Browsing by Author "Li, Simon"
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- ItemOpen AccessApplication of Fuzzy Numbers for Environmental Assessments: A Preliminary Study(2016) Cheng, Xin; Li, Simon; Du, Ke; Xue, Deyi; Zareipour, HamidrezaThis thesis preliminarily explores the use of fuzzy numbers in two applications: life-cycle assessment (LCA) and building energy analysis. In LCA, this thesis compares the results in the context of concept selection between the fuzzy number and Monte Carlo approaches and finds that the numerical outcomes of both approaches are comparable. In building energy analysis, the traditional degree-day method is adapted by allowing the data inputs as fuzzy numbers. Instead of providing the “average point” results, the fuzzy number approach can yield an “interval” of energy estimations. The proposed fuzzy number approach has been applied to a building located in Calgary, and the results are compared to historical data and building energy simulation (i.e., eQUEST). As the ranking of fuzzy numbers is fundamental to support decision making in both applications, this thesis also investigates the axiomatic properties of one well-known ranking method, namely, the centroid index method. This thesis has explored using a numerical approach to identify counter-examples in the proof process.
- ItemOpen AccessBeyond Service Learning: Towards an Understanding of Engineering Student Development of Social Responsibility(2018-09-26) Jatana, Lauren Shawna; Eggermont, Marjan J.; Brennan, Robert William; Li, Simon; Nygren, AndersMore than ever, engineering students must be prepared for social responsibility. The 20th-century saw unprecedented social and technical advancement, leading to population growth and depleting natural resources. As a result, engineering educators have been called upon to prepare students for these professional social responsibilities. However, social responsibility is more discretionary and interpretive than legal and ethical responsibilities, and educators lack clarity on what exactly social responsibility entails and how to design effective learning experiences for it. This thesis explores the relationship between student participation in various engineering learning experiences and social responsibility. Results of this study show that fourth- year students were likely to have higher social responsibility measures than first-year students, especially in a professional capacity. Participation in certain learning experiences corresponded to increased social responsibility measures, while others did not. Lastly, the results show that social responsibility measures could be correlated with three professional graduate attributes.
- ItemOpen AccessChemical Characteristics of Ambient Fine Particles in Calgary(2016) Yu, Kuangyou; Du, Ke; Mohamad, Majeed; Li, Simon; Norman, Ann-LiseAirborne fine particles (PM2.5) play a major role in air quality. This study focuses on the chemical composition of PM2.5 and its seasonal variation in 2015, based on an 8-month observation campaign in Calgary. Elemental carbon, organic carbon, and nine kinds of water-soluble ions were analyzed in this study. PM2.5 samples were collected by URG-3000ABC air sampler and were analyzed by DRI Model 2001A Thermal/Optical Carbon Analyzer and ICS-2500 ion chromatography. The concentration of PM2.5 has demonstrated a significant seasonal variation with an elevated concentration in summer (13±6μg/m3) and low concentration in winter (9±3μg/m3), which is opposite to most previous studies. Most of the water-soluble inorganic ion components have shown opposite variations. Organics were the main chemical components accounting for 59% of PM2.5. Secondary organic carbon accounted for more than 65% of the total organic carbon. Therefore, the seasonal variation of PM2.5 followed the pattern of SOC.
- ItemOpen AccessConcept and Methods for the Development of Blockchain based Cloud Manufacturing(2023-04-30) Wang, Binni; Tu, Paul; Li, Simon; Xue, Deyi; Thekinen, Joseph; Patterson, Raymond; Zeng, YongCloud manufacturing is seen as a promising improvement for networked manufacturing, with potential for achieving higher service quality at a lower cost, although the progress of its application has not met expectations. The main obstacle hindering its adoption is the reluctance of potential users to share data with cloud manufacturing due to concerns about data safety issues caused by central management. To address this drawback, the integration of a decentralized blockchain system has been proposed as a promising solution. However, the traceable data and provable system features of blockchain technology cannot guarantee data security, particularly for small or moving objects that lack the ability for data verification. Therefore, blockchain-based cloud manufacturing systems must have the ability to ensure data reliability. Moreover, the scalability problem must be addressed for the adoption of a blockchain-based system. This PhD thesis proposes a feasible architecture for a blockchain-based cloud manufacturing system and evaluates the reliability of perceived data through data similarity measurement. Firstly, the architecture of the system is outlined, with the use of rollup technology to address scalability issues. Second, the proper fee setting for the system is analyzed. For data similarity measurement, comparable data is selected using a modified clustering algorithm, and the data is described using polygon-based descriptions, including existing and proposed polygon descriptions. Finally, data similarity measurement is transformed into a similarity comparison of polygon descriptions, with methods such as multi-objective programming-based similarity comparison and overlap area-based similarity comparison applied for this purpose. The feasibility of the proposed methods is verified through a case study.
