酒後代駕調度排程最佳化之研究

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Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/91670

Title:	酒後代駕調度排程最佳化之研究
Authors:	林曉青;Lin, Hsiao-Ching
Contributors:	土木工程學系
Keywords:	酒後代駕;調度排程;流量分解法;時空網路;整數多商品網路流
Date:	2023-08-11
Issue Date:	2023-10-04 14:38:42 (UTC+8)
Publisher:	國立中央大學
Abstract:	為降低酒後駕駛的發生率及致死率，政府鼓勵民眾使用酒後代駕服務，隨著代駕服務的需求量日漸成長，業者如何負擔快速增加的代駕服務需求顯得至關重要。目前實務上代駕司機員以大眾運輸、共享運具、折疊式電動載具等方式，前往代駕服務起點或從代駕服務終點離開，此營運模式恐無法應對未來龐大的代駕服務需求量。有鑑於此，本研究以代駕業者之角度，考量顧客之人身安全，以是否要求代駕司機員之性別為基準將顧客分類，並以最小化系統總成本為目標，構建一代駕調度排程規劃模式，期能協助決策者有效率地進行調度作業排程及人車指派。　　本研究利用時空網路流動技巧建構代駕調度排程規劃模式，此模式利用C++程式語言與數學規劃軟體CPLEX直接求解，並以圖形搜尋法進行流量分解。同時，為評估模式之實用性及績效，本研究以隨機生成之資料進行範例測試，並調整不同參數和任務需求規模進行敏感度及方案分析。結果顯示本模式能有效地使用資源，且運用於實務上之成效良好。;To reduce the occurrence and fatality rate of drunk driving incidents, the government encourages the use of designated driving services. As the demand for such services rapidly increases, it becomes crucial for service providers to cope with the growing demand efficiently. Currently, designated drivers travel to the service starting points or leave from the service endpoints using public transportation, shared vehicles, or foldable electric vehicles. However, this operating mode may not be able to meet the future high demand for designated driving services. In view of this, this study considers the personal safety of customers and classifies them based on whether they request drivers of a specific gender from the perspective of designated driving companies. The objective is to minimize the total system cost by constructing a designated driving scheduling model, aiming to assist decision-makers in efficiently scheduling operations and assigning drivers and vehicles. This study utilizes time-space network flow techniques to construct the designated driving scheduling model, and the model is solved directly using the C++ programming language in combination with the mathematical programming software CPLEX. To evaluate the practicality of the model, the study conducts example tests using randomly generated data and analyzes sensitivity and scenarios by adjusting different parameters and task demand scales. The results demonstrate that the model effectively utilizes resources and yields excellent outcomes when applied in practical settings.
Appears in Collections:	[Graduate Institute of Civil Engineering] Electronic Thesis & Dissertation

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