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Airports are booming in China, to enlarge their capacities and stimulate economic development. Large-span spatial steel structures are commonly used in the terminal buildings of airport projects. Their advantages include prefabrication, strength, usability, adaptability and aesthetic quality. To manage large-span spatial steel structure projects, building information modeling (BIM) is recommended. Although there are plenty of studies on BIM application in steel structure projects, it is still rare to apply BIM to optimize the schedule and cost of steel structures, especially for airport projects.

This paper aims to develop a framework in which BIM and a time-cost optimization model are integrated to optimize construction costs and the duration of large-span spatial steel structure projects. A real case study was conducted to verify the feasibility of the BIM-based time-cost optimization model in an airport terminal building, which was built with a large-span spatial steel structure.

The results preliminarily support the reliability of the proposed BIM-based time-cost optimization model. The BIM-based time-cost optimization model will benefit construction planning for professionals and enrich relevant research on the application of BIM in large-span spatial steel structure projects.

The steel structure is difficult to control budgets and progress. This paper is expected to be adopted for optimizing the time and cost plans for projects involving steel structures in airport terminal buildings.

Achieving project objectives in constructionprojects such as time, cost and quality is a challenging task. Minimizing project cost often results in additional project duration and might jeopardize quality, and minimizing project duration often results in additional cost and might jeopardize quality. Also, increasing construction quality often results in additional cost and time. The purpose of this paper is to identify and analyze trade-offs among the project objectives of time, cost and quality.

The optimization model adopted a quantitative research method and is developed in two main steps formulation step that focuses on identifying model decision variables and formulating objective functions, and implementation step that executes the model computations using multi-objective optimization of Non-Dominated Sorting Genetic Algorithms to identify the aforementioned trade-offs, and codes the model using python. The model performance is verified and tested using a case study of construction project consisting of 20 activities.

The model was able to show practical and needed value for construction managers by identifying various trade-off solutions between the project objectives of time, cost and quality. For example, the model was able to identify the shortest project duration at 84 days while keeping cost under $440,000 and quality higher than 85 percent. However, with an additional budget of $20,000 (4.5 percent increase), the quality can be increased to 0.935 (8.5 percent improvement).

The present research work is limited to project objectives of time, cost and quality. Future expansion of the model will focus on additional project objectives such as safety and sustainability. Furthermore, new optimization models can be developed for construction projects with repetitive nature such as roads, tunnels and high rise buildings.

The present model advances existing research in planning construction projects efficiently and achieving important project objectives. On the practical side, the optimization model will support the construction industry by allowing construction managers to identify the highest quality to deliver a construction project within specified budget and duration, lowest cost for specified duration and quality or shortest duration for specified budget and quality.

The present model introduces new and innovative method of increasing working hours per day and number of working days per shift while analyzing labor working efficiency and overtime rate to identify optimal trade-offs among important project objectives of time, cost and quality.

The purpose of this paper is to develop a framework to optimize time, cost and quality in a multi-mode resource-constrained project scheduling environment.

A case study approach identified the activity execution modes in building construction projects in India to support multi-mode resource-constrained project scheduling. The data required to compute time, cost and quality of each activity are compiled from real construction projects. A binary integer-programming model has been developed to perform multi-objective optimization and identify Pareto optimal solutions. The RR-PARETO3 algorithm was used to identify the best compromise trade-off solutions. The effectiveness of the proposed framework is demonstrated through sample case study projects.

Results show that good compromise solutions are obtained through multi-objective optimization of time, cost and quality.

Case study data sets were collected only from eight building construction projects in India.

It is feasible to adopt multi-objective optimization in practical construction projects using time, cost and quality as the objectives; Pareto surfaces help to quantify relationships among time, cost and quality. It is shown that cost can be reduced by increasing the duration, and quality can be improved only by increasing the cost.

The use of different activity execution modes compiled from multiple projects in optimization is illustrated, and good compromise solutions for the multi-mode resource-constrained project scheduling problems using multi-objective optimization are identified.

The purpose of this paper is to propose a new framework for time–cost trade-off. The new framework provides the optimum time–cost value taking into account the float loss impact.

The stochastic framework uses Monte Carlo Simulation to calculate the effect of float loss on risk. This is later translated into an added cost to the trade-off problem. Five examples, from literature, are solved using the proposed framework to test the applicability of the developed framework.

