Given that a construction site is both temporary and unique, the outcome of every construction project is dependent upon having all of the proper resources delivered to the site at the appropriate time. Although this is common knowledge in the construction industry, late deliverables to the site continue to be a major impediment to project success. In order to better understand late deliverables and their impacts on performance, the Construction Industry Institute, in collaboration with the Construction Users Roundtable, commissioned Research Team (RT) 300 to investigate how various types of late deliverables affect the cost, schedule, quality, safety, and organizational performance of industrial construction projects. Using case studies, industry surveys and questionnaires, existing literature, and internal team expertise, RT 300 developed two research thrusts: investigate how the industry understands, manages, and is affected by late deliverables, and document and give visibility to the true risks and impacts associated with late deliverables. When examining how late deliverables affect the construction industry, RT 300 found that (1) there is limited understanding of the full range of late deliverables and their far-reaching impacts, (2) the most common late deliverables tend to have the most severe impacts on projects, (3) project teams are typically reactionary when managing late deliverables, (4) project stakeholders have varying perceptions of the risks and impacts associated with late deliverables, and (5) proactively managing late deliverables and impacts is key for improvement in the industry. With these findings and the second research thrust in mind, RT 300 created a database tool, the Late Deliverable Risk Catalog (LDRC), to document common types of late deliverables, give visibility to the full range of impacts, and help project teams recognize risks, improve alignment, and proactively manage late deliverables and mitigate the impacts. RT 300 has also developed implementation recommendations for the LDRC, prevention recommendations for the highest risk deliverables, and lessons learned in managing late deliverables. Altogether, this research can help improve the understanding of late deliverables and resulting impacts and risks in order to improve project delivery, productivity, and predictability as well as enhance safety, quality, and organizational and individual performance. / text

Primary polydipsia, excessive drinking without known medical cause, continues to occur with a significant prevalence in psychiatric populations. While the etiology of polydipsia remains unknown, the fact that it is significantly associated with a diagnosis of schizophrenia has led some to postulate that the two may share common neurological pathophysiologies. Animal models of schizophrenia-like symptoms have focused on modeling the core behavioral and neurochemical features of the illness, like cognitive deficits and enhanced dopamine transmission. Here, we used three well-established models, including repeated amphetamine treatment, subchronic MK-801 (an N-methyl-D-aspartate [NMDA]-receptor antagonist), and post-weaning social isolation. We also examined a “double-hit” model, combining NMDA-receptor antagonism and social isolation. We paired these models to test the hypothesis that drinking will be enhanced in a paradigm of excessive drinking in the rat. In rodents, non-physiologic drinking can be induced by intermittent presentation of food (e.g., one sugar-pellet a minute) in the presence of a drinking spout to a hungry animal, termed schedule-induced polydipsia (SIP). Animals pretreated with pharmacological or non-pharmacological models of schizophrenia-like symptoms showed significantly increased SIP, The “double hit” model did not further increase drinking above that of either social isolation or MK-801 treatment alone. A moderate amount of spontaneous polydipsia in the homecage of MK-801-treated rats was also observed and resulted in one death secondary to excessive drinking, a phenomenon also found in inpatients with schizophrenia. Following repeated treatment with AMPH, there was some evidence that over time, animals learned to drink increased amounts independently of the scheduled food presentation. This evidence suggests that the excessive drinking behavior observed in polydipsia associated with schizophrenia may have a learned component. In summary, animal models of schizophrenia-like symptoms augmented SIP behavior, showing that polydipsia associated with schizophrenia may be modeled in rodents. As each model has been shown to modify dopamine transmission to some degree, the evidence suggests augmented SIP may reflect changes in dopamine transmission and dopamine may be the common link between polydipsia and schizophrenia. Further research is necessary to fully elucidate the mechanisms underlying SIP, polydipsia and schizophrenia. / Thesis (Ph.D, Neuroscience Studies) -- Queen's University, 2012-10-31 17:43:18.34

This research investigates a batch arrival queueing system with a Bernoulli scheduled vacation and random system breakdowns. It is assumed that the repair process does not start immediately after the breakdown. Consequently there maybe a delay in starting repairs. After every service completion the server may go on an optional vacation. When the original vacation is completed the server has the option to go on an extended vacation. It is assumed that the system is equipped with a stand-by server to serve the customers during the vacation period of the main server as well as during the repair process. The service times, vacation times, repair times, delay times and extended vacation times are assumed to follow different general distributions while the breakdown times and the service times of the stand-by server follow an exponential distribution. By introducing a supplementary variable we are able to obtain steady state results in an explicit closed form in terms of the probability generating functions. Some important performance measures including; the average length of the queue, the average number of customers in the system, the mean response time, and the value of the traffic intensity are presented. The professional MathCad 2001 software has been used to illustrate the numerical results in this study.

