本研究旨在探討垂直型重複性工程在初期規劃階段的工項方案決策及施工排程，研究的目標有三：（1）建立數學模型進行工期及成本最佳化 （2）比較兩超啟發式演算法的差異（3）產出結果進行決策分析。藉由學習曲率模型可了解人類重複執行類似的事物，可提升對該事物的熟練度，進而縮短工作時間，本研究納入此理論，用以表示施工團隊在重複性工程中，單位生產率隨著施作單元數量增加而提升。
工程專案多採用最早開始時間排程，然而在重複性工程中，工程專案施作的工項數量繁多，且前後置關係複雜，若採用此排程方法可能使工項因等待前置工項完成而產生閒置時間，增加閒置時間成本，乃至於得到較不經濟的結果。由於重複性工程每個單元的完成時間皆相當長，而過長的半成品時間可能導致單元內已完成施作的部分遭到損壞，進而重工增加半成品時間成本。故本研究使用整數線性規劃，調整每個工項在每個單元的開始時間，求得最低的總閒置時間成本及總半成品時間成本總和的施工排程，並比較最低總成本施工排程與對應工項方案的最早、最晚開始時間排程的總成本差異。工程專案中每個工項依據人力數量、施作工法及機具效率等等有不同數量的可能方案，須準確地分配各工項應採取的方案才可使工程專案資源不浪費，並且能減少公司財務支出。本研究提出混合整數線性規劃與多目標超啟發式演算法（Integer Linear Programming – MultiObjective Metaheuristic, ILP – MOM）進行工期及成本最佳化，透過混合整數線性規劃與快速非支配分類基因演算法（NSGA II）及多目標粒子群最佳化演算法（MOPSO），將求得的柏拉圖最適解繪製工期—成本權衡圖進行決策分析，在三種不同的情境下，決策者應選擇何種工項方案以滿足最佳效益。最後比較兩種超啟發式演算法於本研究的分析結果表現，利用三種指標決定何者演算法較佳：（1）演算法的運算時間 （2）非支配解數量 （3）非支配解面積占比。經比較發現， MOPSO在三項指標中皆較NSGA II優。

The objectives of this study include (1) to develop a model for time and cost optimization (2) to compare two multi objective metaheuristic algorithm; and (3) to develop the optimal Time-Cost trade-off diagram for project decision analysis. The learning curve effect explains that the work efficiency can be improved by repeatedly performing similar work, thereby shortening the working time. The present study consider the learning curve effect as the unit productivity of a construction team in a repetitive project would increase as the number of units increases.
Scheduling activities at the earliest start time is often used in construction projects. Practical repetitive construction projects involve a large number of repeated activities and complicated precedence relationships. The earliest start time may lead to unnecessary idleness because crews have to wait before the preceding crews to finish the job. Since the completion time of each unit of repetitive construction project is quite long, the excessively long work-in-progress time may cause damage to the completed part of the unit, and then rework increases the cost. Therefore, this study uses integer linear programming to adjust the start time of each activity in each unit in order to find the lowest total cost construction schedule and compare the total cost difference between the earliest start time and latest start time schedules of the corresponding activity options.
This study proposes a framework, an integrationg of Integer Linear Programming and MultiObjective Metaheuristic (ILP – MOM), to optimize two objective functions: project duration and total cost. Two popular multiobjective optimization tools are implemented in ILP-MOM: NSGA II (Non-dominated Sorting Genetic. Algorithm II) and MOPSO (Multibjective Particle Swarm Optimization) . The optimization result is the Pareto front, plotted as a time-cost trade-off for decision analysis, which can help decision makers choose the optimal schedule. The decisiuon-making process is illustrated in various scenrior analyses.
Finally, the study compares NSGA II and MOPSO in the ILP-MOM framework using two cases. The comparision metrics are: ILP–MOM(1) the computation time of the algorithm (2) the number of non-dominated solutions and (3) the percentage of non-dominated solution area. It is found that MOPSO is suprerior to NSGA II in the case studies.

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