- ItemOpen AccessData-driven and Model-based Bearing Fault Analysis - Wind Turbine Application(2017) Mollasalehi, Ehsan; Wood, David; Sun, Qiao; Egberts, Philip; Mechefske, Chris; Li, Simon; Korobenko, Artem; Knight, AndyThis thesis is primarily about fault analysis for wind turbine generator bearings including vibration data analysis, and modeling of bearing outer race faults to understand how the vibration signal develops as the faults progresses. Case studies from large wind turbines are described where amplitude and phase demodulation are used to identify the outer race fault on the generator bearing. In many applications of roller bearing fault diagnosis, such as in wind turbines, shaft rotational speed varies with respect to time, and consequently the normal frequency domain analysis, which is valid for constant speed condition, cannot be used. Alternatively, order tracking in conjunction with phase demodulation has been used to resample the time domain signal into angular domain, and estimate the shaft speed, respectively. Speed estimation using phase demodulation has been mainly used for gearbox applications where a reference signal or a shaft harmonic is well defined, which is not the case for bearings. In this thesis, Empirical Mode Decomposition (EMD) was used for decomposing the vibration signals, and shaft speed was estimated by phase demodulation. The estimated instantaneous shaft speed was then used for order tracking to capture the bearing fault, under variable speed profile. The method was applied on a simulated bearing signal having an outer race fault, and then on operational vibration data of a 1.5 MW wind turbine which had a low signal-to-noise ratio. In order to de-noise the vibration signals and select the most suitable decomposed mode for further analysis, an EMD-based de-noising algorithm including an indicator (combination of Kurtosis factor and Root Mean Square value) was proposed to eliminate the effect of unwanted components from other drive-train rotating elements as well as noise. The idea of indicative fault diagnosis scheme based on the wind turbine tower vibration was demonstrated. It had been reported that a major fault on the generator bearing causes shock and noise to be heard from the bottom of the wind turbine tower. Thus, two accelerometers were attached orthogonally inside of the wind turbine tower. Tower vibration signals were analyzed using EMD and the outcomes were correlated with the vibration signal acquired directly from the generator bearings. It was shown that the generator bearing fault signatures are present in the vibration from the tower. The results suggest that useful condition monitoring of nacelle components, such as generator bearings can be done even when there is no condition monitoring system installed on the generator bearings, as is often the case for older wind turbines. A dynamic model of a bearing with an outer race fault was developed including load zone, contact force and traction/friction forces. The model was validated both analytically, and using the generator bearing vibration data. Faults were modeled as simple surface step-like profile changes (to simulate small cracks), and as random sinusoidal (to simulate distributed defects). Different fault sizes were then simulated, and their effects on the vibration signal were analyzed. The results were compared with the historical generator bearing vibration data. This model can be ultimately be used to track the status of the fault size.