This study was designed to determine the effects of two different schedule types on mathematics achievement in public high school students. The instruments used included the Texas Assessment of Knowledge and Skills, given annually to all students in grades 3 through 11, the Texas Algebra I end-of-course examination, given as a district option to Algebra I students, and student final course grades as determined by classroom teachers. The study compared students' performance in these three areas during the 2004-2005 academic year in one suburban school district in North Texas. The study considers the type of schedule, either traditional or 8-block, between students in teachers' classes who teach the same course on both schedules concurrently. This study also investigates a qualitative aspect by including a short opinion survey of teachers' perceptions regarding student academic performance, teacher satisfaction and retention, and the ability to accomplish curricular goals. Findings from this research suggest course schedule does not have significant effects on student academic performance as measured using analyses of covariance comparisons with a 0.05 alpha-level, leading to the conclusion that a particular course schedule does not adversely impact student performance on academic measures. However, in some comparisons conducted within the course of the research, statistically significant results emerged. Qualitative data generated from a survey of teacher perceptions regarding the benefits of the two scheduling types, traditional 50-minute verses alternating day 8-block, suggested teachers preferred a traditional schedule over that of a block schedule design. Most teachers who responded to the survey instrument expressed the perception that traditional daily meeting classes allowed their students to be more successful. Additional research into the effects of scheduling types on students academic performance are suggested and would include examining larger population samples, a narrower study of specific courses within the field of mathematics, or an expansion of the content areas explored to fields such as science, languages, or non-academic core subjects, including the fine arts.

Research using visual activity schedules (VAS) with embedded video models (VM) has been effective to teach novel skills to children and adolescents with intellectual disability (ID). However, there is limited research using VAS/VM to teach adults with ID. The purpose of the current study was to determine if VAS/VM could be used as a self-instructional tool for adults with ID to increase independence in laundry skills. Results from the current study provide evidence that a functional relation exists when using VAS/VM to teach laundry skills to adults with ID. Two adults generalized the use of the VAS/VM to a novel setting with similar tasks.

Traditional clustering algorithms have different criteria and biases, and there is no single algorithm that can be the best solution for a wide range of data sets. This problem often presents a significant obstacle to analysts in revealing meaningful information buried among the huge amount of data. Ensemble Clustering has been proposed as a way to avoid the biases and improve the accuracy of clustering. The difficulty in developing Ensemble Clustering methods is to combine external information (provided by input clusterings) with internal information (i.e. characteristics of given data) effectively to improve the accuracy of clustering. The work presented in this thesis focuses on enhancing the clustering accuracy of Ensemble Clustering by employing heuristic optimisation techniques to achieve a robust combination of relevant information during the consensus clustering stage. Two novel heuristic optimisation-based Ensemble Clustering methods, Multi-Optimisation Consensus Clustering (MOCC) and K-Ants Consensus Clustering (KACC), are developed and introduced in this thesis. These methods utilise two heuristic optimisation algorithms (Simulated Annealing and Ant Colony Optimisation) for their Ensemble Clustering frameworks, and have been proved to outperform other methods in the area. The extensive experimental results, together with a detailed analysis, will be presented in this thesis.

The purpose of the current study was to investigate the immediate and subsequent effects of fixed-time attention on problem behavior maintained by therapist attention utilizing a three-component multiple-schedule design. The treatment analysis indicated that fixed-time attention produced a significant immediate decrease in the frequency of physically disruptive behavior (PDB), represented by low frequencies of PDB in Component 2, as well as a continued subsequent effect, represented by lower frequencies of problem behavior in Component 3 when compared to Component 1. The possible behavioral mechanisms responsible for the observed suppression in Component 2 of the treatment analysis are discussed. Evidence of behavioral contrast was observed in Components 1 and 3 of the treatment analysis in conditions in which Component 2 contained a fixed-time schedule of stimulus delivery. In addition, limitations and future research are outlined.

