「微移動」為一種低速的個人移動模式，可有效解決都會中通勤或物流需求最棘手的「最初及最後一哩路」問題，同時可達成節能滅碳的效果。近年來，無樁式共享之電動滑板車因其體積、動力方式與機動性皆優於自行車，逐漸成為大都會區中新一代流行的共享微移動服務系統。該類系統成功與否的重要關鍵，在於能否有效地執行載具運補任務，亦即將合宜數量的滿電載具適時地佈署在合宜地區。若以傳統的貨車運補方式來處理，勢必將因電動滑板車體積較小且四散各處而導致運補效率不彰。據此，本研究建議以群眾外包的「眾包」方式來處理無樁式共享電動滑板車之運補及充換電作業，藉由作業研究手法，找出最適的群眾外包運補與充換電作業配對方式，以達成群眾藉由改善系統服務水準且亦能同時賺取外快的雙贏效果。
無樁式系統的分析策略上，本研究以「虛擬站點」方式將原先的營運區域分成數區、一區以單一虛擬站點來整合並簡化該區同一時段的租還需求。我們先探討在無運補作業的情境下，如何同時考慮載具之電力消耗與各區的各期歷史租借需求，計算出各虛擬站點應佈署的「期初電動滑板車數」，以極大化被滿足之租車需求量為目標。
良好的期初車輛配置僅能保障當天前幾期的租借需求較能被滿足，然而租車需求通常依時地而變動，若無合宜的運補策略，需求尖峰時期仍常會發生缺車，因此本研究將探討週期性的「動態運補機制」，在固定系統總車數的情境下，決定期初車數配置與運補工作，改善供給不均以提升服務水準。並設定以下三種運補情境，分別建立三個數學規劃模式以實作日間車輛動態運補工作：(1)「動態車輛分區統籌運補模式」：營運公司利用現有的運補車與員工進行運補；(2)「動態車輛眾包運補模式」：將運補工作分派給具合作關係的群眾；(3)「動態車輛眾包運補之群眾招募模式」：招募新群眾以完成運補工作。最後將以前兩部分計算出的期初車輛佈署為基準，探討其「靜態隔夜運補機制」雇用並指揮有空閒的群眾，依指示沿路搜集閒置且待充換電的電動滑板車，將之帶回其家中充換電，並指揮其在隔日清晨將滿電之滑板車佈署合宜數量在合宜的地點上。本研究針對上述之靜態運補機制，將分為兩階段探討，第一階段將運補工作分派給曾有運補紀錄的群眾後，第二階段再將剩餘未完成之運補工作以招募新群眾而完成，設計並實作兩階段個別的整數規劃模式，以擬定最能滿足租借需求的靜態運補策略。

A Micromobility sharing service, such as an e-scooter sharing system, can well solve the first and last mile problem in an urban area. This thesis focused on issues related to the free-floating e-scooter sharing system. We first investigate the initial vehicle deployment problem to deploy appropriate quantities and locations of e-scooters at the beginning of the operation and then discuss dynamic and static repositioning strategies conducted by crowdsourcing. We introduce the "virtual station" that represents a virtual center to consolidate all the vehicle rentals and returns in that region so that we can simplify the operations in a free-floating system as a station-based system.
We discuss the quantity and location of the deployed e-cooters for each virtual station to maximize the satisfied rental demand. We also consider the battery consumption of e-scooters and the historical rental demand under the situation of no repositioning work.
We explore the periodic "dynamic repositioning" in the context of the fixed e-scooter fleet size to improve the service level. This includes the initial vehicle deployment and daytime repositioning to improve the imbalanced fleet distribution. We set up three repositioning scenarios and establish three mathematical models to implement the daytime vehicle dynamic repositioning.
The initial vehicle deployment calculated in the previous two parts was used as a benchmark to explore its "static overnight repositioning mechanism." We consider two stages for the static repositioning mechanism. In the first stage, after assigning the repositioning work to some experienced crowds, the remaining repositioning work will be completed by the newly recruited crowds in the second stage. And then, we design and implement a two-stage integer programming model to formulate a static repositioning strategy that best meets the rental demand.

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