Time-cost Relationships Research Articles

Time - Cost Relationships for Superstructure Projects in Turkey

The concept of time-cost relationship in construction projects was first introduced by Bromilow. Bromilow used the data of the superstructure projects completed in Australia in the creation of the proposed time-cost model (BTC model). The aim of this study is to determine the time-cost relationship of superstructure projects in Turkey. Time and cost data of superstructure projects completed between the years of 1999-2018 was used in the study. Data was grouped primarily on the basis of the intended use of buildings (individual buildings, educational buildings, hospitals, industrial buildings and social housing), and then time-cost relationships were separately determined for each group. In addition, the effects of the parameters such as exchange rates and the number of non-working days on the time-cost relationship were investigated statistically.

The Influence of the Construction Technology in Time-Cost Relationships of Sewerage Projects

Time-cost models have been developed in various contexts to assist in forecasting the duration of different types of projects. The present research explores the influence of the construction technology on the time-cost relationship. For that purpose, the data from 21 sewerage projects using trenchless technology and from 49 sewerage projects using the traditional open-cut method was analyzed. The 70 sewerage projects analyzed were completed in Chicago, U.S., between 1994 and 2002. The influence of the construction technology in the time-cost relationship was found to be statistically significant and time-relationships were determined for sewerage projects using trenchless technology and the traditional open-cut method. Furthermore, along with the Bromilow type models an alternative approach was tested using the time normalized by the project cost as dependent variable, allowing additional insight into the time performance of the open-cut and lining technology used (cured in place).

Dual objective oil and gas field development project optimization of stochastic time cost tradeoff problems

Conducting stochastic-time-cost-tradeoff-problem (STCTP) analysis beneficially extends the scope of discrete project duration-cost analysis for oil and gas field development projects. STCTP can be particularly insightful when using a dual-objective optimization approach to locate minimum-total-project-cost solutions, and to additionally derive a Pareto frontier of non-dominated-total-project-cost solutions across a wide range of potential project durations. For STCTP project-work-item durations and costs are expressed as probability distributions and sampled with random numbers (0, 1). By controlling the fractional numbers used to sample the work-item cost distributions by formulas linked to the random numbers used to sample the work-item duration distribution, a wide range of complex time-cost relationships are readily applied. The memetic algorithm developed for constrained STCTP involves ten metaheuristics configured to focus partly on local exploitation and partly on exploration of the feasible solution space. This dual focus effectively delivers the dual objective of: 1) locating the global minimum total-project- cost solution, if it exists, or the region in the vicinity of where that solution exists; and, 2) developing a Pareto frontier. Analysis of an example project, applying eight distinct work-item time-cost relationships, demonstrates with the aid of metaheuristic profiling, that the memetic STCTP algorithm coded in Visual Basic for Applications and operated in Microsoft Excel effectively delivers on both objectives. Dynamic adjustment factors applied by some metaheuristics, derived from fat-tailed distributions adjusted by chaotic sequences, aid the efficient sampling of the feasible solution space. The metaheuristic profiles also help to fine tune the configuration of the algorithm to further enhance performance for specific work-item time-cost relationships. Cited as : Wood, D.A. Dual objective oil and gas field development project optimization of stochastic time cost tradeoff problems. Advances in Geo-Energy Research, 2018, 2(1): 14-33, doi: 10.26804/ager.2018.01.02

Gas and oil project time-cost-quality tradeoff: Integrated stochastic and fuzzy multi-objective optimization applying a memetic, nondominated, sorting algorithm

