Integrate computation intelligence with Bayes theorem into complex construction installation: a heuristic two-stage resource scheduling optimisation approach
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 co...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-12-01
|
| Series: | Connection Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/09540091.2023.2186333 |
| _version_ | 1797684035535765504 |
|---|---|
| author | Jia Zhao Wenhao Wang Liyuan Zhang Yan Ding |
| author_facet | Jia Zhao Wenhao Wang Liyuan Zhang Yan Ding |
| author_sort | Jia Zhao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T00:23:32Z |
| format | Article |
| id | doaj.art-e200348eed624ca1948e5d09286ce9b8 |
| institution | Directory Open Access Journal |
| issn | 0954-0091 1360-0494 |
| language | English |
| last_indexed | 2024-03-12T00:23:32Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj.art-e200348eed624ca1948e5d09286ce9b82023-09-15T10:48:01ZengTaylor & Francis GroupConnection Science0954-00911360-04942023-12-0135110.1080/09540091.2023.21863332186333Integrate computation intelligence with Bayes theorem into complex construction installation: a heuristic two-stage resource scheduling optimisation approachJia Zhao0Wenhao Wang1Liyuan Zhang2Yan Ding3Changchun Institute of TechnologyChangchun Institute of TechnologyChangchun Institute of TechnologyChangchun Institute of TechnologyThe 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.http://dx.doi.org/10.1080/09540091.2023.2186333resource schedulingcost controlintelligent computingheuristic two-stage optimal deployment approach |
| spellingShingle | Jia Zhao Wenhao Wang Liyuan Zhang Yan Ding Integrate computation intelligence with Bayes theorem into complex construction installation: a heuristic two-stage resource scheduling optimisation approach Connection Science resource scheduling cost control intelligent computing heuristic two-stage optimal deployment approach |
| title | Integrate computation intelligence with Bayes theorem into complex construction installation: a heuristic two-stage resource scheduling optimisation approach |
| title_full | Integrate computation intelligence with Bayes theorem into complex construction installation: a heuristic two-stage resource scheduling optimisation approach |
| title_fullStr | Integrate computation intelligence with Bayes theorem into complex construction installation: a heuristic two-stage resource scheduling optimisation approach |
| title_full_unstemmed | Integrate computation intelligence with Bayes theorem into complex construction installation: a heuristic two-stage resource scheduling optimisation approach |
| title_short | Integrate computation intelligence with Bayes theorem into complex construction installation: a heuristic two-stage resource scheduling optimisation approach |
| title_sort | integrate computation intelligence with bayes theorem into complex construction installation a heuristic two stage resource scheduling optimisation approach |
| topic | resource scheduling cost control intelligent computing heuristic two-stage optimal deployment approach |
| url | http://dx.doi.org/10.1080/09540091.2023.2186333 |
| work_keys_str_mv | AT jiazhao integratecomputationintelligencewithbayestheoremintocomplexconstructioninstallationaheuristictwostageresourceschedulingoptimisationapproach AT wenhaowang integratecomputationintelligencewithbayestheoremintocomplexconstructioninstallationaheuristictwostageresourceschedulingoptimisationapproach AT liyuanzhang integratecomputationintelligencewithbayestheoremintocomplexconstructioninstallationaheuristictwostageresourceschedulingoptimisationapproach AT yanding integratecomputationintelligencewithbayestheoremintocomplexconstructioninstallationaheuristictwostageresourceschedulingoptimisationapproach |