Time-cost Trade-off Optimization Research Articles

Development of time-cost trade-off optimization model for Indian highway construction projects using non-dominated sorting genetic algorithm-II methodology

Development of time-cost trade-off optimization model for Indian highway construction projects using non-dominated sorting genetic algorithm-II methodology

Development of resource-constrained time-cost trade-off optimization model for ventilation system retrofitting using NSGA-III

Development of resource-constrained time-cost trade-off optimization model for ventilation system retrofitting using NSGA-III

Integrating quality in resource-constrained time-cost trade-off optimization for civil construction projects using NSGA-III technique

Integrating quality in resource-constrained time-cost trade-off optimization for civil construction projects using NSGA-III technique

Development of time–cost trade-off optimization model for construction projects with MOPSO technique

Development of time–cost trade-off optimization model for construction projects with MOPSO technique

Augmented Time–Cost Trade-Off Optimization Using Particle Swarm Optimization

Augmented Time–Cost Trade-Off Optimization Using Particle Swarm Optimization

Multi-objective time–cost trade-off optimization for the construction scheduling with Rao algorithms

Determining the time and cost of activities is one of the most important stages of construction project planning. Measures taken to shorten the time, such as the need for more or more qualified employees, equipment and machinery, increase project direct cost. The process of deciding which option to use in activities with more than one time–cost option has revealed the time–cost trade-off problem which is discrete optimization. Within the scope of this study, the non-dominant sorting (NDS) method is integrated into the Rao-1 and Rao-2 algorithms and applied to small and medium-scale problems. The numerical results show that the NDS-Rao-2 algorithm has found a better result with a significant number of function evaluations compared to algorithms such as PSO, ACO, GA and TLBO used in previous studies. In addition, the NDS-based Rao-2 algorithm not only find optimal /near results, but also suggest more than one pareto result to the decision maker for time–cost trade-off problem. For this reason, the developed multi-objective Rao-2 algorithm can be considered as a very good alternative for time–cost trade-off problems which are combinatorial problems.

Streamlined project time-cost tradeoff optimization methodology: Algorithm, automation, and application

The existence of multiple optimum solutions in project Time-Cost Tradeoff (TCT) optimization has not been seriously researched, while the demand on computing time and resources considering project network models of practical size has not been benchmarked. To a certain degree, this has accounted for (1) the lack of commercial software in the market to enable TCT optimization in project management and (2) the absence of applications of TCT optimization in practice. This research proposes a streamlined TCT optimization methodology to generate the first order derivative function of project direct cost against project duration and identify the quantity and position of minimum solution(s) of total project cost. An Excel-based program was prototyped to facilitate validation and computing performance benchmarking using (1) “textbook” examples found in the literature and (2) simulated “test-bed” projects with practical complexity. In conclusion, this research opens the window of opportunity for implementing TCT optimization in practice.

Latin hypercube sampling-based NSGA-III optimization model for multimode resource constrained time–cost–quality–safety trade-off in construction projects

Many researchers have paid significant amount of attention for the development of time-cost, time-cost-quality and time–cost–safety trade-off optimization models. However, there is still a need to integrate the quality and safety together in time–cost trade-off optimization. To fill this gap, considering the multimode activities in project, this paper presents a multimode resource-constrained time–cost–quality–safety trade-off optimization model. Besides, this study considered the limited availability of resources for each execution mode of activities. A population-based meta-heuristics approach the nondominated sorting genetic algorithm III (NSGA III) is employed to develop the model. Also, Latin hypercube sampling for population initialization, analytical hierarchy process for quality determination and fuzzy logic for safety parameters determination are used. A case study of building construction project is used to demonstrate the applicability of proposed model. Whereas, the measure of various performance metrics and comparisons with existing trade-off optimization models demonstrate the effectiveness of proposed model in simultaneous optimization of four objectives. Moreover, a value path plot is prepared for the visualization of more than three objectives and a priori approach is presented to select one solution from obtained Pareto-optimal front.

