Characterization of gradient estimators for stochastic activity networks

This thesis aims to characterize the statistical properties of Monte Carlo simulation-based gradient estimation techniques for performance measures in stochastic activity networks (SANs) using the estimators' variance as the comparison criterion. When analyzing SANs, both performance measures and their sensitivities (gradient, Hessian) are important. This thesis focuses on analyzing three direct gradient estimation techniques: infinitesimal perturbation analysis, the score function or likelihood ratio method, and weak derivatives. To investigate how statistical properties of the different gradient estimation techniques depend on characteristics of the SAN, we carry out both theoretical analyses and numerical experiments. The objective of these studies is to provide guidelines for selecting which technique to use for particular classes of SANs based on features such as complexity, size, shape and interconnectivity. The results reveal that a specific weak derivatives-based method with common random numbers outperforms the other direct techniques in nearly every network configuration tested

Assessing the Impact of the Lead/Lag Times on the Project Duration Estimates in Highway Construction

The literature mentions multiple factors that can affect the accuracy of estimating the project duration in highway construction, such as weather, location, and soil conditions. However, there are other factors that have not been explored, yet they can have significant impact on the accuracy of the project time estimate. Recently, TxDOT raised a concern regarding the importance of the proper estimating of the lead/lag times in project schedules. These lead/lag times are often determined based on the engineer’s experience. However, inaccurate estimates of the lead/lag time can result in unrealistic project durations. In order to investigate this claim, the study utilizes four time sensitivity measures (TSM), namely the Criticality Index (CI), Significance Index (SI), Cruciality Index (CRI), and the Schedule Sensitivity Index (SSI) to statistically analyze and draw conclusions regarding the impact of the lead/lag time estimates on the total duration in highway projects. An Excel-based scheduling software was developed with Monte Carlo simulation capabilities to calculate these TSM. The results from this paper show that the variability of some lead/lag times can significantly impact the accuracy of the estimated total project duration. It was concluded that the current practices used for estimating the lead/lag times are insufficient. As such, it is recommended to utilize more robust methods, such as the time sensitivity measures, to accurately estimate the lead/lad times in the projects scheduled

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

Projektin seuranta teollisuuden mekaanisissa asennuksissa

The objective of this work is to provide comprehensive understanding of Earned Value Management methodology in order to formulate a rubric for proper implementation of the methodology and to develop a tool for monitoring and forecasting costs for Caverion Industria Project Services. A comprehensive understanding of EVM methodology and state-of-the-art is founded by a careful review of related literature. Second, literature is being reviewed to provide assistance in implementing and practicing EVM. Third, the specific needs are confirmed by reviewing related company guidelines and assessment of current practices. The results are twofold. First, a rubric for proper and simple EVM implementation has been established on the basis of literary research and assessment of current practice. The management approach is characterized, supporting analysis techniques described, and guidelines for managing with earned value provided. Second, an EVM Tool has been developed upon specific company needs and two methodologies that have been validated with large historical datasets, simulation experiments and withstood the test of time. As the scope of this study did not include field-testing, it is suggested that the given processes shall be implemented and tested, and EVM Tool piloted for further feedback. Moreover, the tool shall be further developed to allow more detailed analysis of control accounts, consideration of scope changes without limitations and to enable efficient exploitation of MS Project and SAP interfaces. Similarly, implementation of Earned Duration Management, Schedule Adherence concept and assessment of forecasting quality in the EVM Tool as respectable extensions to add additional value in managing project costs shall be considered.Tämän työn tavoitteena on antaa Caverion Industrian Projektipalvelut -yksikölle kokonaisvaltainen käsitys Ansaittu arvo -menetelmästä ja laatia sen pohjalta ohjeistus menetelmän käyttöönottamiseksi asianmukaisella tavalla sekä kehittää työkalu kustannusten seurantaa ja ennustamista varten. Ensiksi kirjallisuustutkimuksen avulla pohjustetaan kattava ymmärrys menetelmästä ja viimeisimmistä tutkimuksista. Toiseksi kirjallisuutta tarkastellaan menetelmän käyttöönoton ja harjoittamisen näkökulmasta. Kolmanneksi erityiset tarpeet määritetään yrityksen ohjeistuksen ja nykyisten käytäntöjen pohjalta. Tulokset voidaan jakaa kahteen osaan. Ohjeistus asianmukaiseen mutta yksinkertaiseen käyttöönottoon on laadittu kirjallisuustutkimuksen ja nykyisten käytäntöjen arvioinnin pohjalta. Menetelmän johtamislähestymistapa luonnehditaan, tukevat analysointitekniikat kuvataan ja suosituksia Ansaittu arvo -menetelmällä johtamiseksi esitetään. Lisäksi Ansaittu arvo -työkalu on kehitetty yrityksen tarpeiden sekä kahden simulaatioilla ja laajalla empiirisellä aineistolla pätevöidyn teorian pohjalta. Koska testaus ei kuulunut tämän työn laajuuteen, on suositeltavaa, että prosessit otetaan käyttöön, testataan ja työkalu pilotoidaan palautteen saamiseksi. Lisäksi työkalua tulee kehittää siten, että se mahdollistaa koontitilien yksityiskohtaisen analysoinnin, työn laajuuden muutoksien huomioinnin ilman rajoituksia sekä MS Project ja SAP -rajapintojen tehokkaan hyödyntämisen. Samoin suositellaan, että Earned Duration Management -menetelmää, Schedule Adherence -konseptia ja ennusteiden laadun arviointia tulisi harkita varteenotettavina laajennuksina Ansaittu arvo -menetelmän rinnalle, millä voitaisiin tuottaa lisäarvoa projektien kustannusten hallitsemiseksi

