Integrate computation intelligence with Bayes theorem into complex construction installation: a heuristic two-stage resource scheduling optimisation approach. Issue 1 (31st December 2023)

• Record Type: Journal Article
• Authors: Zhao, Jia; Wang, Wenhao; Zhang, Liyuan; Ding, Yan
• Is Part Of: Connection science. Volume 35:Issue 1(2023)
• Publication Date: 2023-12-31
• Subjects: Resource scheduling -- cost control -- intelligent computing -- heuristic two-stage optimal deployment approach; Neural computers -- Periodicals; Artificial intelligence -- Periodicals; Cognitive science -- Periodicals; Connectionism -- Periodicals; 006.3
• DOI: 10.1080/09540091.2023.2186333
• Languages: English
• ISSNs: 0954-0091

Abstract

Abstract : The cost control challenge in construction and installation projects has always been a critical concern for construction entities. The complexity of task collaboration among various equipment and nodes during the installation process leads to extended construction duration, resulting in increased construction costs. To address this issue, this paper proposes a heuristic two-stage optimal deployment approach called MERD. The MERD approach incorporates intelligent computing principles from computer science into the resource scheduling of the construction process, modelling the installation scheduling problem into a combinatorial optimisation problem. Designing the probability method based on Bayes theorem, the MERD approach carries out an installation provisioning mechanism to optimise personnel and device allocation in the selected area. As a result, the MERD approach minimises construction hours and reduces labour costs in the construction process. Experimental results demonstrate the effectiveness and efficiency of the MERD approach in reducing work time and cost in engineering projects.