Framework for Enterprise Systems Engineering

This research aimed at developing a research framework for the emerging field of enterprise systems engineering (ESE). The framework consists of an ESE definition, an ESE classification scheme, and an ESE process. This study views an enterprise as a system that creates value for its customers. Thus, developing the framework made use of system theory and IDEF methodologies. This study defined ESE as an engineering discipline that develops and applies systems theory and engineering techniques to specification, analysis, design, and implementation of an enterprise for its life cycle. The proposed ESE classification scheme breaks down an enterprise system into four elements. They are work, resources, decision, and information. Each enterprise element is specified with four system facets: strategy, competency, capacity, and structure. Each element-facet combination is subject to the engineering process of specification, analysis, design, and implementation, to achieve its pre-specified performance with respect to cost, time, quality, and benefit to the enterprise. This framework is intended for identifying research voids in the ESE discipline. It also helps to apply engineering and systems tools to this emerging field. It harnesses the relationships among various enterprise aspects and bridges the gap between engineering and management practices in an enterprise. The proposed ESE process is generic. It consists of a hierarchy of engineering activities presented in an IDEF0 model. Each activity is defined with its input, output, constraints, and mechanisms. The output of an ESE effort can be a partial or whole enterprise system design for its physical, managerial, and/or informational layers. The proposed ESE process is applicable to a new enterprise system design or an engineering change in an existing system. The long-term goal of this study aims at development of a scientific foundation for ESE research and development.

A simulation-based decision support system for evaluating operating policies in an emergency room facility

Increased pressure to control costs and increased competition has prompted health care managers to look for tools to effectively operate their institutions. This research sought a framework for the development of a Simulation-Based Decision Support System (SB-DSS) to evaluate operating policies. A prototype of this SB-DSS was developed. It incorporates a simulation model that uses real or simulated data. ER decisions have been categorized and, for each one, an implementation plan has been devised. Several issues of integrating heterogeneous tools have been addressed. The prototype revealed that simulation can truly be used in this environment in a timely fashion because the simulation model has been complemented with a series of decision-making routines. These routines use a hierarchical approach to organize the various scenarios under which the model may run and to partially reconfigure the ARENA model at run time. Hence, the SB-DSS tailors its responses to each node in the hierarchy.

Design and implementation of a web based shop floor control system to maintain schedule feasibility in a dynamic job shop environment

The effective control of production activities in dynamic job shop with predetermined resource allocation for all the jobs entering the system is a unique manufacturing environment, which exists in the manufacturing industry. In this thesis a framework for an Internet based real time shop floor control system for such a dynamic job shop environment is introduced. The system aims to maintain the schedule feasibility of all the jobs entering the manufacturing system under any circumstance. The system is capable of deciding how often the manufacturing activities should be monitored to check for control decisions that need to be taken on the shop floor. The system will provide the decision maker real time notification to enable him to generate feasible alternate solutions in case a disturbance occurs on the shop floor. The control system is also capable of providing the customer with real time access to the status of the jobs on the shop floor. The communication between the controller, the user and the customer is through web based user friendly GUI. The proposed control system architecture and the interface for the communication system have been designed, developed and implemented.

A simulation-based heuristic for fleet assignment

Integer programming, simulation, and rules of thumb have been integrated to develop a simulation-based heuristic for short-term assignment of fleet in the car rental industry. It generates a plan for car movements, and a set of booking limits to produce high revenue for a given planning horizon. Three different scenarios were used to validate the heuristic. The heuristic's mean revenue was significant higher than the historical ones, in all three scenarios. Time to run the heuristic for each experiment was within the time limits of three hours set for the decision making process even though it is not fully automated. These findings demonstrated that the heuristic provides better plans (plans that yield higher profit) for the dynamic allocation of fleet than the historical decision processes. Another contribution of this effort is the integration of IP and rules of thumb to search for better performance under stochastic conditions.

