The time, cost and quality are crucial, conflicting aspects to construction project management. Tradeoff optimization among project duration (time), project cost, and the project quality within the project scope is necessary to enhance overall construction project benefit. A novel optimization algorithm, Opposition-based Multiple Objective Differential Evolution (OMODE), is presented to solve the time-cost-quality tradeoff (TCQT) problem. This novel algorithm employs an opposition-based learning technique for population initialization and for generation jumping. Opposition numbers are used to improve the exploration and convergence performance of the optimization process. A numerical high-way construction project case study is analyzed to illustrate the use of the algorithm and demonstrate its capabilities in generating non-dominated solutions. Comparisons with non-dominated sorting genetic algorithm (NSGA-II), multiple objective particle swarm optimization (MOPSO), multiple objective differential evolution (MODE) and previous results verify the efficiency and effectiveness of the proposed algorithm. This study is expected to provide an alternative solving methodology for the TCQT problem and help project manager plan construction methods with optimal time-cost-quality tradeoff.

The main goal of this paper is to propose a Multiple-Criteria Decision-Making (MCDM) approach that will facilitate decision-making in the field of logistics—i.e., in the selection of the optimal equipment for performing a logistics activity. For defining the objective weights of the criteria, the correlation coefficient and the standard deviation (CCSD method) are applied. Furthermore, for determining the semi-objective weights of the considered criteria, the indifference threshold-based attribute ratio analysis method (ITARA) is used. In this way, by combining these two methods, the weights of the criteria are determined with a higher degree of reliability. For the final ranking of the alternatives, the measurement of alternatives and ranking according to the compromise solution method (MARCOS) is utilized. For demonstrating the applicability of the proposed approach, an illustrative case study pointing to the selection of the best manual stacker for a small warehouse is performed. The final results are compared with the ones obtained using the other proved MCDM methods that confirmed the reliability and stability of the proposed approach. The proposed integrated approach shows itself as a suitable technique for applying in the process of logistics equipment selection, because it defines the most influential criteria and the optimal choice with regard to all of them in a relatively easy and comprehensive way. Additionally, conceiving the determination of the criteria with the combination of objective and semi-objective methods enables defining the objective weights concerning the attitudes of the involved decision-makers, which finally leads to more reliable results.

The development of information and communication technologies has revolutionized and changed the way we do business in various areas. The field of education did not remain immune to the mentioned changes; there was a gradual integration of the educational process and the mentioned technologies. As a result, platforms for distance learning, as well as the organization of e-learning courses of various types, have been developed. The rapid development of e-learning courses has led to the problem of e-learning course selection and evaluation. The problem of the e-learning course selection can be successfully solved by using multiple-criteria decision-making (MCDM) methods. Therefore, the aim of the paper is to propose an integrated approach based on the MCDM methods and symmetry principles for e-learning course selection. The pivot pairwise relative criteria importance assessment (PIPRECIA) method is used for determining the weights of criteria, and the interval-valued triangular fuzzy additive ratio assessment (ARAS) method is used for the ranking of alternatives i.e., e-learning courses. The suitability of the proposed integrated model is demonstrated through a numerical case study.

The construction industry has played an essential role in the process of modernization and industrialization and it has also been a major factor in determining the development of the infrastructure for other economic sectors. Construction companies consider the measurement of work progress, which often wastes time and has a low resolution, to be one of the most challenging problems faced by project management. Therefore, this research aimed to propose practical solutions by applying recent technological achievements of the 4.0 industrial revolution to improve the efficiency of the quantity management process. By utilizing the advantages and features of a BIM model and 3D laser scanning, this paper proposes that adopting a BIM model and 3D laser scanning has the potential to improve the accuracy and efficiency of the quantity management process. The case study demonstrated some typical tasks to evaluate accuracy and efficiency as well as to showcase the research proposal.

Accident, injury, and fatality rates remain disproportionately high in the construction industry. Information from past mishaps provides an opportunity to acquire insights, gather lessons learned, and systematically improve safety outcomes. Advances in data science and industry 4.0 present new unprecedented opportunities for the industry to leverage, share, and reuse safety information more efficiently. However, potential benefits of information sharing are missed due to accident data being inconsistently formatted, non-machine-readable, and inaccessible. Hence, learning opportunities and insights cannot be captured and disseminated to proactively prevent accidents. To address these issues, a novel information sharing system is proposed utilizing linked data, ontologies, and knowledge graph technologies. An ontological approach is developed to semantically model safety information and formalize knowledge pertaining to accident cases. A multi-algorithmic approach is developed for automatically processing and converting accident case data to a resource description framework (RDF), and the SPARQL protocol is deployed to enable query functionalities. Trials and test scenarios utilizing a dataset of 200 real accident cases confirm the effectiveness and efficiency of the system in improving information access, retrieval, and reusability. The proposed development facilitates a new “open” information sharing paradigm with major implications for industry 4.0 and data-driven applications in construction safety management.