- ItemOpen AccessData-driven approach for emission monitoring and management(2023-02-03) Si, Minxing; Du, Ke; Li, Simon; Mohamad, AbdulmajeedData-driven approach for emission monitoring and management is the process of making decisions that are informed by collecting, processing, and analyzing data. In this research, we developed and assessed predictive models using six algorithms, including linear regression, lasso regression, ridge regression, adaptive boosting, gradient boosting, and artificial neural networks (ANN), to monitor NOx emissions from point sources. The long-term evaluation showed that the moderate complexity algorithm, adaptive boosting, had the best long-term monitoring performance with a root mean square error (RMSE) of 0.48 kg/hr. The two algorithms with the high-complexity developed by gradient boosting and ANN algorithms had the worst RMSE score, 0.51 kg/hr and 0.57 kg/hr, during the long-term monitoring period. Additionally, we used machine learning methods to calibrate a low-cost fine particulate matter (PM) sensor. After calibration by gradient boosting and ANN, the variances of the PM2.5 values were not statistically significantly different from the variance of the PM2.5 values measured by the reference method. The ANN method generated the lowest RMSE of 3.91 in the test dataset with 610 samples. Moreover, we applied data-driven discovery to an oil and gas database. The use of clustering and association rules implied that: (1) the cyclic steam stimulation (CSS) recovery method was less efficient than Steam-Assisted Gravity Drainage (SAGD) recovery as schemes proceed toward maturity; (2) gas co-injection resulted in low Steam Oil Ratio (SOR)s; and (3) the Cold Lake region had higher solution gas oil ratio compared to the two other regions, including Athabasca and Peace River. Finally, data-driven approaches were used to analyze GHG emissions from in-situ oil sands operations. The weighted averages of the fuel use for the schemes using SAGD and CSS were 0.20 103m3 fuel to produce 1 m3 bitumen (0.24 103m3/m3) and 0.34 103m3 fuel to produce 1 m3 bitumen (0.34 103m3/m3), respectively. The average emission intensities (EIs) for SAGD ranged from 0.25 t CO2e/m3 to 0.98 t CO2e/m3, and the average EIs for CSS ranged from 0.61 t CO2e/m3 to 1.18 t CO2e/m3.
- ItemOpen AccessDecentralized Scheduling Using The Multi-Agent System Approach For Smart Manufacturing Systems: Investigation And Design(2023-09-20) Ebufegha, Akposeiyifa Joseph; Li, Simon; Brennan, Robert; Lee, JihyunThe advent of industry 4.0 has resulted in increased availability, velocity, and volume of data as well as increased data processing capabilities. There is a need to determine how best to incorporate these advancements to improve the performance of manufacturing systems. The purpose of this research is to present a solution for incorporating industry 4.0 into manufacturing systems. It will focus on how such a system would operate, how to select resources for the system, and how to configure the system. Our proposed solution is a smart manufacturing system that operates as a self-coordinating system. It utilizes a multi-agent system (MAS) approach, where individual entities within the system have autonomy to make dynamic scheduling decisions in real-time. This solution was shown to outperform alternative scheduling strategies (right shifting and dispatching priority rule) in manufacturing environments subject to uncertainty in our simulation experiments. The second phase of our research focused on system design. This phase involved developing models for two problems: (1) resource selection, and (2) layout configuration. Both models developed use simulation-based optimization. We first present a model for determining machine resources using a genetic algorithm (GA). This model yielded results comparable to an exhaustive search whilst significantly reducing the number of required experiments to find the solution. To address layout configuration, we developed a model that combines hierarchical clustering and GA. Our numerical experiments demonstrated that the hybrid layouts derived from the model result in shorter and less variable order completion times compared to alternative layout configurations. Overall, our research showed that MAS-based scheduling can outperform alternative dynamic scheduling approaches in manufacturing environments subject to uncertainty. We also show that this performance can further be improved through optimal resource selection and layout design.
- ItemOpen AccessDesign Project Scheduling with Probabilistic Iterations: Optimization & Investigation of Robustness(2018-09-05) Ebufegha, Akposeiyifa Joseph; Li, Simon; Tu, Paul; Xue, Deyi; Sadeghpour, FarnazThis research focuses on reliable prediction of product development (PD) project duration within reasonable solution times. It is based on the model of PD projects presented by Smith and Eppinger which suggests that PD projects are akin to Markov reward chains. The research was divided into two phases; the development of a hybrid algorithm to minimize the problem’s solve time without compromising solution quality and the identification of features of a robust PD schedule. The first phase of the research shows that the hybrid algorithm yields similar quality solutions whilst reducing solve times by 50% - 97.4%. The second phase highlights four features that affect robust PD schedule as well as patterns observed in the schedule. In conclusion, this research demonstrates the utility of the developed hybrid algorithm and also illustrates the importance of examining expected project duration and the standard deviation in the results when developing robust PD schedules.