A probabilistic approach to assess hydrate formation and design preventive measures

Formation of hydrates is one of the major flow assurance problems faced by the oil and gas industry. Hydrates tend to form in natural gas pipelines with the presence of water and favorable temperature and pressure conditions, generally low temperatures and corresponding high pressures. Agglomeration of hydrates can result in blockage of flowlines and equipment, which can be time consuming to remove in subsea equipment and cause safety issues. Natural gas pipelines are more susceptible to burst and explosion owing to hydrate plugging. Therefore, a rigorous risk-assessment related to hydrate formation is required, which assists in preventing hydrate blockage and ensuring equipment integrity. This thesis presents a novel methodology to assess the probability of hydrate formation and presents a risk-based approach to determine the parameters of winterization schemes to avoid hydrate formation in natural gas pipelines operating in Arctic conditions. It also presents a lab-scale multiphase flow loop to study the effects of geometric and hydrodynamic parameters on hydrate formation and discusses the effects of geometric and hydrodynamic parameters on multiphase development length of a pipeline. Therefore, this study substantially contributes to the assessment of probability of hydrate formation and the decision making process of winterization strategies to prevent hydrate formation in Arctic conditions.

An application of evolutionary algorithms for WAG optimisation in the Norne Field

Water-alternating-gas (WAG) is an enhanced oil recovery method combining the improved macroscopic sweep of water flooding with the improved microscopic displacement of gas injection. The optimal design of the WAG parameters is usually based on numerical reservoir simulation via trial and error, limited by the reservoir engineer’s availability. Employing optimisation techniques can guide the simulation runs and reduce the number of function evaluations. In this study, robust evolutionary algorithms are utilized to optimise hydrocarbon WAG performance in the E-segment of the Norne field. The first objective function is selected to be the net present value (NPV) and two global semi-random search strategies, a genetic algorithm (GA) and particle swarm optimisation (PSO) are tested on different case studies with different numbers of controlling variables which are sampled from the set of water and gas injection rates, bottom-hole pressures of the oil production wells, cycle ratio, cycle time, the composition of the injected hydrocarbon gas (miscible/immiscible WAG) and the total WAG period. In progressive experiments, the number of decision-making variables is increased, increasing the problem complexity while potentially improving the efficacy of the WAG process. The second objective function is selected to be the incremental recovery factor (IRF) within a fixed total WAG simulation time and it is optimised using the same optimisation algorithms. The results from the two optimisation techniques are analyzed and their performance, convergence speed and the quality of the optimal solutions found by the algorithms in multiple trials are compared for each experiment. The distinctions between the optimal WAG parameters resulting from NPV and oil recovery optimisation are also examined. This is the first known work optimising over this complete set of WAG variables. The first use of PSO to optimise a WAG project at the field scale is also illustrated. Compared to the reference cases, the best overall values of the objective functions found by GA and PSO were 13.8% and 14.2% higher, respectively, if NPV is optimised over all the above variables, and 14.2% and 16.2% higher, respectively, if IRF is optimised.

A framework for reliability assessment of ship hull damage under ship bow impact

Peer reviewed

A framework for reliability assessment of ship hull damage under ship bow impact

Peer reviewed

A framework for reliability assessment of ship hull damage under ship bow impact

Peer reviewed

The Timing of Motherhood While Earning a PhD in Engineering

In spite of tremendous efforts, women are still under-represented in the field of science. Post-graduate education and early tenure track employment are part of the academic career establish-ment in research and development during periods that usually overlap with family formation. Though women tend to leave science mainly after obtaining their PhD, and the timing of mother-hood plays a vital role in a successful research career, qualitative data on this life period are scarce. Our paper focuses on how the normative and institutional contexts shape female PhD engineering students’ family plans. The research was based on intersections of life course and risk and uncertainty theories. Using qualitative interviews we explored how contradicting social norms of childbearing cause tensions in postgraduate students’ lives, and how the different uncer-tainties and risks permeate young researchers’ decisions on early life events. We concluded that, despite the general pattern of delaying motherhood among higher educated women, these students struggle against this postponement, and they hardly have any good options to avoid risk stem-ming from uncertainties and from some characteris-tics of studying and working in engineering. Find-ings of this research may call the attention of stake-holders to possible intervention points.

Impact of financial crisis on earnings management in listed companies of Portugal and UK

