Applying Bayesian networks to model uncertainty in project scheduling

PhDRisk Management has become an important part of Project Management. In spite of numerous advances in the field of Project Risk Management (PRM), handling uncertainty in complex projects still remains a challenge. An important component of Project Risk Management (PRM) is risk analysis, which attempts to measure risk and its impact on different project parameters such as time, cost and quality. By highlighting the trade-off between project parameters, the thesis concentrates on project time management under uncertainty. The earliest research incorporating uncertainty/risk in projects started in the late 1950’s. Since then, several techniques and tools have been introduced, and many of them are widely used and applied throughout different industries. However, they often fail to capture uncertainty properly and produce inaccurate, inconsistent and unreliable results. This is evident from consistent problems of cost and schedule overrun. The thesis will argue that the simulation-based techniques, as the dominant and state-of-the-art approach for modelling uncertainty in projects, suffers from serious shortcomings. More advanced techniques are required. Bayesian Networks (BNs), are a powerful technique for decision support under uncertainty that have attracted a lot of attention in different fields. However, applying BNs in project risk management is novel. The thesis aims to show that BN modelling can improve project risk assessment. A literature review explores the important limitations of the current practice of project scheduling under uncertainty. A new model is proposed which applies BNs for performing the famous Critical Path Method (CPM) calculation. The model subsumes the benefits of CPM while adding BN capability to properly capture different aspects of uncertainty in project scheduling

A Framework for Leveraging Artificial Intelligence in Project Management

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementThis dissertation aims to support the project manager in their daily tasks. As we use artificial intelligence (AI) and machine learning (ML) in everyday life, it is necessary to include them in business and change traditional ways of working. For the purpose of this study, it is essential to understand challenges and areas of project management and how artificial intelligence can contribute to them. A theoretical overview, applying the knowledge of project management, will show a holistic view of the current situation in the enterprises. The research is about artificial intelligence applications in project management, the common activities in project management, the biggest challenges, and how AI and ML can support it. Understanding project managers help create a framework that will contribute to optimizing their tasks. After designing and developing the framework for applying artificial intelligence to project management, the project managers were asked to evaluate. This study is essential to increase awareness among the stakeholders and enterprises on how automation of the processes can be improved and how AI and ML can decrease the possibility of risk and cost along with improving the happiness and efficiency of the employees

Technology and Management Applied in Construction Engineering Projects

This book focuses on fundamental and applied research on construction project management. It presents research papers and practice-oriented papers. The execution of construction projects is specific and particularly difficult because each implementation is a unique, complex, and dynamic process that consists of several or more subprocesses that are related to each other, in which various aspects of the investment process participate. Therefore, there is still a vital need to study, research, and conclude the engineering technology and management applied in construction projects. This book present unanimous research approach is a result of many years of studies, conducted by 35 well experienced authors. The common subject of research concerns the development of methods and tools for modeling multi-criteria processes in construction engineering

Relationship Between Time Estimation, Cost Estimation, and Project Performance

Although the project management role is increasing, project failure rates remain high. Project time and cost are 2 project factors that can affect the performance of the projects. The purpose of this correlational study was to examine the relationship between time estimation, cost estimation, and project performance. Data collection involved a purposive sample of 67 project sponsors, managers, and coordinators in Qatar. The theoretical framework was the iron triangle, also known as the triple constraints. Participants were randomly invited to answer 18 questions using the project implementation profile instrument. A standard multiple regression analysis was used to examine the correlation between the independent variables and the dependent variable. A significant linear relationship was found of time estimation and cost estimation to the project performance, F (2,63) = 24.57, p \u3c .05, R = .66, R2 = .44, and adj. R2 = .42. The null hypothesis was rejected that there was no relationship between time estimation, cost estimation, and project performance. The statistically proven findings of the study might provide researchers and practitioners with microlevel information about project factors that influence project performance. The increased rate of project performance might bring about social change by leading to the improvement of local communities, increasing business performance, increasing economies\u27 sustainability, increasing the quality of life, opening new business opportunities, and increasing the rate of employment

Development of Simulation Based Approaches for Cost Estimation and Effect Analysis in Industrial and Humanitarian projects, Including System Dynamic Model and Monte Carlo Simulation

