群眾配送 (Crowdsourced Delivery) 為結合共享經濟與最後一哩配送的創新方案，透過募集群眾方式外包配送作業，以降低物流成本，提升配送彈性。以沃爾瑪 (Walmart) 的群眾配送為例，供應商除了以自有車隊配送訂單外，亦透過來店購物的顧客協助配送，以降低配送成本和等待時間。
群眾配送中，線上訂單及到店顧客均具動態特性，集中式最佳化指派方法或傳統離散事件模擬對於動態且需考量不同個體特性之問題有其侷限。在實務上，群眾配送中的到店顧客具有個體決策行為，因此本研究採用代理人模擬 (Agent-based simulation)，每位到店之顧客可設定不同接受度而決定是否配送及欲配送之訂單，而非統一由中心派遣，屬於分散式決策。
本研究建立群眾配送代理人模型，並設計個別代理人之配送行為，包含訂單選擇基準、配送接受程度與訂單配送策略，研究結果發現相比於傳統物流服務模式，群眾配送服務能提供更低的配送成本與延遲率表現，對於整體服務績效提升顯著。未來，本研究之代理人模擬方法可進一步分析其他配送策略，提供業者或決策者參考。

In recent years, the concept of sharing economy has begun to get attention, the main idea is to share underused assets or resources with others. With the concept of sharing economy, crowdsourced delivery is an innovative solution for last-mile delivery. It recruits crowd for goods delivery service, that is, to use idle space or time of the crowd to assist delivery operations. The traditional delivery service provider can thus reduce operational costs. For example, in order to reduce delivery costs and waiting time, Wal-Mart not only has their own fleets to deliver packages but also tries to find in-store customers to assist delivery service.
Based on above concept, the purpose of our research is to use agent-based simulation for the dynamic crowdsourced delivery. Agent-based simulation is suitable for complex problems that are difficult to build the mathematical model. The simulation can set agent's (e.g. professional fleets, occasional drivers) behaviors and analyze the impact of changes in environment characteristics on service quality and operational cost. In addition, the simulation result can provide suitable operational recommendation for operators.
This study establishes an agent-based model of crowdsourced delivery and designs delivery behaviors of individual agents, including order selection, delivery service acceptance, and delivery strategies. The results show that compared with traditional delivery service, crowdsourced delivery services can provide fast, low-cost home delivery service. The sensitivity analysis and delivery strategy analysis also show that agent-based model of this study can be widely used in various delivery scenarios and provide realistic simulation results.

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