Objective-driven and Pareto Front analysis: Optimizing time, cost, and job-site movements (original) (raw)
Related papers
Extended Genetic Algorithm for Optimized BIM-Based Construction Scheduling
Wiley StatsRef: Statistics Reference Online, 2018
Construction project scheduling is one of the most important tools for project managers in the architecture, engineering, and construction (AEC) industry. Construction schedules allow project managers to track and manage the time, cost, and quality (i.e., the project management triangle) of projects. Developing project schedules is almost always troublesome, as it is heavily dependent on project planners' knowledge of work packages, on-the-job experience, planning capability, and oversight. Having a thorough understanding of the project geometries and their internal interacting stability relations plays a significant role in generating practical construction sequencing. On the other hand, the new concept of embedding all the project information into a three-dimensional (3D) representation of a project (also known as building information model or BIM) has recently drawn the attention of the construction industry. It seems timely to use this source of project data for generating better construction schedules. In this article, the authors demonstrate how to develop and extend the usage of the genetic algorithm (GA), not only to generate construction schedules, but also to optimize the outcome for different objectives (i.e., cost, time, and job-site movements). The proposed methodology initially generates structurally stable construction schedules and then optimizes these schedules based on three distinct project management objectives. The basis for the GA calculations is the embedded data available in the BIM of the project, which should be provided as an input to the algorithm. By reading the geometric information in the 3D model and more specific information about the project and resources from the user, the algorithm results in various construction schedules. The output 4D animations and schedule quality scores can further help the user to find the most suitable construction schedules for the given project.
Construction Scheduling Using Genetic Algorithm Based on Building Information Model
Expert Systems With Applications, 2014
The construction project schedule is one of the most important tools for project managers in the Architecture, Engineering, and Construction (AEC) industry that makes them able to track and manage the time, cost, and quality (a.k.a. Project Management Triangle) of projects. Developing project schedules is almost always troublesome, since it is heavily dependent on project planners’ knowledge of work packages, on-the-job-experience, planning capability and oversight. Having a thorough understanding of the project geometries and their internal interacting stability relations plays a significant role in generating practical construction sequencing. On the other hand, the new concept of embedding all the project information into a 3-dimentional representation of a project (a.k.a. Building Information Model or BIM) has recently drawn attention to the construction industry. In this paper, the authors demonstrate a novel approach of retrieving enough information from the BIM of a project and then develop construction sequencing for the installation of the project elements. For this reason a computer application is developed that can automatically derive a structurally (statically) stable construction sequence, using the concept of the Genetic Algorithm (GA). The term “structurally stable sequencing” in this article refers to the sequencing order of erection in which the structure remains statically stable locally and globally during the entire installation process. To validate the proposed methodology, the authors designed 21 different experiments and used the proposed method for generating stable construction schedules, which all were successfully accomplished. Therefore, this methodology proposes a novel approach of construction project application of the GA, as an Expert System tool.
Multiobjective Construction Schedule Optimization Using Modified Niched Pareto Genetic Algorithm
A construction schedule must satisfy multiple project objectives that often conflict with each other. While several earlier approaches attempted to generate optimal schedules in terms of several criteria, most of their optimization processes were segmented into multiple steps. Owing to such a lack of simultaneous optimization, limited alternative solutions could be searched and some trade-offs between goals could not be identified. This paper presents an optimization approach that enables a simultaneous search for an optimal construction schedule in terms of three objectives: minimization of construction duration, cost, and resource fluctuation. A multiobjective optimization (MOO) approach was adopted to generate scheduling solutions considering all those objectives. To enable a simultaneous optimization, we propose a new data structure that can compute the performances of solutions in terms of all the objectives at the same time. A Niched Pareto Genetic Algorithm (NPGA) is modified to facilitate the optimization procedure. Then the proposed optimization approach is implemented in an existing case study. The result indicates that the proposed approach has the capability to explore and generate a greater range of solutions compared to existing models. Trade-offs between all three objectives are identified, limitations and further research needs are discussed.
Smart optimization for mega construction projects using artificial intelligence
During practicing the planning process, scheduling and controlling mega construction projects, there are varieties of procedures and methods that should be taken into consideration during project life cycle. Accordingly, it is important to consider the different modes that may be selected for an activity in the scheduling, for controlling mega construction projects. Critical Path Method ''CPM'' is useful for scheduling, controlling and improving mega construction projects; hence this paper presents the development of a model which incorporates the basic concepts of Critical Path Method ''CPM'' with a multi-objective Genetic Algorithm ''GA'' simultaneously. The main objective of this model is to suggest a practical support for compound horizontally and vertically mega construction planners who need to optimize resource utilization in order to minimize project duration and its cost with maximizing its quality simultaneously. Proposed software is named Smart Critical Path Method System, ''SCPMS'' which uses features of Critical Path Method ''CPM'' and multi-objective Genetic Algorithms ''GAs''. The main inputs and outputs of the proposed software are demonstrated and outlined; also the main subroutines and the inference wizards are detailed. The application of this research is focused on planning and scheduling mega construction projects that hold a good promise to: (1) Increase resource use efficiency; (2) Reduce construction total time; (3) Minimize construction total cost; and (4) Measure and improve construction total quality. In addition, the verification and validation of the proposed software are tested using a real case study. ª 2014 Production and hosting by Elsevier B.V. on behalf
Journal of Civil Engineering and Management, 2015
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 mai...
Revista de la Construcción, 2019
Considering the construction industry holds ten percent on average in the gross national product over the world, the importance of efficient use of resources emerges. To alleviate the possibility of the risk factors and various uncertainties' negative impact on the project, the usage of the scheduling tools should be supported for planning as well as risk management. In today's construction perspective, the quality is not a primary objective; construction projects have to be completed within the cost and duration limits. During the construction progress, the inserting of extra activities affects to construction delays. Project success; from the planning stage to the completion of the building, it is possible to plan the resources, use them efficiently, and realize the determined time and cost objectives. In this study, a model is developed by using a fuzzy logic approach and genetic algorithm in order to provide time-cost optimization in construction projects under uncertainties. Firstly, fuzzy sets are used to take into account the effects of time and cost uncertainties on construction works. Fuzzy sets are used to model uncertainties, and the genetic algorithm is used to acquire minimum Project cost and duration. Thus, by establishing a fuzzy time-cost optimization model, optimum time-cost results are obtained according to different risk levels determined by the decision-makers. At the final stage, Pareto fronts from different risk levels that contain both minimum costs and durations are obtained and plotted.