Cost management has become an integral part of management fields these days and has acquired great weight in the sector of project management as well. For most beneficiaries in the industry and humanitarian field, the management of projects is synonymous with the management of cost that affects directly the funds they need to mobilize to deliver their scheme. This thesis deals with the development and validation of simulation-based methods in the industry and the humanitarian field. In addition, several novel methods of cost management have been proposed considering the complexity of different factors. In the industry field, construction projects are characterized by great uncertainty. Appropriate risk analysis techniques are required to estimate the adequate coverage level against the occurrence of extra costs to increase the progress of the project in the tenders. The project margin increases when an excessive provision leads to more comprehensive coverage of the risks. Also, an accurate estimation of the contingency reserve is a crucial subject in construction projects to reduce the risk of overruns\u2019 costs to an acceptable level and ensure the competitiveness of the company\u2019s bid. To achieve this goal, a Company\u2019s traditional approach has been applied to a real railway project and then a stochastic Risk Mode and Effect Analysis (RMEA) methodology base on Monte Carlo Simulation compared with the outcome of the company\u2019s traditional approach applied to the same project. Most of the contingency estimation methods are included problems of subjectivity, complex mathematical models, and inaccurate estimation. This research proposes a combination of the Risk Mode and Effect Analysis (RMEA) with Monte Carlo Simulation (MCS) to determine the amount of allocated contingency fund that overcomes other methods\u2019 limitations. The output of the analysis is a cumulative distribution function that demonstrates a coverage level related to the contingency amount to control extra cost and reduce the amount of contingency in projects. The developed method is validated by applying a real construction project and the obtained results are compared with the outcomes\u2019 of the company\u2019s traditional approach, clearly demonstrate the potential and the benefits of the proposed methodology. The result of the proposed method allows the decision-makers to operate with a lower contingency amount and control extra expenses of projects. In addition, a Decision Support System (DSS) approach using Failure Mode Effect Analysis and Monte Carlo Simulation has been discussed in this chapter. Besides, in the humanitarian field, A System Dynamic (SD) model has been applied to a humanitarian project to study the impact of different levels of financial aid paid to beneficiaries for different impact factors and estimate financial aid variation. Natural and man-made disasters seem unpredictable every year, increasing a wide range of universal sufferers. Several people are affected by the direct outcomes of these disasters, and their life depends on disaster relief aid administered by humanitarian organizations. Recently, there has been renewed interest in cash distribution in the humanitarian sector during disaster relief to increase access of vulnerable people to supporting services such as health or education and develop their life\u2019s condition while rising the efficiency of humanitarian organizations committed to the program. The research proposes a casual-loop and system dynamic model to assess multi aspects of related impact factors to provide optimal support of beneficiaries. The model provides a decision-making framework with a high-level overview of the interactions between the education and health aspects of the recipient\u2019s life, provides a system dynamics analysis including interactions that could have led to improving the vulnerable people's condition life. This system dynamics approach can be used to study the significant factors on education and health aspects of refugee crises such as the case of Syrian refugees in Turkey. Reviewing the humanitarian management literature, a causal loop is developed to better understand the health and education variables and their interactions. Then a system dynamic model is proposed and validated by historical data of Syrian refugees in Turkey. The result of financial aid sensitivity shows that more financial aid from humanitarian organizations are significantly improved the general health of refugees and also it is caused higher attendance for children in schools. In addition, enhanced financial aid supports can lead to improving access to water and hygiene facilities and also building more schools for their children

INTELLIGENT TECHNIQUES FOR HANDLING UNCERTAINTY IN THE ASSESSMENT OF NEONATAL OUTCOME

Objective assessment of the neonatal outcome of labour is important, but it is a difficult and challenging problem. It is an invaluable source of information which can be used to provide feedback to clinicians, to audit a unit's overall performance, and can guide subsequent neonatal care. Current methods are inadequate as they fail to distinguish damage that occurred during labour from damage that occurred before or after labour. Analysis of the chemical acid-base status of blood taken from the umbilical cord of an infant immediately after delivery provides information on any damage suffered by the infant due to lack of oxygen during labour. However, this process is complex and error prone, and requires expertise which is not always available on labour wards. A model of clinical expertise required for the accurate interpretation of umbilical acid-base status was developed, and encapsulated in a rule-based expert system. This expert system checks results to ensure their consistency, identifies whether the results come from arterial or venous vessels, and then produces an interpretation of their meaning. This 'crisp' expert system was validated, verified and commercially released, and has since been installed at twenty two hospitals all around the United Kingdom. The assessment of umbilical acid-base status is characterised by uncertainty in both the basic data and the knowledge required for its interpretation. Fuzzy logic provides a technique for representing both these forms of uncertainty in a single framework. A 'preliminary' fuzzy-logic based expert system to interpret error-free results was developed, based on the knowledge embedded in the crisp expert system. Its performance was compared against clinicians in a validation test, but initially its performance was found to be poor in comparison with the clinicians and inferior to the crisp expert system. An automatic tuning algorithm was developed to modify the behaviour of the fuzzy model utilised in the expert system. Sub-normal membership functions were used to weight terms in the fuzzy expert system in a novel manner. This resulted in an improvement in the performance of the fuzzy expert system to a level comparable to the clinicians, and superior to the crisp expert system. Experimental work was carried out to evaluate the imprecision in umbilical cord acid-base parameters. This information, in conjunction with fresh knowledge elicitation sessions, allowed the creation of a more comprehensive fuzzy expert system, to validate and interpret all acid-base data. This 'integrated' fuzzy expert system was tuned using the comparison data obtained previously, and incorporated vessel identification rules and interpretation rules, with numeric and linguistic outputs for each. The performance of each of the outputs was evaluated in a rigorous validation study. This demonstrated excellent agreement with the experts for the numeric outputs, and agreement on a par with the experts for the linguistic outputs. The numeric interpretation produced by the fuzzy expert system is a novel single dimensional measure that accurately represents the severity of acid-base results. The development of the crisp and fuzzy expert systems represents a major achievement and constitutes a significant contribution to the assessment of neonatal outcome.Plymouth Postgraduate Medical Schoo