Abstract In many institutional settings, blocking, response restriction (e.g., restraint, protective equipment), and re-direction procedures are used extensively as intervention for self-injurious behavior (SIB) and other forms of problem behavior. In the current study, a three component, multiple-schedule analysis was used to examine the immediate and subsequent effects of blocking on SIB that persisted in the absence of social reinforcement contingencies. During the first and third components the participant was in the room, alone, with no social consequences for SIB. During the second component (response restriction) the therapist sat in the room with the participant and blocked occurrences of SIB. Results indicated that, although blocking was effective in decreasing SIB while it was being implemented, subsequent effects were idiosyncratic across participants. Evidence of increased levels of SIB following blocking was observed for some participants.

One of the problems of real-life production scheduling is dynamics of manufacturing environments with new production demands and breaking machines during the schedule execution. Simple rescheduling from scratch in response to unexpected events occurring on the shop floor may require excessive computation time. Moreover, the recovered schedule may be prohibitively deviated from the ongoing schedule. This thesis reviews existing approaches in the field of dynamic scheduling and proposes techniques how to modify a schedule to accommodate disturbances such as resource failure, hot order arrival or order cancellation. The importance is put on the speed of suggested procedures as well as on a minimum modification from the original schedule. The scheduling model is motivated by the FlowOpt project, which is based on the Temporal Networks with Alternatives. The algorithms are written in the C# language.

With maritime industry carrying out nearly 90% of the volume of global trade, the algorithms and solutions to provide quality of services in maritime transportation are of great importance to both academia and the industry. This research investigates an optimization problem using evolutionary algorithms and big data analytics to address an important challenge in maritime disruption management, and illustrates how it can be engaged with information technologies and Internet of Things. Accordingly, in this thesis, we design, develop and evaluate methods to improve decision support systems (DSSs) in maritime supply chain management. We pursue three research goals in this thesis. First, the Vessel Schedule recovery Problem (VSRP) is reformulated and a bi-objective optimization approach is proposed. We employ bi-objective evolutionary algorithms (MOEAs) to solve optimization problems. An optimal Pareto front provides a valuable trade-off between two objectives (minimizing delay and minimizing financial loss) for a stakeholder in the freight ship company. We evaluate the problem in three domains, namely scalability analysis, vessel steaming policies, and voyage distance analysis, and statistically validate their performance significance. According to the experiments, the problem complexity varies in different scenarios, while NSGAII performs better than other MOEAs in all scenarios. In the second work, a new data-driven VSRP is proposed, which benefits from the available Automatic Identification System (AIS) data. In the new formulation, the trajectory between the port calls is divided and encoded into adjacent geohashed regions. In each geohash, the historical speed profiles are extracted from AIS data. This results in a large-scale optimization problem called G-S-VSRP with three objectives (i.e., minimizing loss, delay, and maximizing compliance) where the compliance objective maximizes the compliance of optimized speeds with the historical data. Assuming that the historical speed profiles are reliable to trust for actual operational speeds based on other ships' experience, maximizing the compliance of optimized speeds with these historical data offers some degree of avoiding risks. Three MOEAs tackled the problem and provided the stakeholder with a Pareto front which reflects the trade-off among the three objectives. Geohash granularity and dimensionality reduction techniques were evaluated and discussed for the model. G-S-VSRPis a large-scale optimization problem and suffers from the curse of dimensionality (i.e. problems are difficult to solve due to exponential growth in the size of the multi-dimensional solution space), however, due to a special characteristic of the problem instance, a large number of function evaluations in MOEAs can still find a good set of solutions. Finally, when the compliance objective in G-S-VSRP is changed to minimization, the regular MOEAs perform poorly due to the curse of dimensionality. We focus on improving the performance of the large-scale G-S-VSRP through a novel distributed multiobjective cooperative coevolution algorithm (DMOCCA). The proposed DMOCCA improves the quality of performance metrics compared to the regular MOEAs (i.e. NSGAII, NSGAIII, and GDE3). Additionally, the DMOCCA results in speedup when running on a cluster.