在台灣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(如全聯福利社、家樂福、大潤發…等)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重大的財物損失。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，頻率非常之高，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是導致被歹徒輕易的跟蹤、觀察，進而掌握行駛的動向的主要因素。目前實務排程方式是經由人工排程的方式規劃出幾條可選方案進行指派，此作法缺乏系統分析，因此規劃結果經常造成資源浪費。此外，人工排程的方式並未考慮到運鈔車護運路線缺乏變化性這一點。緣此，本研究構建一運鈔車護運作業排程模式，以保全公司營運成本最小化為目標，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之決策者規劃每日護運路線時考慮到先前所行駛過的護運路線與抵達時間，盡可能規劃出與先前護運路線不相似之護運路線，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此模式，以定式車輛在時空中的流動情況，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。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 companies in Taiwan are mainly in charge of escorting (and delivering) the cash from branches to each major bank and ATM, and also from the companies (i.e. superstore, mall…etc.), which had contract with the security companies, to some specific banks. When the guards from the security company is in charge of the escorting mission of cash-transportation truck, they often faces the risk of robbing, which always causes great property of damage to the clients. In the past 10 years, the robbing incidents in Taiwan have reached up to 35 cases, with an average of 3.5 cars per year being robbed. From the recent cases, we have discovered that, the lack of variability of the route and arriving time of cash-transportation truck is the main factor causing the robberies. Bandit can easily trace, observe, and master the driving tendencies of each truck. Present practical scheduling model is used for artificial scheduling to plan several modes of feasible plans and designs. This mode lacks the systematic analysis which often causes the waste of resources for the resulted scheduling. Besides, the artificial scheduling also lacks the consideration of route variability for the trucks. Therefore, this research constructs a scheduled model for cash-transportation truck, which accounts for the variability of the routes and minimizes the operating costs of security companies as much as possible. Therefore, the research designs parameters of dissimilar space-time, which hoped to help the decision-makers plan the daily route and arriving time as different as possible, and to increase the variability and security of the routes and arriving time for cash-transport truck.
We employ network flow technique to develop the model, which is characterized as NP-hard to formulate the vehicles in the dimension of time and space. We use C++ and CPLEX to solve the problem, but it is difficult to solve in a reasonable time when problem size is huge. Therefore, we develop a heuristic for solving problem effectively. Numerical tests based on real operating data from a security company and reasonable example to evaluate the proposed solution algorithm. The preliminary results could be useful for security companies.

1.乃啟育，「策略聯盟環境下城際客運排程規劃模式之研究」，碩士論文，國立中央大學土木工程系 (2006)。
2.王至誠，「台灣地區保全業經營管理的特性與問題之研究」，碩士論文，國立中山大學高階經營碩士班 (2000)。
3.申生元，「時窗限制車輛途程問題」，博士論文，國立交通大學工業工程與管理學系 (1998)。
4.余秀梅，「多元商品模式應用在動態貨櫃調度問題之研究」，碩士論文，國立成功大學交通管理科學研究所(1994)。
5.江文毅，「運鈔車護運路線決策支援系統建立之研究」，碩士論文，華梵大學工業管理學系 (2001)。
6.吳權哲，「都會區計程車共乘配對模式暨求解演算法之研究」，碩士論文，國立中央大學土木工程系 (2007)。
