Intelligent Critical Path Computation Algorithm Utilising Ant Colony Optimisation for Complex Project Scheduling
In large and complex project schedule networks, existing algorithms to determine the critical path are considerably slow. Therefore, an algorithm with a faster convergence is needed to improve the efficiency of the critical path computation. The ant colony algorithm was first applied to the travelli...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/9930113 |
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| author | Xiaokang Han Wenzhou Yan Mei Lu |
| author_facet | Xiaokang Han Wenzhou Yan Mei Lu |
| author_sort | Xiaokang Han |
| collection | DOAJ |
| description | In large and complex project schedule networks, existing algorithms to determine the critical path are considerably slow. Therefore, an algorithm with a faster convergence is needed to improve the efficiency of the critical path computation. The ant colony algorithm was first applied to the travelling salesman problem to determine the shortest path. However, many problems require the longest path in practice; the critical path in the scheduling problem is the longest path in the scheduling network. In this study, an improved ant colony algorithm to determine the critical path by setting the path distance and time as negative, while the transition probability remains unchanged, is proposed. The case of a coal power plant engineering, procurement, and construction (EPC) project was considered. The results show that a peak number of optimal solutions appeared at approximately the 9th iteration; however, instabilities and continued fluctuations were observed even afterward, indicating that the algorithm has a certain randomness. Convergence is apparent at the 29th iteration; after the 34th iteration, a singular optimal solution, the longest or critical path, is obtained, indicating that the convergence rate can be controlled and that the critical path can be obtained by setting appropriate parameters in the solution method. This has been found to improve the efficiency of calculating the critical path. Case validation and algorithm performance testing confirmed that the improved ant colony algorithm can determine the critical path problem and make it computationally intelligent. |
| format | Article |
| id | doaj-art-5c690ed8a34b48d5902e387d5f0e48a4 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-5c690ed8a34b48d5902e387d5f0e48a42025-02-03T01:27:08ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/99301139930113Intelligent Critical Path Computation Algorithm Utilising Ant Colony Optimisation for Complex Project SchedulingXiaokang Han0Wenzhou Yan1Mei Lu2School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, ChinaSchool of Management, Xi’an University of Architecture and Technology, Xi’an 710055, ChinaSchool of Management, Xi’an University of Architecture and Technology, Xi’an 710055, ChinaIn large and complex project schedule networks, existing algorithms to determine the critical path are considerably slow. Therefore, an algorithm with a faster convergence is needed to improve the efficiency of the critical path computation. The ant colony algorithm was first applied to the travelling salesman problem to determine the shortest path. However, many problems require the longest path in practice; the critical path in the scheduling problem is the longest path in the scheduling network. In this study, an improved ant colony algorithm to determine the critical path by setting the path distance and time as negative, while the transition probability remains unchanged, is proposed. The case of a coal power plant engineering, procurement, and construction (EPC) project was considered. The results show that a peak number of optimal solutions appeared at approximately the 9th iteration; however, instabilities and continued fluctuations were observed even afterward, indicating that the algorithm has a certain randomness. Convergence is apparent at the 29th iteration; after the 34th iteration, a singular optimal solution, the longest or critical path, is obtained, indicating that the convergence rate can be controlled and that the critical path can be obtained by setting appropriate parameters in the solution method. This has been found to improve the efficiency of calculating the critical path. Case validation and algorithm performance testing confirmed that the improved ant colony algorithm can determine the critical path problem and make it computationally intelligent.http://dx.doi.org/10.1155/2021/9930113 |
| spellingShingle | Xiaokang Han Wenzhou Yan Mei Lu Intelligent Critical Path Computation Algorithm Utilising Ant Colony Optimisation for Complex Project Scheduling Complexity |
| title | Intelligent Critical Path Computation Algorithm Utilising Ant Colony Optimisation for Complex Project Scheduling |
| title_full | Intelligent Critical Path Computation Algorithm Utilising Ant Colony Optimisation for Complex Project Scheduling |
| title_fullStr | Intelligent Critical Path Computation Algorithm Utilising Ant Colony Optimisation for Complex Project Scheduling |
| title_full_unstemmed | Intelligent Critical Path Computation Algorithm Utilising Ant Colony Optimisation for Complex Project Scheduling |
| title_short | Intelligent Critical Path Computation Algorithm Utilising Ant Colony Optimisation for Complex Project Scheduling |
| title_sort | intelligent critical path computation algorithm utilising ant colony optimisation for complex project scheduling |
| url | http://dx.doi.org/10.1155/2021/9930113 |
| work_keys_str_mv | AT xiaokanghan intelligentcriticalpathcomputationalgorithmutilisingantcolonyoptimisationforcomplexprojectscheduling AT wenzhouyan intelligentcriticalpathcomputationalgorithmutilisingantcolonyoptimisationforcomplexprojectscheduling AT meilu intelligentcriticalpathcomputationalgorithmutilisingantcolonyoptimisationforcomplexprojectscheduling |