SENSITIVITY ANALYSIS AND STOCHASTIC OPTIMIZATIONS IN STOCHASTIC ACTIVITY NETWORKS

Activity networks are a powerful tool for modeling and analysis in project management, and in many other applications, such as circuit design and parallel computing. An activity network can be represented by a directed acyclic graph with one source node and one sink node. The directed arcs between nodes in an activity network represent the precedence relationships between different activities in the project. In a stochastic activity network (SAN), the arc lengths are random variables. This dissertation studies stochastic gradient estimators for SANs using Monte Carlo simulation, and the application of stochastic gradient estimators to network optimization problems. A new algorithm called Threshold Arc Criticality (TAC) for estimating the arc criticalities of stochastic activity networks is proposed. TAC is based on the following result: given the length of all arcs in a SAN except for the one arc of interest, that arc is on the critical path (longest path) if and only if its length is greater than a threshold. By applying Infinitesimal Perturbation Analysis (IPA) to TAC, an unbiased estimator of the derivative of the arc criticalities with respect to parameters of arc length distributions can be derived. The stochastic derivative estimator can be used for sensitivity analysis of arc criticalities via simulation. Using TAC, a new IPA gradient estimator of the first and second moments of project completion time (PCT) is proposed. Combining the new PCT stochastic gradient estimator with a Taylor series approximation, a functional estimation procedure for estimating the change in PCT moments caused by a large perturbation in an activity duration's distribution parameter is proposed and applied to optimization problems involving time-cost tradeoffs. In activity networks, crashing an activity means reducing the activity's duration (deterministic or stochastic) by a given percentage with an associated cost. A crashing plan of a project aims to shorten the PCT by reducing the duration of a set of activities under a limited budget. A disruption is an event that occurs at an uncertain time. Examples of disruptions are natural disasters, electrical outages, labor strikes, etc. For an activity network, a disruption may cause delays in unfinished activities. Previous work formulates finding the optimal crashing plan of an activity network under a single disruption as a two-stage stochastic mixed integer programming problem and applies a sample average approximation technique for finding the optimal solution. In this thesis, a new stochastic gradient estimator is derived and a gradient-based simulation optimization algorithm is applied to the problem of optimizing crashing under disruption

Development of the Williams Work Estimator (W2E): A Tool for Determining the Most Effective Match between Worker Capabilities and Job Task Requirements

Demographics indicate that the United States and many other industrialized nations are currently experiencing what is called the ¡°graying¡± of the workforce (Hayslip & Panek, 1993). Today the majority of the workers in many companies are in the age groups of 40-44 and 45-49 years. However, by the year 2010, the largest proportion of workers will probably be in the age groups of 55-59 and 60-64 years (Ilmarinen, 1995). Thus, a growing concern of employers in the near future will be the assignment of older workers to specific job tasks and responsibilities (Williams & Crumpton, 1996) as well as other issues pertinent to the employment of older workers. As workers age they typically experience physiological and psychological changes which must be estimated to minimize the mismatch between their capabilities and job demands as well as to prevent work related injuries such as over exertion injuries. Early identification of declines in work ability and implementation of ergonomic interventions are key to sustaining older and more experienced workers in the workplace (Williams et al., 1996). If preventive measures are not taken, older employees are likely to experience a decline in work capacities (Ilmarinen, 1994). Therefore, reliable and valid measures of one¡¯s ability to perform physical work activities are essential for preventing work-related injuries. Hence, the focus of this research project is to develop a diagnostic tool that can be used by employers to estimate their workers¡¯ ability to perform daily work activities. Specifically, the Williams Work Estimator (W2E) is designed to provide information concerning workers¡¯ ability to perform physical work activities such as lifting, lowering, pushing, pulling, etc. A field research study involving 32 employees at a beer distribution warehousing facility was conducted to evaluate the following attributes of the W2E: (a) test-retest reliability, (b) concurrent criterion validity, and (c) predictive validity. Test-retest reliability of the W2E was assessed using Pearson correlation coefficients. The overall correlation coefficients obtained on both the task evaluation (.64) and the self-evaluation (.58) were near minimal acceptable levels (.60 or greater) for each job task evaluated. In addition, the W2E ranged from 50 to 100% accurate when identifying persons who had experienced a work-related injury within the past year. Findings of this research study suggest the W2E represents a promising new tool for assessing work capability and deserves further study to improve reliability and validity