The results confirmed the research hypothesis that the new optimum solution will be at a higher duration and cost but at a lower risk compared to traditional methods. The probabilities of finishing the project on time using the developed framework in all five cases were better than those using the classical deterministic optimization technique.

The objective of time–cost trade-off is to determine the optimum project duration corresponding to the minimum total cost. Time–cost trade-off techniques result in reducing the available float for noncritical activities and thus increasing the schedule risks. Existing deterministic optimization technique does not consider the impact of the float loss within the noncritical activities when the project duration is being crashed. The new framework allows project managers to exercise new trade-offs between time, cost and risk which will ultimately improve the chances of achieving project objectives.

Developing an optimized project schedule that considers all decision criteria represents a challenge for project managers. The purpose of this paper is to provide a multi-objectives overall optimization model for project scheduling considering time, cost, resources, and cash flow. This development aims to overcome the limitations of optimizing each objective at once resulting of non-overall optimized schedule.

In this paper, a multi-objectives overall optimization model for project scheduling is developed using particle swarm optimization with a new evolutionary strategy based on the compromise solution of the Pareto-front. This model optimizes the most important decisions that affect a given project including: time, cost, resources, and cash flow. The study assumes each activity has different execution methods accompanied by different time, cost, cost distribution pattern, and multiple resource utilization schemes.

Applying the developed model to schedule a real-life case study project proves that the proposed model is valid in modeling real-life construction projects and gives important results for schedulers and project managers. The proposed model is expected to help construction managers and decision makers in successfully completing the project on time and reduced budget by utilizing the available information and resources.

The paper presented a novel model that has four main characteristics: it produces an optimized schedule considering time, cost, resources, and cash flow simultaneously; it incorporates a powerful particle swarm optimization technique to search for the optimum schedule; it applies multi-objectives optimization rather than single-objective and it uses a unique Pareto-compromise solution to drive the fitness calculations of the evolutionary process.

Project managers work to ensure successful project completion within the shortest period and at the lowest cost. One of the main tasks of a project manager in the planning phase is to generate the project time–cost curve, and furthermore, to determine the most appropriate schedule for the construction process. Numerous existing time–cost tradeoff analysis models have focused on solving a simple project representation without regarding for typical activity and project characteristics. This study aims to present a novel approach called “multiple-objective social group optimization” (MOSGO) for optimizing time–cost decisions in generalized construction projects.

In this paper, a novel MOGSO to mimic the time–cost tradeoff problem in generalized construction projects is proposed. The MOSGO has slightly modified the mechanism operation from the original algorithm to be a free-parameter algorithm and to enhance the exploring and exploiting balance in an optimization algorithm. The evidential reasoning technique is used to rank the global optimal obtained non-dominated solutions to help decision makers reach a single compromise solution.

Two case studies of real construction projects were investigated and the performance of MOSGO was compared to those of widely considered multiple-objective evolutionary algorithms. The comparison results indicated that the MOSGO approach is a powerful, efficient and effective tool in finding the time–cost curve. In addition, the multi-criteria decision-making approaches were applied to identify the best schedule for project implementation.

Accordingly, the first major practical contribution of the present research is that it provides a tool for handling real-world construction projects by considering all types of construction project. The second important implication of this study derives from research finding on the hybridization multiple-objective and multi-criteria techniques to help project managers in facilitating the time–cost tradeoff (TCT) problems easily. The third implication stems from the wide-range application of the proposed model TCT.

The model can be used in early stages of the construction process to help project managers in selecting an appropriate plan for whole project lifecycle.

The proposal model can be applied to multi-objective contexts in diversified fields. Moreover, the model is also a useful reference for future research.

This paper makes contributions to extant literature by: introducing a method for making TCT models applicable to actual projects by considering general activity precedence relations; developing a novel MOSGO algorithm to solving TCT problems in multi-objective context by a single simulation; and facilitating the TCT problems to project managers by using multi-criteria decision-making approaches.

The concept of sustainability is about the relationships among people, the planet, and profit. Nowadays, it has been spread to many areas, including construction projects. Sustainability in construction projects has been so important in the present time. It is commonly pointed to the environment and construction materials. Nevertheless, it is also important in construction management which addresses sustainable project scheduling. Sustainable project scheduling will help to deliver the project under the deadlines, within the available budget, and also ensuring about the quality of the works. This study presents the trade-off among time, cost, and quality in the construction project to provide the decision-maker some alternative solutions in the planning phase to conduct a sustainable construction project. Multi-objective optimisation using Symbiotic Organism Search (SOS) algorithm is done to find the best solutions to the problem under investigation. Then, fuzzy logic approach is utilised to model the diverse environment and uncertainty in the construction project. A case study in repetitive construction project is analysed to see the capability of the model to work in time cost quality trade-off to reach into a sustainable construction project.

Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and relies on the analysis of uncertain, imprecise and incomplete information, including subjective and linguistically expressed information. Various modelling and computing techniques have been used by construction researchers and applied to practical construction problems in order to overcome these challenges, including fuzzy hybrid techniques. Fuzzy hybrid techniques combine the human-like reasoning capabilities of fuzzy logic with the capabilities of other techniques, such as optimization, machine learning, multi-criteria decision-making (MCDM) and simulation, to capitalise on their strengths and overcome their limitations. Based on a review of construction literature, this chapter identifies the most common types of fuzzy hybrid techniques applied to construction problems and reviews selected papers in each category of fuzzy hybrid technique to illustrate their capabilities for addressing construction challenges. Finally, this chapter discusses areas for future development of fuzzy hybrid techniques that will increase their capabilities for solving construction-related problems. The contributions of this chapter are threefold: (1) the limitations of some standard techniques for solving construction problems are discussed, as are the ways that fuzzy methods have been hybridized with these techniques in order to address their limitations; (2) a review of existing applications of fuzzy hybrid techniques in construction is provided in order to illustrate the capabilities of these techniques for solving a variety of construction problems and (3) potential improvements in each category of fuzzy hybrid technique in construction are provided, as areas for future research.

As often in project scheduling, when the project duration is shortened to reduce total cost, the total float is lost resulting in more critical or nearly critical activities. This, in turn, results in reducing the probability of completing the project on time and increases the risk of schedule delays. The objective of project management is to complete the scope of work on time, within budget in a safe fashion of risk to maximize overall project success. The purpose of this paper is to present an effective algorithm, named as adaptive multiple objective differential evolution (DE) for project scheduling with time, cost and risk trade-off (AMODE-TCR).

In this paper, a multi-objective optimization model for project scheduling is developed using DE algorithm. The AMODE modifies a population-based search procedure by using adaptive mutation strategy to prevent the optimization process from becoming a purely random or a purely greedy search. An elite archiving scheme is adopted to store elite solutions and by aptly using members of the archive to direct further search.

A numerical construction project case study demonstrates the ability of AMODE in generating non-dominated solutions to assist project managers to select an appropriate plan to optimize TCR problem, which is an operation that is typically difficult and time-consuming. Comparisons between the AMODE and currently widely used multiple objective algorithms verify the efficiency and effectiveness of the developed algorithm. The proposed model is expected to help project managers and decision makers in successfully completing the project on time and reduced risk by utilizing the available information and resources.

The paper presented a novel model that has three main contributions: First, this paper presents an effective and efficient adaptive multiple objective algorithms named as AMODE for producing optimized schedules considering time, cost and risk simultaneously. Second, the study introduces the effect of total float loss and resource control in order to enhance the schedule flexibility and reduce the risk of project delays. Third, the proposed model is capable of operating automatically without any human intervention.

The purpose of this paper is to present a newly developed multi-objective optimization method for the time, cost and work interruptions for repetitive scheduling while considering uncertainties associated with different input parameters.

The design of the developed method is based on integrating six modules: uncertainty and defuzzification module using fuzzy set theory, schedule calculations module using the integration of linear scheduling method (LSM) and critical chain project management (CCPM), cost calculations module that considers direct and indirect costs, delay penalty, and work interruptions cost, multi-objective optimization module using Evolver © 7.5.2 as a genetic algorithm (GA) software, module for identifying multiple critical sequences and schedule buffers, and reporting module.

For duration optimization that utilizes fuzzy inputs without interruptions or adding buffers, duration and cost generated by the developed method are found to be 90 and 99 percent of those reported in the literature, respectively. For cost optimization that utilizes fuzzy inputs without interruptions, project duration generated by the developed method is found to be 93 percent of that reported in the literature after adding buffers. The developed method accelerates the generation of optimum schedules.

Unlike methods reported in the literature, the proposed method is the first multi-objective optimization method that integrates LSM and the CCPM. This method considers uncertainties of productivity rates, quantities and availability of resources while utilizing multi-objective GA function to minimize project duration, cost and work interruptions simultaneously. Schedule buffers are assigned whether optimized schedule allows for interruptions or not. This method considers delay and work interruption penalties, and bonus payments.