Overview of Multi-Objective Optimization Approaches in Construction Project Management

The difficulties that are met in construction projects include budget issues, contractual time constraints, complying with sustainability rating systems, meeting local building codes, and achieving the desired quality level, to name but a few. Construction researchers have proposed and construction practitioners have used optimization strategies to meet various objectives over the years. They started out by optimizing one objective at a time (e.g., minimizing construction cost) while disregarding others. Because the objectives of construction projects often conflict with each other, single-objective optimization does not offer practical solutions as optimizing one objective would often adversely affect the other objectives that are not being optimized. They then experimented with multi-objective optimization. The many multi-objective optimization approaches that they used have their own advantages and drawbacks when used in some scenarios with different sets of objectives. In this chapter, a review is presented of 16 multi-objective optimization approaches used in 55 research studies performed in the construction industry and that were published in the period 2012–2016. The discussion highlights the strengths and weaknesses of these approaches when used in different scenarios

Supply Chain

Traditionally supply chain management has meant factories, assembly lines, warehouses, transportation vehicles, and time sheets. Modern supply chain management is a highly complex, multidimensional problem set with virtually endless number of variables for optimization. An Internet enabled supply chain may have just-in-time delivery, precise inventory visibility, and up-to-the-minute distribution-tracking capabilities. Technology advances have enabled supply chains to become strategic weapons that can help avoid disasters, lower costs, and make money. From internal enterprise processes to external business transactions with suppliers, transporters, channels and end-users marks the wide range of challenges researchers have to handle. The aim of this book is at revealing and illustrating this diversity in terms of scientific and theoretical fundamentals, prevailing concepts as well as current practical applications

On the duration and cost variability of construction activities: an empirical study

The unique nature of construction projects can mean that construction activities often suffer from duration and cost variability. As this variability is unplanned it can present a problem when attempting to complete a project on time and on budget. Various factors causing this variability have been identified in the literature, but they predominantly refer to the nature and/or context of the whole project, rather than their specific activities. In this paper, the order of magnitude of and correlation between activity duration and cost variability is analyzed in 101 construction projects with over 5000 activities. To do this, the first four moments (mean, standard deviation, skewness and kurtosis) of actual versus planned duration and cost (log) ratios are analyzed by project, phase of execution and activity type. Results suggest that, contrary to common wisdom, construction activities do not end late on average. Instead, the large variability in the activity duration is the major factor causing significant project delays and cost overruns. The values of average activity duration and cost variability gathered in this study will also serve as a reference for construction managers to improve future construction planning and project simulation studies with more realistic data

Mitigating Space Industry Supply Chain Risk Thru Risk-Based Analysis

Using risk-based analysis to consider supply chain disruptions and uncertainty along with potential mitigation strategies in the early stages of space industry projects can be used avoid schedule delays, cost overruns, and lead to successful project outcomes. Space industry projects, especially launch vehicles, are complicated assemblies of high-technology and specialized components. Components are engineered, procured, manufactured, and assembled for specific missions or projects, unlike make-to-stock manufacturing where assemblies are produced at a mass production rate for customers to choose off the shelf or lot, like automobiles. The supply chain for a space industry project is a large, complicated web where one disruption, especially for sole-sourced components, could ripple through the project causing delays at multiple project milestones. This ripple effect can even cause the delay or cancelation of the entire project unless project managers develop and employ risk mitigations strategies against supply chain disruption and uncertainty. The unpredictability of when delays and disruptions may occur makes managing these projects extremely difficult. By using risk-based analysis, project managers can better plan for and mitigate supply chain risk and uncertainty for space industry projects to better manage project success. Space industry project supply chain risk and uncertainty can be evaluated through risk assessments at major project milestones and during the procurement process. Mitigations for identified risks can be evaluated and implemented to better manage project success. One mitigation strategy to supply chain risk and uncertainty is implementing a dual or multi-supplier sourcing procurement strategy. This research explores using a risk-based analysis to identify where this mitigation strategy can be beneficial for space industry projects and how its implementation affects project success. First a supply chain risk assessment and mitigation decision tool will be used at major project milestones to show where a multi-sourcing strategy may be beneficial. Next, updated supplier quote evaluation tools will confirm the usage of multiple suppliers for procurement. Modeling and simulation are then used to show the impact of that strategy on the project success metrics of cost and schedule