Delay causes analysis in complex construction projects: a Semantic Network Analysis approach

Delays are among the most crucial adversaries to the success and performance of construction projects, making delay analysis and management a critical task for project managers. This task will be highly complicated in large-scale projects such as construction, which usually consist of a complex network of heterogeneous entities in continuous interaction. Traditional approaches and methods for the analysis of delays and their causes have been criticised for their ability to handle complex projects, and for considering the interrelationships between delay causes. Addressing this gap, this research introduces an alternative approach for delay causes analysis by adopting Semantic Network Analysis (SNA) method. The paper reports the results from an investigation of delays in construction projects in the Oil-Gas-Petrochemical sector using SNA. The method’s capacity to identify and rank delay causes, which can assist managers in selecting appropriate measures for eliminating them, are empirically examined and discussed. The paper argues that SNA leads to a more comprehensive understanding of the main causes of delay in large and complex projects, allowing a better identification and mapping of the interrelationships between these discrete factors.     

New Modified Scrambled Faure Sequences

In this paper, we introduce two new modified scrambled Faure sequences based on linear digit scrambling method with good two-dimensional projections in consecutive dimensions using two new linear digit scrambling matrices for linear and nonlinear scrambled Faure sequences. Also, we present performance of our two new scrambling methods by two-dimensional projections and test-integrals.     

Reducing Periodicity of Fuzzy Markov Chains Based on Simulation Using Halton Sequence

We first introduce fuzzy finite Markov chains and present some of their fundamental properties based on possibility theory. We also bring in a way to convert fuzzy Markov chains to classic Markov chains. In addition, we simulate fuzzy Markov chain using different sizes. It is observed that the most of fuzzy Markov chains not only do have an ergodic behavior, but also they are periodic. Finally, using Halton quasi-random sequence we generate some fuzzy Markov chains, which are compared with the ones generated by the RAND function of MATLAB. Therefore, we improve the periodicity behavior of fuzzy Markov chains.     

Optimal Halton Sequence via Inversive Scrambling

In this article, we develop the Halton sequence of generating quasi-random numbers to an optimal sequence which has the inversive property. The new constructed quasi-random number generator satisfies the extra uniformity condition on [0, 1]. We finally present the performances of this generator in contrast to the former optimal Halton sequence in Chi et al. (2005 Chi, H., Mascagni, M. and Warnock, T. 2005. On the Optimal Halton Sequences. Math. Comput. in Simul., 70(1): 9–21. [Crossref], [Web of Science ®] , [Google Scholar]) and modified optimal Halton sequence in Fathi et al. (2009 Fathi, B., Samimi, H. and Eskandari, A. 2009. A New Efficient Algorithm for Linear Scrambling Halton Sequence. International Journal of Applied Mathematics, 22(7): 1059–1065.  [Google Scholar]).     

A non parametric approach to monitor simple linear profiles in phases I and II

In this paper, a non parametric approach is first proposed to monitor simple linear profiles with non normal error terms in Phase I and Phase II. In this approach, two control charts based on a transformation technique and decision on beliefs are designed in order to monitor the intercept and the slope, simultaneously. Then, some simulation experiments are performed in order to evaluate the performance of the proposed control charts in Phase II under both step and drift shifts in terms of out-of-control average run length (ARL₁). Besides, the performance of the proposed control charts is compared to the ones of seven other existing schemes in the literature. Simulation results show that the proposed control charts outperform the other control charts in detecting both the small step and small drift shifts of intercept. However, they have a weaker performance compared to other control charts in detecting both small step and small drift shifts of the slope. At the end, a real example from an electronic industry is used to illustrate the implementation of the proposed method.