Time-Cost Trade-Off Optimization with a New Initial Population Approach

Considering the competitive environment in all industries, completion on time is crucial for the stakeholders of a project. This favorable target is achieved by finding the optimal set of time-cost alternatives and this is known as time-cost trade-off problem (TCTP) in the literature. In this study, a new initial population approach is presented to improve the quality of the optimal set of time-cost alternatives. It put a predefined number of the solutions of the single objective TCTP into the initial population of teaching learning-based algorithm, which is utilized as an optimizer for the multi-objective optimization of TCTP. Hence, it is aimed to descend the randomness on initial population and to decrease the searching effort to catch the optimal set of time-cost alternatives in the search space. The proposed methodology is tested on a series of benchmark problems and the obtained results are compared with those available in the technical literature. It can produce good solutions as effective as with other techniques applied for simultaneous optimization of TCTPs.

Time-cost optimization using harmony search algorithm in construction projects

In construction projects, the relationship between project cost and duration, which is one of the main factors of the project, are considered the parameters decision-makers. When a construction schedule is planned by the planners, additional resources are allocated to shorten the project duration considering the completion time and cost of the activities so that the most suitable construction schedule is prepared by providing a trade-off between project duration and total cost. In the case of time-cost trade-off problems, the total cost of the project is determined by summing each activities’ cost by adding indirect costs. Moreover, total project duration is determined by obtaining total project duration after CPM (Critical Path Method) calculations. Obtaining optimum duration and costs is such a problematic issue with using conventional methods and common software packages. These techniques consider deterministic project values and include CPM calculations for fitness value calculation. Moreover, current studies defined alternatives as non-variable discrete values. However, construction projects are dynamic processes, and quantities are prone to be changed related to project size. In this study, time-cost trade-off optimization is performed by using harmony search method with introducing different implementation that can be more easily used for different projects. The model is implemented in a real construction project, and the conclusions are interpreted.

A multi-objective decision making model based on TLBO for the time – cost trade-off problems

In a project schedule, it is possible to reduce the time required to complete a project by allocating extra resources for critical activities. However, accelerating a project causes additional expense. This issue is addressed by finding optimal set of time-cost alternatives and is known as the time-cost trade-off problem in the literature. The aim of this study is to identify the optimal set of time-cost alternatives using a multiobjective teaching-learning-based optimization (TLBO) algorithm integrated with the non-dominated sorting concept and is applied to successfully optimize the projects ranging from a small to medium large projects. Numerical simulations indicate that the utilized model searches and identifies optimal / near optimal trade-offs between project time and cost in construction engineering and management. Therefore, it is concluded that the developed TLBO-based multiobjective approach offers satisfactorily solutions for time–cost trade-off optimization problems.

Time-Cost Trade-off Optimization of Construction Projects using Teaching Learning Based Optimization

Accelerating the project schedule raises the total cost of the project and shall be efficient only up to a certain limit. A Time-Cost Trade-off Problem (TCTP) is utilized to detect the optimal set of time-cost alternatives to enhance the overall construction project benefit. In this study, to find a set of Pareto front solutions, a multi-objective optimization model which is based on the Teaching- Learning Based Optimization (TLBO) incorporated with the Modified Adaptive Weight Approach (MAWA), is proposed. Four examples of construction projects taken from the technical literature ranging from 7 to 63 activities are investigated to show the performance of the MAWA-TLBO. The results are compared with those obtained using previously proposed models considering the optimal or near optimal solutions. It was found that the MAWA-TLBO algorithm works effectively for the TCTP in construction engineering and management field.

Logistics Cost Optimization in Total Life Cycle of Power Grid Engineering Project Using Improved Ant Colony Algorithm

Effective control of the logistics costs of the power grid engineering cost project can minimize the total cost, and improve the economic efficiency of it. This paper regards the minimal logistics cost of the power grid engineering cost project in the total life cycle as goal, and puts forward the project logistics time-cost trade-off optimization model based on improved ant colony algorithm. This model integrates the two objectives of time and cost into a single objective in terms of adaptive weight approach, and this approach is applied to ant colony algorithm to search for solutions. The result of the case study indicates that this approach is very effective for determining practical solutions, and has strong global searching ability and efficiency. So the model is very suitable for solving the problem of power grid engineering cost project logistics cost optimization.

GA optimization model for repetitive projects with soft logic

Abstract This paper presents a Genetic Algorithm (GA) based optimization model for repetitive projects when considering soft logic. There have been several models developed for Time–cost Trade-Off optimization in repetitive projects and these models were set up to search for optimum output rate that yields the minimum total cost for each project. Previous researches are based on the assumption that work sequence cannot be changed for different work zones. In reality, work sequence between work zones are not fixed (actually, constantly adjusted) throughout the project. Instead, they are often changed to shorten the construction time and minimize the total cost. Scheduling with the non-fixed work sequences between work zones is known as the soft logic method. Considering the soft logic in repetitive projects, the proposed GA model aims to assist the project team to find the minimum overall cost subjecting to different output rates and logical sequences.