How robust is your project? from local failures to global catastrophes:a complex networks approach to project systemic risk

Current societal requirements necessitate the effective delivery of complex projects that can do more while using less. Yet, recent large-scale project failures suggest that our ability to successfully deliver them is still at its infancy. Such failures can be seen to arise through various failure mechanisms; this work focuses on one such mechanism. Specifically, it examines the likelihood of a project sustaining a large-scale catastrophe, as triggered by single task failure and delivered via a cascading process. To do so, an analytical model was developed and tested on an empirical dataset by the means of numerical simulation. This paper makes three main contributions. First, it provides a methodology to identify the tasks most capable of impacting a project. In doing so, it is noted that a significant number of tasks induce no cascades, while a handful are capable of triggering surprisingly large ones. Secondly, it illustrates that crude task characteristics cannot aid in identifying them, highlighting the complexity of the underlying process and the utility of this approach. Thirdly, it draws parallels with systems encountered within the natural sciences by noting the emergence of self-organised criticality, commonly found within natural systems. These findings strengthen the need to account for structural intricacies of a project's underlying task precedence structure as they can provide the conditions upon which large-scale catastrophes materialise

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 159

This bibliography lists 257 reports, articles, and other documents introduced into the NASA scientific and technical information system in September 1976

Establishment of a novel predictive reliability assessment strategy for ship machinery

There is no doubt that recent years, maritime industry is moving forward to novel and sophisticated inspection and maintenance practices. Nowadays maintenance is encountered as an operational method, which can be employed both as a profit generating process and a cost reduction budget centre through an enhanced Operation and Maintenance (O&M) strategy. In the first place, a flexible framework to be applicable on complex system level of machinery can be introduced towards ship maintenance scheduling of systems, subsystems and components.;This holistic inspection and maintenance notion should be implemented by integrating different strategies, methodologies, technologies and tools, suitably selected by fulfilling the requirements of the selected ship systems. In this thesis, an innovative maintenance strategy for ship machinery is proposed, namely the Probabilistic Machinery Reliability Assessment (PMRA) strategy focusing towards the reliability and safety enhancement of main systems, subsystems and maintainable units and components.;In this respect, the combination of a data mining method (k-means), the manufacturer safety aspects, the dynamic state modelling (Markov Chains), the probabilistic predictive reliability assessment (Bayesian Belief Networks) and the qualitative decision making (Failure Modes and Effects Analysis) is employed encompassing the benefits of qualitative and quantitative reliability assessment. PMRA has been clearly demonstrated in two case studies applied on offshore platform oil and gas and selected ship machinery.;The results are used to identify the most unreliability systems, subsystems and components, while advising suitable practical inspection and maintenance activities. The proposed PMRA strategy is also tested in a flexible sensitivity analysis scheme.There is no doubt that recent years, maritime industry is moving forward to novel and sophisticated inspection and maintenance practices. Nowadays maintenance is encountered as an operational method, which can be employed both as a profit generating process and a cost reduction budget centre through an enhanced Operation and Maintenance (O&M) strategy. In the first place, a flexible framework to be applicable on complex system level of machinery can be introduced towards ship maintenance scheduling of systems, subsystems and components.;This holistic inspection and maintenance notion should be implemented by integrating different strategies, methodologies, technologies and tools, suitably selected by fulfilling the requirements of the selected ship systems. In this thesis, an innovative maintenance strategy for ship machinery is proposed, namely the Probabilistic Machinery Reliability Assessment (PMRA) strategy focusing towards the reliability and safety enhancement of main systems, subsystems and maintainable units and components.;In this respect, the combination of a data mining method (k-means), the manufacturer safety aspects, the dynamic state modelling (Markov Chains), the probabilistic predictive reliability assessment (Bayesian Belief Networks) and the qualitative decision making (Failure Modes and Effects Analysis) is employed encompassing the benefits of qualitative and quantitative reliability assessment. PMRA has been clearly demonstrated in two case studies applied on offshore platform oil and gas and selected ship machinery.;The results are used to identify the most unreliability systems, subsystems and components, while advising suitable practical inspection and maintenance activities. The proposed PMRA strategy is also tested in a flexible sensitivity analysis scheme