Practical problems in construction can be easily qualified as NP-hard (non-deterministic, polynomial-time hard) problems. The time needed for solving these problems grows exponentially with the increase of the problem’s size – this is why mathematical and heuristic methods do not enable finding solutions to complicated construction problems within an acceptable period of time. In the view of many authors, metaheuristic algorithms seem to be the most appropriate measures for scheduling and task sequencing. However even metaheuristic approach does not guarantee finding the optimal solution and algorithms tend to get stuck around local optima of objective functions. This is why authors considered improving the metaheuristic approach by the use of neural networks. In the article, authors analyse possible benefits of using a hybrid approach with the use of metaheuristics and neural networks for solving the multi-mode, resource-constrained, project-scheduling problem (MRCPSP). The suggested approach is described and tested on a model construction project schedule. The results are promising for construction practitioners, the hybrid approach improved results in 87% of tests. Based on the research outcomes, authors suggest future research ideas.
In the construction practice, at the stage of planning of a construction project, planners are trying to take into account the possibility of unfavorable situations and their consequences during the project execution. Therefore, planning decisions should use appropriate tools for uncertainty modeling as well as consider alternative options for the implementation of the entire undertaking or the most sensitive (critical) works. Taking into account the fact that the management and planning of construction projects is carried out in conditions of non-stochastic uncertainty, the authors propose to create an alternative model with a fuzzy decision node based on classical network models. This approach allows to model activities that are alternative to each other. An important methodological element is the fuzzy assessment of the considered alternatives with the use of fuzzy set theory. The proposed alternative network model with a fuzzy decision node gives the possibility to conduct a comprehensive analysis with the possibility of taking into account and modeling the uncertainty of input data in an easy way. The numerical example shows the possibility of quick selection of the best variant based on the adopted assessment criteria and allows to serialize work variants, which gives additional information in the alternative activities of the network model in the form of preferences. The combination of a decision model in the form of a decision node with a network model can be useful in both strategic and tactical planning of the implementation of construction projects.
In this paper authors present the set of data on the factors affecting the implementation of construction projects in Poland. To develop that data, surveys were conducted among companies and engineers working in construction industry. The result of the paper is classification of the factors affecting the implementation of construction projects, due to the degree of significance. Elements of the fuzzy sets theory were applied, to order those factors, and to develop their formal description. Authors also describe possibility of using ordered factors, to determine the potential risk of variability of the implementation factors. This could be the basis for the design of construction projects in Polish conditions
This paper concerns to the RCPS problem with imprecise activity duration times and flexible constraints. Schedule input data and constraints are modeled by fuzzy numbers. Fuzzy RCPS problem is based on the concept of pair-wise dominance between two fuzzy numbers and solved with the use of simulation.
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