Stochastic time–cost optimization using non-dominated archiving ant colony approach

Abstract This article employs a non-dominated archiving ant colony approach to solve the stochastic time–cost trade-off optimization problem. The model searches for non-dominated solutions considering total duration and total cost of the project as two objectives. In order to expect more realistic outcomes for the time–cost trade-off problem, uncertainties in time and cost of the project should be taken into account. Fuzzy sets theory is used to answer for uncertainties in time and cost of the project. The model embeds the α-cut approach to account for accepted risk level of the project manager. Left and right dominance ranking method is used for finding non-dominated solutions. The ranking method employs decision maker's optimism using β concept. The performance of the model is tested according to performance metrics for multi-objective evolutionary algorithms proposed in the literature. The results show that the algorithm is adequately reliable. A case study is solved to show the application of the proposed model for the uncertain time–cost trade-off problem.

Nondominated Archiving Multicolony Ant Algorithm in Time–Cost Trade-Off Optimization

Time–cost trade-off analysis is addressed as an important aspect of any construction project planning and control. Nonexistence of a unique solution makes the time–cost trade-off problems very difficult to tackle. As a combinatorial optimization problem one may apply heuristics or mathematical programming techniques to solve time–cost trade-off problems. In this paper, a new multicolony ant algorithm is developed and used to solve the time–cost multiobjective optimization problem. Pareto archiving together with innovative solution exchange strategy are introduced which are highly efficient in developing the Pareto front and set of nondominated solutions in a time–cost optimization problem. An 18-activity time–cost problem is used to evaluate the performance of the proposed algorithm. Results show that the proposed algorithm outperforms the well-known weighted method to develop the nondominated solutions in a combinatorial optimization problem. The paper is more relevant to researchers who are interested i...

Particle Swarm Optimization을 이용한 공기-비용 절충관계 최적화 모델에 관한 연구

It is time-consuming and difficulty to solve the time-cost trade-off problems, as there are trade-offs between time and cost to complete the activities in construction projects and this problems do not have unique solutions. Typically, heuristic methods, mathematical models and GA models has been used to solve this problems. As heuristic methods and mathematical models are have weakness in solving the time-cost trade-off problems, GA based model has been studied widely in recent. This paper suggests the time-cost trade-off optimization algorithm using particle swarm optimization. The traditional particle swarm optimization model is modified to generate optimal tradeoffs among construction time and cost efficiently. An application example is analyzed to illustrate the use of the suggested algorithm and demonstrate its capabilities in generating optimal tradeoffs among construction time and cost. Future applications of the model are suggested in the conclusion.

Project Time-Cost Trade-Off Optimization by Maximal Flow Theory

Time and cost are related on projects. Project managers are frequently required to make time-cost trade-offs. With the complexity of large projects and the schedule impact of time-cost modifications, decisions on time-cost optimization are usually done on a hit or miss basis. This technical note presents an innovative technique that can be used to automate and optimize the time-cost trade-off process. The technique is based on maximum flow.minimal cut theory. The method is an improvement over current practice.

Using Genetic Algorithms to Solve Construction Time-Cost Trade-Off Problems

Time-cost trade-off analysis is one of the most important aspects of construction project planning and control. There are trade-offs between time and cost to complete the activities of a project; in general, the less expensive the resources used, the longer it takes to complete an activity. Using critical path method (CPM), the overall project cost can be reduced by using less expensive resources for noncritical activities without impacting the project duration. Existing methods for time-cost trade-off analysis focus on using heuristics or mathematical programming. These methods, however, are not efficient enough to solve large-scale CPM networks (hundreds of activities or more). Analogous to natural selection and genetics in reproduction, genetic algorithms (GAs) have been successfully adopted to solve many science and engineering problems and have proven to be an efficient means for searching optimal solutions in a large problem domain. This paper presents: (1) an algorithm based on the principles of GAs for construction time-cost trade-off optimization; and (2) a computer program that can execute the algorithm efficiently.