Mulit Objective Ant Colony Optimization for Time-Cost- Risk Trade for Consstruction Projects (original) (raw)
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MULTI-OBJECTIVE OPTIMIZATION OF TIME-COST-RISK USING ANT COLONY OPTIMIZATION
Time, cost and risk of project delivery are among the crucial aspects of each project with both client and contractor striving to optimize the project duration and cost concurrently. Studies have been conducted to model the time-cost relationships, ranging from heuristic methods and mathematical approaches to genetic algorithms. Emergence of new contracts that place an increasing pressure on maximizing the quality of projects while minimizing its time and cost, require the development of innovative models considering risk in addition to time and cost. In this research, a meta-heuristic multi-colony ant algorithm is developed for optimization of three objectives time-cost-risk as a trade-off problem. An attempt is made to develop a Multi Objective Optimization Model (MOOM) capable of minimizing time and cost of projects (inclusive of risk) as multi-objective optimization of time-cost-risk. The model this developed is then compared with similar model and the efficiency of this model is ascertained. An example is analyzed to illustrate the capabilities of the present method in generating optimal/near optimal solutions. The result thus obtained from the algorithm developed is compared with solutions of the problem obtained from MAWA (modified adaptive weight approach) approach adopted by and MOACO (multi objective ant colony optimization) adopted by and it is found that the model developed gives efficient results when compared to MAWA and comparable results when compared to MOACO.
Time-cost trade-off analysis in project management: An ant system approach
IEEE Transactions on Engineering Management (ISI-indexed)
Time-cost trade-off problem (TCTP) is an important issue of industrial projects scheduling in planning phase. This paper introduces and develops a new multi-mode approach for the discrete time-cost trade-off problem (DTCTP) in PERT networks of project management, where activities are subjected to multi-mode discrete cost functions with normal distribution of activity durations. The main objective of proposed model is to optimally maximize the project completion probability in a predefined deadline using four potential extreme strategies. The first application of ant colony optimization (ACO) algorithm for the stochastic DTCTP has been introduced to solve the proposed DTCTP as a non-linear zero-one mathematical problem. In order to show the process of proposed approach, an illustrative example is discussed and an overall efficiency measure is developed to determine the appropriate levels of ACO parameters that lead to the accurate results such that the computational time is minimized. The results obtained from the comparative-computational study show that the proposed algorithm is an effective approach for the presented stochastic DTCTP.
Proceedings of the Creative Construction Conference 2019, 2019
The performance of construction projects has traditionally been measured based on cost, time, and quality. Recently, the environment impact has been introduced as a fourth criterion for the assessment of project performance. Significant research advancements have been made in the area of optimizing resource utilization to minimize the total cost, duration, and quality for construction projects. A number of models have been developed using a variety of methods, including heuristics methods, mathematical programming, genetic algorithms, ant colony, and particle swarm optimization. However, there has been little or no reported research focusing on studying and optimizing the collective impact of resource utilization decisions on construction cost, duration, and environmental impact. The objective of this paper is to present the development of a multi-objective optimization model for optimizing resource utilization and scheduling of projects. The model provides construction planners and decision makers with new and important capabilities, including: (1) evaluating the combined impact of multiple resource utilization decisions on construction cost, duration, and environmental impact and (2) generating and visualizing optimal/near-optimal resource utilization and scheduling plans that provide optimal trade-offs between the project cost, duration, and environmental impact. The model is developed in two stages: (1) model formulation to identify the decision variables and optimization objectives for the construction optimization problem and (2) model implementation to perform the optimization computations using three modules that compute project duration, total cost, and environmental impact, respectively.
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...
Engineering Applications of Artificial Intelligence
This article proposes a multi-criteria decision support tool fully integrated within system engineering and project management processes that allows decision makers to select an optimal scenario of a project. A model based on an oriented graph includes all the alternative choices of a new system's conception and realization. These choices take into account the risks inherent to perform project tasks in terms of cost and duration. The model of the graph is constructed by considering all the collaborative decisions of the different actors involved in the project. This decision support tool is based on an Ant Colony Algorithm (ACO) for its ability to provide optimal solutions in a reasonable amount of time. The model developed is a multi-objective new ant colony algorithm based on an innovative learning mechanism (named MONACO) that allows ants to learn from their previous choices in order to influence the future ones. The objectives to be minimized are the total cost of the project, its global duration and the risk associated with these criteria. The risk is modeled as an uncertainty related to the increase of the nominal values of cost and duration. The optimization tool is a part of an integrated and more global process, based on industrial standards (the System Engineering process and the Project Management one) that are widely known and used in companies.