Firm’s financial information is essential to stakeholders’ decision making. Although not always financial statements show the firm’s real image. This study examines listed firms from Portugal and UK. Firms have different purposes to manipulate earnings: some strive for influencing investors’ perception about a particular company, some try to provide better position for gaining finance from credit institutions or paying less tax to tax authorities. Usually, this behaviour is induced when firms have financial problems. Consequently, the study also aims to see the impact of financial crisis on earnings management. We try to answer question how does extent of firms’ involvement in earnings management change when the world undergoes financial crisis. Furthermore, we also compare two countries with different legal forces in terms of quality of accounting to see the main differences. We used a panel data methodology to analyse financial data from 2004 till 2014 of listed firms from Portugal and UK. Beneish (1999) model was applied to categorize manipulator and non-manipulator firms. Analysing accounting information according to Beneish’s ratios, findings suggest that financial crisis had certain impact on firms’ tendency to manipulate financial results in UK although it is not statistically significant. Moreover, besides the differences between Portugal and UK, results contradict the common view of legal systems’ quality, as UK firms tend to apply more accounting techniques for manipulation than the Portuguese ones. Our main results also confirm that some UK firms manipulate ratios of receivables’ days, asset quality index, depreciation index, leverage, sales and general administrative expenses whereas Portuguese firms manipulate only receivables’ days. Finally, we also find that the main reason to manipulate results is not to influence the cost of obtained funds neither to minimize tax burden since net profit does not explain the ratios used in the Beneish model. Results suggest that the main concern to listed firms manipulate results is to influence financial investors perception.

Managing supply chain knowledge-based linkages for improving operational performance

Purpose: This paper aims to explore the role of internal and external knowledgebased linkages across the supply chain in achieving better operational performance. It investigates how knowledge is accumulated, shared, and applied to create organization-specific knowledge resources that increase and sustain the organization's competitive advantage. Design/methodology/approach: This paper uses a single case study with multiple, embedded units of analysis, and the social network analysis (SNA) to demonstrate the impact of internal and external knowledge-based linkages across multiple tiers in the supply chain on the organizational operational performance. The focal company of the case study is an Italian manufacturer supplying rubber components to European automotive enterprises. Findings: With the aid of the SNA, the internal knowledge-based linkages can be mapped and visualized. We found that the most central nodes having the most connections with other nodes in the linkages are the most crucial members in terms of knowledge exploration and exploitation within the organization. We also revealed that the effective management of external knowledge-based linkages, such as buyer company, competitors, university, suppliers, and subcontractors, can help improve the operational performance. Research limitations/implications: First, our hypothesis was tested on a single case. The analysis of multiple case studies using SNA would provide a deeper understanding of the relationship between the knowledge-based linkages at all levels of the supply chain and the integration of knowledge. Second, the static nature of knowledge flows was studied in this research. Future research could also consider ongoing monitoring of dynamic linkages and the dynamic characteristic of knowledge flows. Originality/value: To the best of our knowledge, the phrase 'knowledge-based linkages' has not been used in the literature and there is lack of investigation on the relationship between the management of internal and external knowledge-based linkages and the operational performance. To bridge the knowledge gap, this paper will show the importance of understanding the composition and characteristics of knowledge-based linkages and their knowledge nodes. In addition, this paper will show that effective management of knowledge-based linkages leads to the creation of new knowledge and improves organizations' operational performance.

Improved image analysis based corrosion assessment using preprocessing techniques

The importance of non-destructive techniques (NDT) in structural health monitoring programmes is being critically felt in the recent times. The quality of the measured data, often affected by various environmental conditions can be a guiding factor in terms usefulness and prediction efficiencies of the various detection and monitoring methods used in this regard. Often, a preprocessing of the acquired data in relation to the affecting environmental parameters can improve the information quality and lead towards a significantly more efficient and correct prediction process. The improvement can be directly related to the final decision making policy about a structure or a network of structures and is compatible with general probabilistic frameworks of such assessment and decision making programmes. This paper considers a preprocessing technique employed for an image analysis based structural health monitoring methodology to identify sub-marine pitting corrosion in the presence of variable luminosity, contrast and noise affecting the quality of images. A preprocessing of the gray-level threshold of the various images is observed to bring about a significant improvement in terms of damage detection as compared to an automatically computed gray-level threshold. The case dependent adjustments of the threshold enable to obtain the best possible information from an existing image. The corresponding improvements are observed in a qualitative manner in the present study.

Adaptive Brain-Computer Interface Systems For Communication in People with Severe Neuromuscular Disabilities

Brain-computer interfaces (BCI) have the potential to restore communication or control abilities in individuals with severe neuromuscular limitations, such as those with amyotrophic lateral sclerosis (ALS). The role of a BCI is to extract and decode relevant information that conveys a user's intent directly from brain electro-physiological signals and translate this information into executable commands to control external devices. However, the BCI decision-making process is error-prone due to noisy electro-physiological data, representing the classic problem of efficiently transmitting and receiving information via a noisy communication channel.

