Integrate computation intelligence with Bayes theorem into complex construction installation: a heuristic two-stage resource scheduling optimisation approach

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.

Keywords:

• Resource scheduling
• cost control
• intelligent computing
• heuristic two-stage optimal deployment approach

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work is supported by the National Natural Science Foundation of China [grant number 61972054], the Key R & D Project of Changchun Science and Technology Development Plan [grant number 21ZY53], Jilin Higher Education Teaching Reform Research Project [grant number JLJY202168939653], the Theme Fund of Changchun Institute of Technology [grant number 320200052, 320200053], the Key R & D Project of Jilin Province Science and Technology Development Plan [grant number 20210201127GX], the Industrial Technology R & D Special Project of Jilin Provincial Development and Reform Commission [grant number 2021C045-6], the Fourth Batch of Jilin Province Youth S & T Talent Lift Project [grant number QT202001] and the Scientific Research Initiation Fund for Doctoral Innovation Team. We would like to thank the anonymous reviewers who helped us by commenting on this paper.