Development of high performance heuristic and meta-heuristic methods for resource optimization of large scale construction projects

Abbasi Iranagh, Mahdi
Despite the importance of resource optimization in construction scheduling, very little success has been achieved in solving the resource leveling problem (RLP) and resource constrained discrete time-cost trade-off problem (RCDTCTP), especially for large-scale projects. The major objective of this thesis is to design and develop new heuristic and meta-heuristic methods to achieve fast and high quality solutions for the large-scale RLP and RCDTCTP. Two different methods are presented in this thesis for the RLP, including a memetic algorithm with simulated annealing (MASA) that is adequately generic for unraveling RLPs incorporating any type of known objective functions, and a hybrid genetic algorithm which limits the searching space to only quasistable schedules (QHGA). QHGA is capable of minimizing the sum of squares of daily resource usage or total overloaded amount from a desired level of resource consumptions, for large-scale projects in a very short computation time. The computational experiments reveal that both MASA and QHGA outperform the state-of-art methods for the RLP. QHGA is also integrated to Microsoft Project to enhance the use of the proposed leveling method in practice The final proposed algorithm within the thesis is a heuristic method which is designed and developed to achieve fast and high quality solutions for the large-scale RCDTCTP. The proposed heuristic consists of two parts including the scheduling and the crashing parts. The scheduling part adopts backward-forward scheduling technique for the resource constrained project scheduling problem. In the second part, the critical sequence including the activities that determine the project duration for a resource constrained schedule are crashed. The computational experiment results reveal that the new critical sequence crashing heuristic outperforms the other state-of-art methods, both in terms of the solution quality and computational time. The main contribution of the thesis is that it provides fast and effective methods for optimal scheduling and resource allocation of real-life-size construction projects.


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Critical path method (CPM) has been commonly used for scheduling of construction projects. However, CPM only considers the relations between the activities, but does not optimize the resource allocation. Resource leveling problem (RLP) concentrates on optimizing resource utilization histograms obtained by Critical Path Method (CPM) without changing the project duration. Resource leveling is crucial for effective use of construction resources particularly, manpower and machinery resources to minimize the pro...
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Just like in all industries, some of the available resources, in order to finish a project on time, are constrained in construction industry. To be able to finish the project on time has high importance both for the contractor and for the owner. Project scheduling in which resources are limited for a particular time are called as resource constrained project scheduling problems (RCPSP) and occupies a significant place in construction management. Especially for large scale projects, little success has been a...
Multi-Project Multi-Resource Leveling Using Mixed Integer Programming
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Critical path method (CPM) is commonly used in scheduling of construction projects. However, CPM only considers the precedence relations between the activities and does not consider resource optimization during scheduling of projects. Optimal allocation of resources can be achieved by resource levelling. Resource levelling is crucial for effective use of construction resources particularly to minimize the project costs. However, commercial scheduling software has very limited capabilities for solving the re...
Citation Formats
M. Abbasi Iranagh, “Development of high performance heuristic and meta-heuristic methods for resource optimization of large scale construction projects,” Ph.D. - Doctoral Program, Middle East Technical University, 2015.