowdsourced connected vehicle information for intelligent package delivery. Paper presented at the 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS).
Xu, Y., Wei, S., & Wang, Y. (2020). Privacy preserving online matching on ridesharing platforms. Neurocomputing, 406, 371-377.
Zhang, H., & Zhao, J. (2018). Mobility sharing as a preference matching problem. IEEE Transactions on Intelligent Transportation Systems, 20(7), 2584-2592.
陳博鈞. (2020). 應用代理人模型模擬動態群眾配送服務. 成功大學交通管理科學系學位論文, 1-66.
許哲誠（2021）。基於拍賣之多代理人模擬於具臨時配送員之動態車輛途程問題。﹝碩士論文。國立成功大學﹞。