- ItemOpen AccessDesign, Development and Testing of a Prototype Extendable Blade Wind Turbine(2017) Cheung, Hiu Fung; Pieper, Jeff; Wood, David; Goldsmith, Peter; Li, Simon; Nowicki, EdwinThe wind energy industry has grown fast over the past decades. One critical part in maximizing and regulating energy production in any wind turbine is the control system. Typical wind turbines perform variable speed and variable pitch control strategy, but the concept of using an extendable blade wind turbine as an alternative actuation is not fully explored. This thesis will explore the design and development of an extendable blade wind turbine along with experimental wind tunnel testing of the prototype operating in a power regulation region. Extendable blade wind turbines add a different degree of freedom to the control strategy. Through the use of a blade extending mechanism, swept area of turbine blades could be adjusted to vary energy capture and mechanical loading on the system. The turbine blades were designed with Blade Element Momentum Theory and they were constructed with 3D printed plastic and a retractable mechanism. A direct current gear motor in the turbine hub is used to control the blade extending and retracting mechanism in a feedback manner wirelessly. A laptop, micro-controllers and sensors are used for measuring the generated power, rotational speed and the blade position. The measured power is fed back to the system and control signals are sent to the motor which extends and retracts the turbine blades. Initial testing showed that the effect of varying blade length has a significant impact on generated power. Closed loop feedback control shows effective power regulation and disturbance rejection of wind speed variations near a selected rated power level for the machine. This regulation is achieved through extending and retracting the blade only based on feedback power measurements. Variations of proportional-integral-derivative controllers were used to track a constant reference under those wind conditions and the result of different controller gains were analyzed using one-way Analysis of Variance. The integral gain was found to be statistically more sensitive as compared to the proportional and differential gains. Thus a proportional-integral control is recommended for operation of this type of system.
- ItemOpen AccessDevelopment of a Scalable Self-Regulation Intervention in Support of Lifelong Learning for First-year Engineering Students(2019-09-18) Sullivan, Monique; Brennan, Robert William; Sun, Qiao; Rosehart, William Daniel; Li, Simon; Weinhardt, Justin M.Lifelong learning is a Canadian Engineering Accreditation Board (CEAB) graduate attribute and thus a fundamental component of engineering education, and yet, unlike many core engineering concepts such as thermodynamics or heat transfer, there is no agreed upon approach to teach it to students. This work narrows in on the self-regulation component of lifelong learning and draws on research from the learning sciences, motivation theory, and psychology to develop a scalable intervention for first-year engineering students. Several research-based instructional strategies were modified into four workshops and sets of reminders, each with an emphasis on higher purpose, learning strategies, metacognition, or growth mindset. They were offered to first-year engineering students during the winter term and impact on student performance and beliefs were measured. This thesis presents the details of the workshops, results from their first implementations, two frameworks to evaluate the learning intervention, and suggestions for future work. While this thesis cannot provide conclusive results, it does provide evidence and support that self-regulation can and should be explicitly taught to engineering students.
- ItemOpen AccessDevelopment of Adaptable Products Based on Modular Design and Optimization Methods(2016) Martinez Barragan, Maribel; Xue, Deyi; Ramirez-Serrano, Alejandro; Li, Simon; Wang, XinAn adaptable product is a product that can be reconfigured or upgraded to satisfy different requirements. Among various advanced design methods, modular design approach is employed in this research for the design of adaptable products. A module in a product is a group of components that can be disassembled non-destructively from the product as a unit. In the traditional modular design approach, components of a product are grouped into modules based on similarity among their functions and/or manufacturing processes. This traditional approach does not consider that the product may have to be modified or upgraded due to a change in the requirements during the operational stage of the product. This results in a problem since the part that needs to be modified to satisfy the new requirement cannot be solely replaced and the entire module where it belongs to has to be replaced. The objective of this research is to improve the adaptable design method by developing a module design approach considering the different life-cycle properties of the components in the adaptable product. In addition, optimization is used to identify the optimal design of adaptable product. In this research, the product description in different life-cycle phases is modeled by different configurations, and each of these configurations is described by a set of parameters. The product components with similar life-cycle properties such as maintenance frequency, life-span, degradation of performance, etc. are grouped into modules. A hybrid AND-OR tree is used to model all feasible design solutions considering different configurations with their corresponding parameters at different life-cycle phases. The adaptable product at a certain life-cycle time point is evaluated by a number of evaluation measures which can have different measurement units. The evaluation measures in different units are converted into comparable evaluation indices. The overall evaluation index for an adaptable product is defined by individual evaluation indices and their importance weighting factors considering the whole product life-cycle span. A multi-level optimization method is employed to identify the best design solution, its configurations in different life-cycle phases and parameter values of the relevant configurations. A case study is implemented to demonstrate the effectiveness of the developed new adaptable design approach.