Using Ant Colony Optimization algorithm for solving project management problems
Expert Systems with Applications, 2009
Network analysis provides an effective practical system for planning and controlling large projects in construction and many other fields. Ant Colony System is a recent approach used for solving path minimization problems. This paper presents the use of Ant Colony Optimization (ACO) system for solving and calculating both deterministic and probabilistic CPM/PERT networks. The proposed method is investigated for a selected case study in construction management. The results demonstrate that -compared to conventional methods -ACO can produce good optimal and suboptimal solutions.
International Journal of Computer Applications in Technology, 2019
Identifying and minimising the risks associated with time, and cost factors in construction projects are the main challenges for all parties involved. The objective of project management is to complete the scope of work on time, within budget and deliver a quality product in a safe fashion to maximise overall project success. This research presents a new hybrid multiple objective evolutionary algorithm based on hybridisation of Artificial Bee Colony (ABC) and differential evolution to facilitate time-cost-risk trade-off problems (MOABCDE-TCR). The proposed algorithm integrates core operations from Differential Evolution (DE) into the original ABC in order to enhance the exploration and exploitation capacity of the optimisation process. A numerical construction project case study demonstrates the ability of MOABCDE-generated non-dominated solutions to optimise TCR problem. Comparisons between the MOABCDE and currently widely used multiple objective algorithms verify the efficiency and effectiveness of the developed algorithm.
2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 2016
This paper presents a multi-objective Ant Colony Optimization (MOACO) algorithm based on a learning mechanism (named MOACO-L) for the optimization of project scenario selection under uncertainty in a system engineering (SE) process. The objectives to minimize are the total cost of the project, its total duration and the global risk. Risk is considered as an uncertainty about task costs and task durations in the project graph. The learning mechanism aims to improve the MOACO algorithm for the selection of optimal project scenarios in a SE project by considering the uncertainties on the project objectives. The MOACO-L algorithm is then developed by taking into account ants' past experiences. The learning mechanism allows a better exploration of the search space and an improvement of the MOACO algorithm performance. To validate our approach, some experimental results are presented.
Revista de la construcción
Completion of the activities within optimal time and cost plays a significant role in construction projects. Recently, project managers have to decrease the total durations and costs of the projects more than before due to the competitive environment. Mostly, decision makers usually seek different alternatives which reduce time and cost. As well as being one of the most major topics of construction management, this problem called time-cost trade-off (TCTO) which is extremely difficult to solve with traditional mathematical methods. In recent years, metaheuristic algorithms are outstanding methods in this field due to their flexible and adaptable structure. This paper presents a new algorithm called F-PSO which consists of hybridizing Firefly Algorithm (FA) with Particle Swarm Optimization (PSO). In this method, the problem is modelled with various execution modes to select the optimal one for each activity. The applicability and validity of the proposed method is confirmed by performing 18-activity project as a benchmark problem. Comparison of numerical results with different metaheuristic algorithms demonstrates the effectiveness and efficiency of F-PSO with regard to optimality of time and cost outcomes.
Genetic Algorithm Based Optimization Model for Time-Cost Trade-Off for Construction Project
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023
The main cause of gradual development of project management is the necessity of controlling and optimizing the construction project's objectives. In planning phase of construction project management, some objectives of proposed project are required to be set as per stakeholder's perspective. Time and cost are of paramount importance objectives of construction project, which vary due to variation in the resource utilization amount. High-cost resources and advance technologies reduce the project time but make the project cost higher. While low-cost resources and traditional technologies give lower project cost but increase the project time. Basically, a construction project is said to be successful if it is completed in minimum possible time and cost. Therefore, the two fundamental goals of any building project are to do it as quickly and inexpensively as possible. The development of time-cost trade-off models has received a lot of focus. However, in addition to a wide range of approaches for time-cost trade-off (TCT) models, this work offers a TCT model based on a genetic algorithm. This model was created in a way that makes it easier to find the best approaches to complete the project on time and for the lowest possible cost. The applicability of proposed TCT model is demonstrated through solving two practically existing construction project. Outcomes of proposed model were found satisfactory based on statistical analysis.