7.辛孟鑫，「撥召運輸系統路線規劃問題之研究－以台北市復康巴士為例」，碩士論文，國立成功大學交通管理科學研究所 (2005)。
8.林士鈞，「定期貨櫃運輸船舶排程暨船期表建立之研究」，碩士論文，國立中央大學土木工程系 (2006)。
9.邱明琦、陳春益、林佐鼎，「海運貨櫃排程模式之研究」，運輸計劃季刊，第三十一卷，第三期，第495-522頁 (2002)。
10.許志成，「定期貨櫃船舶排程計畫研究」，碩士論文，國立中央大學土木工程系 (1998)。
11.陳靜惠，「保全業維護治安之功能與角色研究-以高雄市為例」，碩士論文，國立中正大學犯罪防治所 (2006)。
12.陳妙珍、顏上堯、張珮璇，「航空公司資產與負債管理模式之建立」，第四屆海峽兩岸會計與管理學術研討會論文集，武漢 (2000)。
13.董又榮，「市場競爭下城際客運排程規劃模式暨求解演算法之研究」，碩士論文，國立中央大學土木工程系 (2005)。
14.賴峻偉，「隨機旅行時間下快遞運務員作業排程規劃及即時調整之研究」，碩士論文，國立中央大學土木工程系 (2009)。
15.盧華安，「因應班機延遲之最佳化即時機門指派」，運輸計劃季刊，第三十卷，第四期，第849-869頁 (2001)。
16.顏上堯、齊志仁、湯慶輝，「隨機需求下多目標長途客運排程模式之研究」，運輸計劃季刊，第三十四卷，第一期，第 93-117 頁 (2005)。
17.顏上堯、翁綵穗，「季節轉換間緩衝期飛航排程之研究」，運輸計劃季刊，第三十卷，第四期，第 891-921 頁 (2001)。
18.顏上堯、曾志煌，「單機種機隊排程與班次整合之研究」，運輸計劃季刊，第二十八卷，第四期，第 635-658 頁 (1999)。
19.蘇意婷，「國籍航空公司貨機飛航排程暨班表建立之研究」，碩士論文，長榮大學航運管理研究所 (2006)。
20.Agin, N. and Cullen, D., An Algorithm for Transportation Routing and Vehicle Loading, in Geisler, M. (Ed.), Logistics, pp.1-20, North Holland, Amsterdam (1975).
21.Barnhart, C., Johnson, E. L., Nemhauser, G. L., Savelsbergh, M. W. P. and Vance, P. H., “Branch-and-price: column generation for solving huge integer programs,” Operations Research, Vol 46, pp. 316-329 (1998).
22.Benders, J. F., “Partitioning procedures for solving mixed-variables programming problems,” Numerische Mathematik, Vol. 4, pp. 238-252 (1962).
23.Bent, R. W. and Hentenryck P. V., “Scenario-based planning for partially dynamic vehicle routing with stochastic customers,” Operations Research, Vol.52, pp.977-987 (2003).
24.Chih, K. C. K., “A real time dynamic optimal freight car management simulation model of multiple railroad, mulitcommodity temporal spatial flow problem,” Ph.D. Dissertation, Princeton University, Princeton, NJ (1986).
25.Chen, C. Y. and Kornhauser, A. L., “Decomposition of convex mulitcommodity network flow problem,” Report SOR-90-19, Dept. of Civil Engineering and Operations Research, Princeton University, Princeton, NJ (1990).
26.Desaulniers, G., Desrosiers, J., Dumas, Y., Solomon, M. M. and Soumis, F., “Daily aircraft routing and scheduling,” Management Science, Vol. 43, pp. 841-855 (1997).
27.Diana, M. and Dessouky, M. M., “A new regret insertion heuristic for solving large-scale dial-a-ride problems with time windows,” Transportation Research Part B, Vol.38, pp.539–557 (2004).
28.Diana, M., Dessouky, M. M., and Xia, N., “A model for the fleet sizing of demand responsive transportation services with time windows,” Transportation Research Part B, Vol. 40, Issue 8, pp.651-666 (2006).
29.Fisher, M. L., “The Lagrangian relaxation method for solving integer programming problem,” Management Science, Vol. 27, pp. 1-18 (1981).
30.Garey, M. R. and Johnson, D.S. (1979). “Computers and intractability: a guide to the theory of NP-completeness,” San Francisco, CA: Freeman.
31.Horn, M.E.T., “Fleet scheduling and dispatching for demand-responsive passenger services,” Transportation Research Part C, Vol. 10, pp. 35-63(2002).
32.Ibaraki, T., Kubo, M., Masuda, T., Uno, T. and Yagiura, M., “Effective Local Search Algorithms for the Vehicle Routing Problem with General Time Windows,” working paper, Department of Applied Mathematics and Physics, Kyoto University, Japan (2001).