- ItemOpen AccessDevelopment of Nanocomposite Sensors for Smart Work holding System(2022-01) Sandwell, Allen; Park, Simon; Hugo, Ron; Li, Simon; Xue, DeyiThe advent of Industry 4.0 necessitates developments of new sensor technologies. One of the emerging techniques of fabricating these new types of electronics and sensors is by depositing conductive inks and additional electrically active materials to serve as sensor layers onto flexible polymeric substrates. This study presents copper nanoparticle-based inks. The new formulation is developed that provides both oxidation resistance as well as improved flexibility with suitable rheological properties for deposition using traditional screen-printing practices. To create a conductive matrix from the deposited ink, a sintering step is needed. Intense pulsed light (IPL) using a xenon flash lamp is used to sinter the ink. As the IPL sintering process is applied to the surface of the ink, the sintering process and resulting materials are analysed. The addition of polymeric nanocomposite-based films has significant benefits when used as the sensing layer. In this study we developed a sensing material comprised of (poly)vinylidene fluoride as a piezoelectric polymer matrix, along with carbon nanotubes to create an electrically conductive network. Exhibiting both piezoelectric and piezoresistive properties, the developed sensors are capable of measurement in a wide frequency band. The performance of these nanocomposite sensors was assessed as they are subject to an applied strain under both static and dynamic conditions. As an application for the printed sensors, we investigate their suitability as imbedded sensor systems for smart workholding. We present the design of a smart chuck (the SmartJaw) that can measure the gripping forces on a workpiece during machining operations on a lathe. Continuous monitoring of the jaw clamping forces provides the required feedback to minimize the likelihood of adverse events occurring. Analytical models are developed and presented that reconstruct the cutting forces based on the input jaw gripping forces and other parameters such as spindle speed, workpiece geometry and tool location. The accuracy of the models is examined. The SmartJaw is fabricated using the developed nanocomposite sensors and conductive inks, and the performance is compared with the commercial strain gauge system. Both accurate and precise measurements of forces are critical in machining operations for maximizing production, detecting tool failure and process monitoring.
- ItemOpen AccessDiscrete Choice-based Equilibrium Modeling of Supply Chain Network with Conflicting Objectives and Demand Uncertainty(2021-01-05) Ma, Jun; Tu, Yiliu; Nault, Barrie R.; Patterson, Raymond A.; Li, Simon; Ma, YongshengThis PhD thesis discusses several equilibrium problems in supply chain network integration and specifically concentrates on the importance of collaboration under conflicting objective decision-making and uncertainty management in a supply chain network. In particular, discrete choice models are introduced into supply chain equilibrium models to bridge the conflicting objective decision-making in supply chains and customer preference. Furthermore, numerical examples are provided for model illustration, managerial insights, and algorithm performance. Finally, it attempts to explore the tradeoffs between the operation costs, service level, and time issues in a supply chain, considering customer preference and demand uncertainty. Two conventional assumptions used in both the supply chain network equilibrium model and the newsvendor model are generalized and relaxed in order to obtain more general solutions and methods. First, this PhD thesis adopts Sheffi’s equilibrium condition assumption rather than Wardrop’s network equilibrium condition assumption. Discrete choice models are introduced into supply chain equilibrium models. A probabilistic fashion is used to describe customer choice behavior, because all factors affecting customer choice behavior cannot be observed completely. It assumes equilibrium will be reached when no customer believes that his utility can be improved by unilaterally changing products (or services) provided by supply chains. This assumption is not easy to use in practice, except it can be characterized and formulated as equilibrium conditions mathematically. Next, the equilibrium conditions are formulated as multinomial logit- and newsvendor model-based variational inequalities. Second, the assumption in the newsvendor model that unmet demand is lost implies that customers are stockout neutral. This study assumes the customers are stockout aversion. This work generalizes the implied assumption in the newsvendor model and introduces it into the supply chain equilibrium model. This PhD thesis has several contributions to the supply chain network integration with a focus on the collaboration under conflicting objective decision-making and uncertainty management in a supply chain network. First, discrete choice models are incorporated into supply chain equilibrium models to bridge the conflicting objective decision-making in supply chains and customer preference. It brings several technical problems. A variety of corollaries, theorems, and propositions are provided to illustrate the models and problems. Hence, the model can optimize supply chain profits without multi-objective conversion at the firm level. Second, heterogeneous customers’ discrete choice behaviors are considered in the model. Most existing supply chain network equilibrium models are capable of providing equilibrium solutions in a supply chain network, only under the assumption that customers are homogeneous. This assumption can be extended to heterogeneous customers by using a supply chain network economic or equilibrium models integrated with the multinomial logit model and data at the level of the customer individual. Third, this work assumes that customers are stockout aversion and introduce the newsvendor model to solve the issue that uncertain demand depends on both price and service level in a supply chain network for joint products. The newsvendor model-based variational inequality problems are given to formulate the equilibrium conditions.