Stochastic time-cost-quality tradeoff problem (STCQTP) analysis significantly expands the scope of discrete (deterministic) project duration-cost analysis. STCQTP requires multi-objective optimization methodologies to locate optimum total-project-cost-quality solutions for facilities-construction projects with multiple parallel pathways of work items involving high degrees of duration, cost and quality uncertainties; a situation common in the gas and oil industry. Calculating Pareto frontiers of non-dominated-total-project-cost and total-project-quality solutions across the range of feasible project durations further extends the usefulness of STCQTP analysis. For stochastic analysis project-work-item durations and costs are expressed as probability distributions and sampled with random numbers (0,1). By controlling the fractional numbers used to sample the work-item cost distributions by formulas linked to the random numbers used to sample the work-item duration distribution, a wide range of complex time-cost relationships can be defined. Fuzzy analysis is applied to each stochastic case generated to integrate more subjective assessments of work-item quality achieved. A memetic algorithm, developed for constrained STCQTP involves ten metaheuristics configured to combine local exploitation and global exploration of the feasible duration-cost solution space. Fuzzy analysis of work-item quality is integrated with each stochastic scenario evaluated. The proposed algorithm effectively delivers realistic multiple objectives of: 1) global total-project-cost minima; 2) global total-project-quality minima; and, 3) Pareto frontiers of non-dominated total-project duration versus cost, duration versus quality, and/or duration versus total-project-cost-quality function test score. Analysis of an example gas-processing-plant-construction project, applying three distinct work-item duration-cost relationships, demonstrates with the aid of metaheuristic profiling, that the memetic STCQTP algorithm coded in visual basic for applications for execution via an Excel spreadsheet, requires no proprietary software to deliver its objectives. Dynamic adjustment factors applied by some metaheuristics, derived from fat-tailed distributions sampled by chaotic sequences, aid efficient searching of the feasible solution space. The metaheuristic profiles also help to fine tune the metaheuristic configurations of the algorithm applied to specific project cases.

Role of Statistics and Engineering Judgment in Developing Optimized Time-Cost Relationship Models

AbstractWhen estimating the duration of the construction stage during the design stages of construction projects, empirical models are often used as a substitute for more accurate estimates based on detailed scheduling. These models, traditionally designated as time-cost relationships (TCR), derive from regressions relating the duration of concluded construction projects with characteristics that can be easily foreseen during the planning and design stages—both quantitative (e.g., cost, gross floor area, number of floors) and qualitative (e.g., type of contract). This paper reviews the approaches that have been adopted to develop the TCR models and discusses the complementary roles that statistics and engineering judgment can play towards their optimization and enhanced accuracy. This discussion includes the statistical and practical implications of (1) the quantitative independent variables used, (2) the qualitative independent variables used, and (3) the mathematical structure used. The paper aims at de...

Optimal option selection for finishing works of high-rise building

Time-Cost Trade-Off Problem (TCTP), to determine optimal option among many options of construction work of high-rise building, is an issue that needs to be addressed to optimize construction duration with minimal cost. Despite the fact that the crashing process entailed in a network scheduling method is not suitable for the repetitive activities of a construction project, traditional time-cost trade-off focuses on the techniques for minimizing construction cost and time in a network-based scheduling method. In addition, time-cost relationships are usually presumed to be linear, not nonlinear. Since only integer durations (days) are considered in construction scheduling, linear approximation is not fully suitable to solve the TCTP. Thus, time-cost relationships need to be redefined at discrete points representing time-cost options. In addition, Tact scheduling is one of the most useful scheduling methods to reflect the repetitiveness usually demonstrated in a LOB (Line of Balance) form. However, Tact scheduling requires too much time and effort to determine Tact time, options (cost and time of activity), and the group of activities with the same option. Genetic Algorithms (GAs) are efficient in searching for optimal solutions among decision alternatives. Thus, the authors propose a time-cost trade-off method of Tact scheduling that uses GAs.

STUDY AND APPLY ACO ALGORITHM IN TIME-COST OPTIMIZATION OF CONSTRUCTION PROJECT

Time-cost optimization problem is one of the most important aspects of construction project management. In order to maximize the return, construction planners would strive to optimize the project duration and cost concurrently. Over the years, many researches have been conducted to model the time-cost relationships; the modeling techniques range from the heuristic method and mathematical approach to genetic algorithm. In this paper, an evolutionary-based optimization algorithm known as ant colony optimization (ACO) is applied to solve the multi-objective time-cost problem. By incorporating with the modified adaptive weight approach (MAWA), the proposed model will find out the most feasible solutions. The concept of the ACO-TCO model is developed by a computer program in the Visual Basic platforms. An example was analyzed to illustrate the capabilities of the proposed model and to compare against GA-based TCO model. The results indicate that ant colony system approach is able to generate better solutions without making the most of computational resources which can provide a useful means to support construction planners and managers in efficiently making better time-cost decisions.

