Construction completion forecasting on the basis of statistical modeling and heterogeneous monitoring data

Modern construction represents a complex production process whose effective regulation is based on information about the period of construction works, obtained by way of monitoring. Any delay in the execution of certain works frustrates scheduled project commissioning, which results in increased project management costs, forfeits and lost benefits. Existing monitoring systems need to be improved through the use of probabilistic scheduling geared towards the forecasting of completion dates of certain works and the construction process as a whole. Construction monitoring may be improved by means of taking management quality into account by means of distributing random work durations in the process of statistical modeling of functions. The introduction of six random duration distribution functions, reflecting management quality, is proposed for the improvement of statistical modeling, whereas the use of scheduled durations of works is expedient for identifying optimistic characteristics. The data, extracted from monitoring reports, needs to be used as pessimistic distributions. Monitoring reports contain heterogeneous data, and they may even have no information on particular types of works. Therefore, pessimistic durations based on missing data should be calculated using the time-space analogy method.

PSM: A New Approach to Probabilistic Scheduling

A new scheduling method, where probability values can be assigned to activity durations, is proposed in this thesis. Probabilistic Scheduling Method (PSM) accepts activity durations tagged with probability or confidence intervals. Tests were carried out using examples of 3,7, and 9 activities to evaluate PSM's practical capability. The comparisons of PSM to Critical Path Method (CPM), Performance Evaluation and Review Technique (PERT), and Monte Carlo application to PERT (MC PERT) conclude that PSM results in similar most probable duration estimation. Further tests were implemented to evaluate PSM's capability to project schedule revision on an ongoing project. A microsoft Excel application was used to organize tests data and calculations. PSM computations are more industry friendly. They allow for a range of duration associated with a range of probabilites. PSM provides flexibility and simplicity, and also dependency information that will benefit its user in decision making

PSM: A New Approach to Probabilistic Scheduling

A new scheduling method, where probability values can be assigned to activity durations, is proposed in this thesis. Probabilistic Scheduling Method (PSM) accepts activity durations tagged with probability or confidence intervals. Tests were carried out using examples of 3,7, and 9 activities to evaluate PSM's practical capability. The comparisons of PSM to Critical Path Method (CPM), Performance Evaluation and Review Technique (PERT), and Monte Carlo application to PERT (MC PERT) conclude that PSM results in similar most probable duration estimation. Further tests were implemented to evaluate PSM's capability to project schedule revision on an ongoing project. A microsoft Excel application was used to organize tests data and calculations. PSM computations are more industry friendly. They allow for a range of duration associated with a range of probabilites. PSM provides flexibility and simplicity, and also dependency information that will benefit its user in decision making

VidCloud

As video-streaming services have expanded and improved, cloud-based video has evolved into a necessary feature of any successful business for reaching internal and external audiences. In this article, video streaming over distributed storage is considered where the video segments are encoded using an erasure code for better reliability. We consider a representative system architecture for a realistic (typical) content delivery network (CDN). Given multiple parallel streams/link between each server and the edge router, we need to determine, for each client request, the subset of servers to stream the video, as well as one of the parallel streams from each chosen server. To have this scheduling, this article proposes a two-stage probabilistic scheduling. The selection of video quality is also chosen with a certain probability distribution that is optimized in our algorithm. With these parameters, the playback time of video segments is determined by characterizing the download time of each coded chunk for each video segment. Using the playback times, a bound on the moment generating function of the stall duration is used to bound the mean stall duration. Based on this, we formulate an optimization problem to jointly optimize the convex combination of mean stall duration and average video quality for all requests, where the two-stage probabilistic scheduling, video quality selection, bandwidth split among parallel streams, and auxiliary bound parameters can be chosen. This non-convex problem is solved using an efficient iterative algorithm. Based on the offline version of our proposed algorithm, an online policy is developed where servers selection, quality, bandwidth split, and parallel streams are selected in an online manner. Experimental results show significant improvement in QoE metrics for cloud-based video as compared to the considered baselines.

Optimal Probabilistic Scheduling in Time Slotted Multiple Access

This paper presents an efficient scheduling model for the delivery of sensing data in networks that use time division multiple access. The model is capable of achieving the optimal solution in terms of total delivery time, given certain constraints on radio resources. The proposed solution adopts a probabilistic approach which is based on a problem formulation utilizing chained binomial distributions.