Abstract
As construction projects become larger and more diversified, various factors such as time, cost, quality, environment, and safety that need to be considered make it very difficult to make the final decision. This study was conducted to develop an integrated Multi-Objective Optimization (iMOO) model that provides the optimal solution set based on the concept of the Pareto front, through the following six steps: (1) problem statement; (2) definition of the optimization objectives; (3) establishment of the data structure; (4) standardization of the optimization objectives; (5) definition of the fitness function; and (6) introduction of the genetic algorithm. To evaluate the robustness and reliability of the proposed iMOO model, a case study on the construction time-cost trade-off problem was analyzed in terms of effectiveness and efficiency. The results of this study can be used: (1) to assess more than two optimization objectives, such as the initial investment cost, operation and maintenance cost, and CO2 emission trading cost; (2) to take advantage of the weights as the real meanings; (3) to evaluate the four types of fitness functions; and (4) to expand into other areas such as the indoor air quality, materials, and energy use.
Highlights
The Construction Management Association of America (CMAA) defines Construction Management (CM) as a professional service that applies effective management techniques to control various standards, such as the time, cost, quality, environment, and safety, under a series of processes from the project planning phases to operation and maintenance phase
––The integrated Multi-Objective Optimization (iMOO) model adopts two criteria for improving the performance of the models proposed in previous studies: (1) effectiveness in terms of the quality of the generated optimal solution set; and (2) efficiency in terms of computational time
This research aims to develop an iMOO model for solving a multi-objective optimization problem, which determines the optimal solution set based on the concept of the Pareto front
Summary
The Construction Management Association of America (CMAA) defines Construction Management (CM) as a professional service that applies effective management techniques to control various standards, such as the time, cost, quality, environment, and safety, under a series of processes from the project planning phases to operation and maintenance phase. A plan should be established to engage appropriate labor and equipment Such decision-making affects the time, cost, and quality, among other factors, of a project, and determines the outcome of the project as the final decision. The larger the project scales, the more varied factors for consideration are generated This makes it very difficult to make the final decision. It becomes increasingly difficult to review all possible combinations of factors for the best decisions, which leads to obstacles in obtaining reliability. To solve this problem, experts should be involved or a sophisticated decision support system should be developed (Pagnoni 1990; Adeli, Hung 1995; Adeli, Sarma 2006). A genetic algorithm (GA) was used as a search algorithm for determining the optimal solution of the fitness
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