台灣的保全公司負責銀行各分行現金護運至總行、各提款機現金之護運、有簽訂契約之公司行號現金護運至銀行時，經常面臨被搶劫的風險，每當被歹徒搶劫往往會對保全公司之客戶端造成龐大的財物損失。台灣過去十年間，運鈔車被歹徒搶劫之事件就高達 35 件之多，平均每年會有 3.5 輛運鈔車被歹徒搶劫，頻率非常高。從許多運鈔車被搶劫的案例中可以發現，運鈔車護運作業行駛的路線與抵達時間欠缺變化性，容易被歹徒輕易的跟蹤、觀察，進而掌握運鈔車行駛的動向為主要因素。實務上，運鈔車的排程方式主要是透過人工經驗判斷，規劃出幾條可選方案進行指派，此作法缺乏系統最佳化觀點，因此規劃結果經常造成資源浪費。過去運鈔車排程的研究多以平均旅行時間為依據，進行運鈔車輛的排程，此作法未考量實際旅行時間的隨機性。在實際營運時若隨機旅行時間造成擾動過大，將使原規劃的排程結果失去最佳性。因此，本研究針對隨機性旅行時間之影響，並考量運鈔車每日護運路線所需的變化性，構建一時空相似度幫助決策單位解決護運路線過於單調且缺乏變化之情形，增加運鈔車護運路線與抵達時間的變化性與安全性。
本研究利用時空網路流動技巧建立一此隨機模式，以定式車輛在時空中的流動情況。本研究進一步修改隨機模式之旅行時間為平均旅行時間，建立一確定性模式，此兩模式屬NP-hard 問題。在求解方法上，利用 C++ 程式語言配合數學規劃軟體 CPLEX 進行模式求解，但當面臨實務的大型問題時，勢將難以在有限時間內利用數學規劃軟體求得最佳解。緣此，本研究發展一啟發式演算法以有效地求解問題。此外，本研究亦發展一模擬評估方法，以評估兩模式的實際營運績效。最後為評估模式與演算法之實用績效，本研究以國內一保全公司的營運資料以及合理假設產生測試範例，進行範例測試並針對不同參數進行敏感度分析，結果顯示本模式與演算法在實務上可有效的運用，並提供保全公司作為參考。

The security carriers in Taiwan mainly deal with cash pick-up and delivery from chest to bank and ATM (Automatic Teller Machine). The cash conveyance often faces the risk of the robbery, which always causes great property of damage to the clients. In the past 10 years, the robbery occurrence in Taiwan has reached up to 35 cases, with an average of 3.5 cases per year. From these cases, we could find that the main reason of the robbery is due to the invariant conveyance routes and the arriving time at demand points. In practice, the personal experience of the decision maker is used to construct the cash pick-up and delivery vehicle routing/ scheduling, which lacks the perspective of system optimization. Therefore, the scheduling resources are always wasted. The past researches on the cash pick-up and delivery vehicle routing/ scheduling is mainly based on the average traveling time, which do not consider the stochastic traveling time. Therefore, when actual cash conveyance routing/ scheduling is affected by stochastic traveling time, the already planned vehicle routing/ scheduling will be disturbed and lose its system optimization. This research thus considers the stochastic traveling time and the similarity of time and space of the cash conveyance route to construct the cash pick-up and delivery vehicle routing/ scheduling model. This stochastic traveling time model is also expected to help the decision maker formulate the variant conveyance routes and the arriving time at demand points and reduce the risk of the robbery.
In this research, the time-space network flow technique is used to show the potential movement of the cash pick-up and delivery vehicle under stochastic traveling time and to construct the stochastic traveling time model. We also consider the average traveling time to construct the deterministic traveling time model. Mathematically, these two models are formulated as an integer multiple-commodity network flow problem, which is characterized as NP-hard. We employ the C computer language, coupled with the CPLEX mathematics programming solver, to solve the problem. Because the problem size is expected to be huge, a solution algorithm based on a problem decomposition/collapsing technique is thus developed to efficiently solve the problem. Note that we also develop a method to evaluate the performance of these two models and practical arrangements in simulated real operations. The numerical tests are performed using data from the security carrier in Taiwan. The results show that the model and the solution algorithm would be useful for formulating the variant conveyance routes.

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