This research focuses on P300-based BCIs which rely predominantly on event-related potentials (ERP) that are elicited as a function of a user's uncertainty regarding stimulus events, in either an acoustic or a visual oddball recognition task. The P300-based BCI system enables users to communicate messages from a set of choices by selecting a target character or icon that conveys a desired intent or action. P300-based BCIs have been widely researched as a communication alternative, especially in individuals with ALS who represent a target BCI user population. For the P300-based BCI, repeated data measurements are required to enhance the low signal-to-noise ratio of the elicited ERPs embedded in electroencephalography (EEG) data, in order to improve the accuracy of the target character estimation process. As a result, BCIs have relatively slower speeds when compared to other commercial assistive communication devices, and this limits BCI adoption by their target user population. The goal of this research is to develop algorithms that take into account the physical limitations of the target BCI population to improve the efficiency of ERP-based spellers for real-world communication.

In this work, it is hypothesised that building adaptive capabilities into the BCI framework can potentially give the BCI system the flexibility to improve performance by adjusting system parameters in response to changing user inputs. The research in this work addresses three potential areas for improvement within the P300 speller framework: information optimisation, target character estimation and error correction. The visual interface and its operation control the method by which the ERPs are elicited through the presentation of stimulus events. The parameters of the stimulus presentation paradigm can be modified to modulate and enhance the elicited ERPs. A new stimulus presentation paradigm is developed in order to maximise the information content that is presented to the user by tuning stimulus paradigm parameters to positively affect performance. Internally, the BCI system determines the amount of data to collect and the method by which these data are processed to estimate the user's target character. Algorithms that exploit language information are developed to enhance the target character estimation process and to correct erroneous BCI selections. In addition, a new model-based method to predict BCI performance is developed, an approach which is independent of stimulus presentation paradigm and accounts for dynamic data collection. The studies presented in this work provide evidence that the proposed methods for incorporating adaptive strategies in the three areas have the potential to significantly improve BCI communication rates, and the proposed method for predicting BCI performance provides a reliable means to pre-assess BCI performance without extensive online testing.

A Molecular-scale Programmable Stochastic Process Based On Resonance Energy Transfer Networks: Modeling And Applications

While molecular and cellular processes are often modeled as stochastic processes, such as Brownian motion, chemical reaction networks and gene regulatory networks, there are few attempts to program a molecular-scale process to physically implement stochastic processes. DNA has been used as a substrate for programming molecular interactions, but its applications are restricted to deterministic functions and unfavorable properties such as slow processing, thermal annealing, aqueous solvents and difficult readout limit them to proof-of-concept purposes. To date, whether there exists a molecular process that can be programmed to implement stochastic processes for practical applications remains unknown.

In this dissertation, a fully specified Resonance Energy Transfer (RET) network between chromophores is accurately fabricated via DNA self-assembly, and the exciton dynamics in the RET network physically implement a stochastic process, specifically a continuous-time Markov chain (CTMC), which has a direct mapping to the physical geometry of the chromophore network. Excited by a light source, a RET network generates random samples in the temporal domain in the form of fluorescence photons which can be detected by a photon detector. The intrinsic sampling distribution of a RET network is derived as a phase-type distribution configured by its CTMC model. The conclusion is that the exciton dynamics in a RET network implement a general and important class of stochastic processes that can be directly and accurately programmed and used for practical applications of photonics and optoelectronics. Different approaches to using RET networks exist with vast potential applications. As an entropy source that can directly generate samples from virtually arbitrary distributions, RET networks can benefit applications that rely on generating random samples such as 1) fluorescent taggants and 2) stochastic computing.

By using RET networks between chromophores to implement fluorescent taggants with temporally coded signatures, the taggant design is not constrained by resolvable dyes and has a significantly larger coding capacity than spectrally or lifetime coded fluorescent taggants. Meanwhile, the taggant detection process becomes highly efficient, and the Maximum Likelihood Estimation (MLE) based taggant identification guarantees high accuracy even with only a few hundred detected photons.

Meanwhile, RET-based sampling units (RSU) can be constructed to accelerate probabilistic algorithms for wide applications in machine learning and data analytics. Because probabilistic algorithms often rely on iteratively sampling from parameterized distributions, they can be inefficient in practice on the deterministic hardware traditional computers use, especially for high-dimensional and complex problems. As an efficient universal sampling unit, the proposed RSU can be integrated into a processor / GPU as specialized functional units or organized as a discrete accelerator to bring substantial speedups and power savings.