Optimizing Construction Time and Cost Using Ant Colony Optimization Approach

Time and cost are the most important factors to be considered in every construction project. In order to maximize the return, both the client and contractor would strive to optimize the project duration and cost concurrently. Over the years, many research studies have been conducted to model the time–cost relationships, and the modeling techniques range from the heuristic methods and mathematical approaches to genetic algorithms. Despite that, previous studies often assumed the time being constant leaving the analyses based purely on a single objective—cost. Acknowledging the significance of time–cost optimization, an evolutionary-based optimization algorithm known as ant colony optimization is applied to solve the multiobjective time–cost optimization problems. In this paper, the basic mechanism of the proposed model is unveiled. Having developed a program in the Visual Basic platform, tests are conducted to compare the performance of the proposed model against other analytical methods previously used fo...

Time–Cost Relationships in Australian Building Construction Projects

Project time–cost relationships were examined by using project scope factors as predictors for 161 construction projects that were completed in various Australian States using the multiple regression technique of weighted least squares. The analysis was performed between project duration (i.e., time), project type, procurement method, tender type, gross floor area (GFA), and number of stories. An alternative model to that proposed by Bromilow more than three decades ago for forecasting time and cost performance is proposed. It is shown that GFA and the number of stories in a building are key determinants of time performance in projects. Furthermore, the results indicate that cost is a poor predictor of time performance. Limitations and practical implications of the research are also presented.

The predictive ability of Bromilow's timecost model

Bromilow's log-log time-cost (BTC) model is tested and refitted with a new set of data for Australian construction projects completed between 1991 and 1998. It is shown that, as anticipated by earlier research, different parameter estimates are needed for different project types, with smaller industrial projects taking less time to complete than the smaller educational and residential projects. This results in the development of two separate models, one for industrial projects and one for non-industrial projects. No changes in parameter estimates are needed for projects with different client sectors, contractor selection methods and contractual arrangements. Alternatives to the log-log model failed to produce any improved fit. Finally, the results are compared with previous work to indicate the extent of changes in time-cost relationships in Australian construction projects over the last 40 years. This indicates a clear improvement in construction speed over the period. Furthermore, the ‘public’ sector group in particular has exhibited a greater variation (up to 132%) over the years.

Using Machine Learning and GA to Solve Time-Cost Trade-Off Problems

Existing genetic algorithms (GA) based systems for solving time-cost trade-off problems suffer from two limitations. First, these systems require the user to manually craft the time-cost curves for formulating the objective functions. Second, these systems only deal with linear time-cost relationships. To overcome these limitations, this paper presents a computer system called MLGAS (Machine Learning and Genetic Algorithms based System), which integrates a machine learning method with GA. A quadratic template is introduced to capture the nonlinearity of time-cost relationships. The machine learning method automatically generates the quadratic time-cost curves from historical data and also measures the credibility of each quadratic time-cost curve. The quadratic curves are then used to formulate the objective function that can be solved by the GA. Several improvements are made to enhance the capacity of GA to prevent premature convergence. Comparisons of MLGAS with an experienced project manager indicate that MLGAS generates better solutions to nonlinear time-cost trade-off problems.

Activity Costing¿Key to Progress in Critical Path Analysis

Realization of the full potential of critical path techniques involves six essential aspects. These are 1) network planning, 2) time and resource planning, 3) optimum scheduling and allocation, 4) progress reporting, 5) analysis and control of progress, and 6) statistical analysis to improve time and resource planning. All of these aspects are not currently being employed and thus full benefits are not accruing to the user. The principal reason for this is the lack of data on which to base time-cost trade-off relationships. This in turn is due to the present structure of accounting and control systems which do not relate costs with the work accomplished. Several efforts employing enumerative cost models are currently in existence or under development which largely overcome the shortcomings of present accounting and control systems. The enumerative cost model collects cost by network activity and compares actual expenditure and schedule progress with that planned in order to determine program status in an accurate and unambiguous fashion. In addition, this type of model will provide the raw materials for construction of the time-cost relationships required for optimization provided that the non-comparability of activities problem can be overcome.