Abstracts – Browse Results

Search or browse again.

Click on the titles below to expand the information about each abstract.
Viewing 18 results ...

Aboseif, E and Hanna, A S (2024) Benchmarking of construction projects performance for comparative assessment and performance improvement: a statistical quantitative approach. Engineering, Construction and Architectural Management, 31(07), 2829-51.

Al-Hadidi, S, Sweis, G, Abu-Khader, W, Abu-Rumman, G and Sweis, R (2024) Managing future urbanization growth patterns using genetic algorithm modeling. Engineering, Construction and Architectural Management, 31(07), 2648-68.

Delgado-Hernández, D J and Palacios-Navarro, U J (2024) A Bayesian network for selecting improvement management tools to increase customer satisfaction in the construction industry: case study of Mexico. Engineering, Construction and Architectural Management, 31(07), 2900-15.

Gibson, G E, El Asmar, M, Yussef, A and Ramsey, D (2024) A novel approach for measuring the accuracy of front end engineering design. Engineering, Construction and Architectural Management, 31(07), 2669-96.

Guo, J, Xiang, P, Liu, Q and Luo, Y (2024) Transportation infrastructure network optimisation based on computer simulation using Sichuan Province, China as an example. Engineering, Construction and Architectural Management, 31(07), 2740-69.

Khan, S, Panuwatwanich, K and Usanavasin, S (2024) Integrating building information modeling with augmented reality: application and empirical assessment in building facility management. Engineering, Construction and Architectural Management, 31(07), 2809-28.

Khanapure, V U and Shastri, S S (2024) A sustainable–lean implementation methodology: a catalyst for sustainability enhancement of high-rise residential projects. Engineering, Construction and Architectural Management, 31(07), 2585-603.

Kum, Y T, Yap, J B H, Lew, Y L and Lee, W P (2024) Transforming construction health and safety management during COVID-19 pandemic using innovative technologies: PLS-SEM approach. Engineering, Construction and Architectural Management, 31(07), 2770-808.

Lad, V H, Patel, D A, Chauhan, K A and Patel, K A (2024) Prioritisation of bridge for improving its resilience using multi-criteria decision-making techniques. Engineering, Construction and Architectural Management, 31(07), 2952-76.

Li, T, Li, Z and Cai, J (2024) Antecedents and consequence of intra-organizational diffusion of prefabricated construction technology: a contingency theory perspective. Engineering, Construction and Architectural Management, 31(07), 2932-51.

Li, X, Chang, C T, Zhu, Y Y and Li, L L (2024) Key influence factors on the evacuation route selection for fire emergencies in urban underground complexes. Engineering, Construction and Architectural Management, 31(07), 2630-47.

Lin, Q, Zhang, C, Cai, H, Li, X and Xiao, H (2024) Urban night lighting evaluation system and case study: blending popular contemporary elements, cultural traditions and advanced lighting technologies. Engineering, Construction and Architectural Management, 31(07), 2916-31.

Lu, Y, Liu, J and Yu, W (2024) Social risk analysis for mega construction projects based on structural equation model and Bayesian network: a risk evolution perspective. Engineering, Construction and Architectural Management, 31(07), 2604-29.

Masood, R, Roy, K, González, V A, Lim, J B P and Nasir, A R (2024) A supply chain perspective of prefabricated housebuilding diffusion in New Zealand. Engineering, Construction and Architectural Management, 31(07), 2697-720.

Tennakoon, G A, Rameezdeen, R and Chileshe, N (2024) Walking the talk towards sustainable consumption: interventions to promote the uptake of reprocessed construction materials. Engineering, Construction and Architectural Management, 31(07), 2878-99.

Wang, Z, Shi, L and Cui, H (2024) Effects of the host-country institutional environment on the operation mode change of international construction enterprises. Engineering, Construction and Architectural Management, 31(07), 2721-39.

Xie, L L, Chen, Y, Wu, S, Chang, R D and Han, Y (2024) Knowledge extraction for solving resource-constrained project scheduling problem through decision tree. Engineering, Construction and Architectural Management, 31(07), 2852-77.

  • Type: Journal Article
  • Keywords: data mining; decision tree; genetic algorithm; project scheduling; resource-constrained
  • ISBN/ISSN:
  • URL: https://doi.org/10.1108/ECAM-04-2022-0345
  • Abstract:
    Purpose: Project scheduling plays an essential role in the implementation of a project due to the limitation of resources in practical projects. However, the existing research tend to focus on finding suitable algorithms to solve various scheduling problems and fail to find the potential scheduling rules in these optimal or near-optimal solutions, that is, the possible intrinsic relationships between attributes related to the scheduling of activity sequences. Data mining (DM) is used to analyze and interpret data to obtain valuable information stored in large-scale data. The goal of this paper is to use DM to discover scheduling concepts and obtain a set of rules that approximate effective solutions to resource-constrained project scheduling problems. These rules do not require any search and simulation, which have extremely low time complexity and support real-time decision-making to improve planning/scheduling. Design/methodology/approach: The resource-constrained project scheduling problem can be described as scheduling a group of interrelated activities to optimize the project completion time and other objectives while satisfying the activity priority relationship and resource constraints. This paper proposes a new approach to solve the resource-constrained project scheduling problem by combining DM technology and the genetic algorithm (GA). More specifically, the GA is used to generate various optimal project scheduling schemes, after that C4.5 decision tree (DT) is adopted to obtain valuable knowledge from these schemes for further predicting and solving new scheduling problems. Findings: In this study, the authors use GA and DM technology to analyze and extract knowledge from a large number of scheduling schemes, and determine the scheduling rule set to minimize the completion time. In order to verify the application effect of the proposed DT classification model, the J30, J60 and J120 datasets in PSPLIB are used to test the validity of the scheduling rules. The results show that DT can readily duplicate the excellent performance of GA for scheduling problems of different scales. In addition, the DT prediction model developed in this study is applied to a high-rise residential project consisting of 117 activities. The results show that compared with the completion time obtained by GA, the DT model can realize rapid adjustment of project scheduling problem to deal with the dynamic environment interference. In a word, the data-based approach is feasible, practical and effective. It not only captures the knowledge contained in the known optimal scheduling schemes, but also helps to provide a flexible scheduling decision-making approach for project implementation. Originality/value: This paper proposes a novel knowledge-based project scheduling approach. In previous studies, intelligent optimization algorithm is often used to solve the project scheduling problem. However, although these intelligent optimization algorithms can generate a set of effective solutions for problem instances, they are unable to explain the process of decision-making, nor can they identify the characteristics of good scheduling decisions generated by the optimization process. Moreover, their calculation is slow and complex, which is not suitable for planning and scheduling complex projects. In this study, the set of effective solutions of problem instances is taken as the training dataset of DM algorithm, and the extracted scheduling rules can provide the prediction and solution of new scheduling problems. The proposed method focuses on identifying the key parameters of a specific dynamic scheduling environment, which can not only reproduces the scheduling performance of the original algorithm well, but also has the ability to make decisions quickly under the dynamic interference construction scenario. It is helpful for project managers to implement quick decisions in response to construction emergencies, which is of great practical significance for improving the flexibility and efficiency of construction projects. © 2023, Emerald Publ shing Limited.

Zhou, X, Wang, Y, Zhang, Y and Liu, F (2024) Macro investigation on China's engineering insurance industry: based on industrial organization theories. Engineering, Construction and Architectural Management, 31(07), 2977-94.