33.Kennington, J. L. and Shalby, M., “An effective subgradient procedure for minimum cost multicommodity flow problem,” Management Science, Vol. 23, pp.994-1004 (1977).
34.Levin, A., “Scheduling and fleet routing models for transportation systems,” Transportation Science, Vol. 5, pp. 232-255 (1971).
35.Levin, A., “Some fleet routing and scheduling problems for air transportation systems,” Flight Transportation Laboratory Report R68-5, Massachusetts Institute of Technology, MA (1969).
36.Lee, B. C., “Routing problem with service choices, flight transportation laboratory,” Report R86-4, Massachusetts Institute of Technology, MA (1986).
37.Lamatsch, A., “An approach to vehicle scheduling with depot capacity constraints,” in Desrochers, M. and Rousseau, J. M.（eds.）, Computer Aided Transit Scheduling, Lecture Notes in Economics and Mathematical System 386, Springer Verlag, Berlin, Heidelberg, pp. 181-195 (1992).
38.Mesquita, M. and Paixao, J., “Multiple depot vehicle scheduling problem: a new heuristic based on quasi-assignment algorithm,” in Desrochers, M. and Rousseau, J. M.(eds.), Computer Aided Transit Scheduling, Lecture Notes in Economics and Mathematical System 386, Springer Verlag, Berlin, Heidelberg, pp. 181-195 (1992).
39.Powell, W. B. and Ioannis, A. K., “Shipment routing algorithms with tree constraints,” Transportation Science, Vol. 26, pp. 230-245 (1992).
40.Simpson, R.W., “A Review of Scheduling and Routing Model for Airline Scheduling,” IX AGIFORS Symposium, Broadway, England (1969).
41.Shan, Y.S., “A dynamic mulitcommodity network flow model for real time optimal real freight car management,” Ph.D. Dissertation, Princeton University, Princeton, NJ (1985).
42.Teodorovic, D. and Guberinic, S., “Optimal dispatching strategy on an airline network after a schedule perturbation,” European Journal of Operational Research, Vol. 15, pp. 178-182 (1984).
43.Teodorovic, D., Airline Operations Research, Gordon and Breach Science Publishers, New York (1988).
44.Yan, S. and Young, H. F., “A Decision Support Framework for Multi-Fleet Routing and Multi-Stop Flight Scheduling,” Transportation Research, Vol. 30A, pp. 379-398 (1996).
45.Yan, S. and Chen, H. L., “A Scheduling Model and a Solution Algorithm for Inter-city Bus Carriers,” Transportation Research, Vol. 36A, pp. 805-825 (2002).
46.Yan, S. and Tseng, C.H., “A passenger demand based model for airline flight scheduling and fleet routing,” Computers and Operations Research, Vol. 29, pp. 1559-1581 (2002).
47.Yan, S., Chi, C. J., and Tang, C. H., “Inter-city bus routing and timetable setting under stochastic demands,” Transportation Research Part A, Vol. 40, pp. 572-586 (2006b).
48.Yan, S. and Shih, Y. L., “A Time-Space Network Model for Work Team Scheduling after a Major Disaster,” Journal of the Chinese Institute of Engineers, Vol. 30, No. 1, pp. 63-75. (2006).
49.Yan, S. and Shih, Y.L., “A time-space network model for work team scheduling after a major disaster”, Journal of the Chinese Institute of Engineers, Vol. 30, No. 1, pp. 63-75 (2007).
50.Yan, S. and Tang, C. H., “A heuristic approach for Airport Gate Assignments for Stochastic Flight Delays,” European Journal of Operational Research, Vol. 180, Iss. 2, pp. 547-567 (2007).
51.Yan, S., Tang, C.H., and Fu, T.C., “An airline scheduling model and solution algorithms under stochastic demands,” European Journal of Operational Research, Vol. 190, pp. 22-39 (2008a).
52.Yan, S., Lai, W., and Chen, M., “Production scheduling and truck dispatching of ready mixed concrete,” Transportation Research, Part E, Vol. 44, Issue 1, pp. 164-179 (2008b).