強震或颱風等天然災害可能毀損路段或橋樑，導致災區居民陷入孤立無援的困境，亟需外界給予支援與救助，因此在災害發生後，「如何緊急搶修路段，以使與外界隔絕的災區能與外界儘早連通」應為搶修單位最重要的決策考量，其目標著重在儘快恢復災區與外界的連通性。過往之相關災後路網修復文獻大都直接假設欲修復之災區路段為已知，且大都以修復者的觀點尋求最早修復完成之時刻；實務上，如何自眾多毀損之路段選出應該被修復的路段至為重要，且理應自災民而非修復隊的角度來制定決策目標，亦即如何使災民等待其路網連通外界的時間越短越好，將會更貼近災民需求。而針對上述實務考量的近期文獻，雖已提出理論之整數規劃模型，但僅能處理小規模之路網，且未將各災區之災民人數列入排程考量，導致其實用性大打折扣。
本研究著重於樹狀路網的路段修復排程決策，將救災中心視為該樹狀網路的單根，此樹狀網路可被視為原路網的簡化版本；修復隊自此網路之根節點（亦即修復中心）出發，逐步依各路段與根節點的遠近來修復該樹狀路網之所有路段，以達到網路上所有節點（亦即災區）能儘早連通至根節點的目的。雖說本研究所探討的樹狀網路已為原網路之簡化版本，然而若將此樹狀網路視為由數個沒有節線交集的分枝聯集，每分枝視為一個產品，各分枝上由根節點往葉子末端方向依序之各修復路段視為該產品的加工工序，而各修復隊各為一個獨立機台的話，則本修復排程問題可被類比成欲將各產品（分枝）依其加工工序（自根或分枝節點至葉子末端由內而外）指派給多機台（修復隊）的平行機台排程問題，仍屬NP-hard等級。
本研究將各災區之災民數量列入排程考量，從平行機台排程角度提出整數規劃模型，設計三種（均勻選取式、比例選取式及鄰近搜尋式）單次性之貪婪演算法，與疊代性之基因演算法，並以大規模數值測試，期在短時間內能求得品質不錯之修復排程。

In the post-disaster management, how to repair the roads (arcs) to access villages (nodes) from a disaster recovery center (root) is very important. A wrong restoration scheduling leads to longer waiting time to access villages and may increase number of casualties. This thesis focuses on restoring arcs in a rooted tree network simplified from a general network. In particular, given a rooted tree, we schedule the restoration tasks, one arc by a team at one time, such that the makespan or the weighted total waiting time for all the refugees (weights) to become accessible from the root are minimized.
We explain why this network restoration scheduling problem corresponds to an NP-hard parallel machine job shop scheduling problem, where each branch from the root or a branch node to a leaf node corresponds to a product required to go over sequential processes (restoration for arcs in the branch from the root or a branch node to a leaf node) by machines (restoration teams). We then formulate this arc restoration scheduling problem by an integer program. To further shorten the solution time, we also develop four heuristic algorithms including three greedy algorithms (GD, GDr, GR) and a genetic algorithm (GA) to calculate a schedule of good quality in shorter time. Our intensive computational experiments indicate GR has the best performance among all four algorithms.

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英文文獻
Aksu, D.T. & Ozdamar, L., (2014), “A mathematical model for post-disaster road restoration: Enabling accessibility and evacuation,” Transportation Research Part E: Logistics and Transportation Review, 61, 56-67.
Edrisi, A. & Nadi, A., (2014), “An appropriate approach to routing disaster relief assessment teams,” 2nd International Congress on Structure , Architecture and Urban Development,16-18.
Felix, W., Guido, S., Stefan, F. & Dirk, N., (2014), ” Emergency response in natural disaster management: Allocation and scheduling of rescue units” European Journal of Operational Research,235, 697-708.
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Matisziw, T.C., Murray, A.T. & Grubesic, T.H., (2010), “Strategic network restoration,” Networks and Spatial Economics, 10(3), 345-361.
Michalewicz, Z (2013) Genetic algorithms + data structures = evolution programs Springer Science & Business Media.
Nurre, S.G., Cavdaroglu, B., Mitchell, J.E., Sharkey, T.C. & Wallace, W.A., (2012), “Restoring infrastructure systems: An integrated network design and scheduling (INDS) problem,” European Journal of Operational Research, 223(3), 794-806.
Wang, Y.C., Wang, M.J., Lin, S.C., (2015), “Selection of cutting conditions for power constrained parallel machine scheduling,” Robotics and Computer-Integrated Manufacturing,43(2017),105-110.
Wilson, F.A., Alejandro, V., Héctor, J.C., Suzanna, L., Tom, S., & Steve, C. ,(2014), “A mathematical model for supply chain network infrastructure restoration,” Industrial and Systems Engineering Research Conference,78-85.
網站資料
全國法規資料庫，(2010)，災害防救法，http://law.moj.gov.tw/LawClass/LawAll.aspx?PCode=D0120014