- ItemOpen AccessElectric Field Assisted Alignment of Nanotubes for Low-Pass and Band-Pass Filters(2016) Li, Qiran; Kwok, Daniel; Li, Simon; Li, Leping; Yanushkevich, YanushkevichBecause of their unique electrical properties, carbon nanotubes (CNTs), particularly the single-walled carbon nanotubes (SWCNTs), have shown potential applications in electronics. In this study, by controlling their alignments under varied electric fields, SWCNTs were used to prepare microelectrode devices, which were examined and show low-pass filtering harmonic responses. By analyzing the SWCNT-based devices (vs. bare-chip), equivalent circuit models were created to understand these devices and the mechanism of their filtering performance. After a series of simplification and optimization, a final model was obtained and a hypothesis was proposed to explain the relationships between the CNT structures and the corresponding electrical properties. The resulted model was verified about its predictability to the filtering performance of CNT-based devices. In addition, similar devices were fabricated with two types of Rosette nanotubes and were then analyzed regarding their electric properties, confirming its effectiveness of the methodology that was used for the CNT-based devices.
- ItemOpen AccessEmbedded Multi-Agent Systems Based Wireless Control Networks for Cyber-Physical Systems(2019-05-29) Taboun, Mohammed Salem; Brennan, Robert William; Kremer, Rob C.; Li, Simon; Far, Behrouz Homayoun; Shen, WeimingWith the emergence of cyber-physical systems, there has been a growing interest in network-connected cyber-physical devices. One of the key requirements of a cyber-physical device is the ability to sense its environment. Wireless sensor networks are a widely-accepted solution for this requirement. As the capabilities of low processing power devices increase, there is also a growing interest in wireless control networks. As the capabilities of wireless control networks are extended to reconfigurable systems, the bandwidth requirements are drastically increasing. To remain reconfigurable, an intelligent entity needs to process all of this data. Intelligent models are typically deployed in a cloud environment, and required data (typically sensory and reconfiguration data) is passed through a series of network protocols (eg. wireless sensor network and internet protocols) where it is then aggregated and processed by a model, which then returns reconfiguration data and control instructions. For large-scale industrial networks however, the limit on bandwidth produces a challenge for employing intelligent cloud-based models. In this thesis, embedded intelligence in wireless control networks by means of embedded software agents is proposed. Through the use of wireless sensor communication protocols based off of hardware protocols such as ZigBee, the embedded agents are proposed to maintain intelligence while reducing the bandwidth requirements of the wireless control network. Architectures for both cloud-based and embedded agents are compared through an experiment and simulation, which shows the embedded agents are still able to maintain the same quality of service while reducing the bandwidth usage. As the goal of embedding agents is to maintain the intelligence of the wireless control network in a more distributed fashion, new wireless protocols need to be developed. In harsh industrial environments, the wireless control network is subject to blockages, such as interference from welding machines. In this thesis, two prescriptive reconfiguration protocols to overcome such harshness are proposed. The first reconfiguration protocol aims to overcome blockage between the individual sensing/acting nodes and the sink, which is responsible for aggregating and transmitting data to a data acquisition system. The second reconfiguration protocols are meant to overcome blockage between sensor nodes and actor nodes handling the same process. Both reconfiguration protocols are detailed, and tested in an experimental simulation. The results of the experiment show that when there is either type of blockage, the quality of service remains the same. The only significant effect is seen on process-level metrics when there is blockage between the sensor and actor nodes of the same process.
- ItemOpen AccessFactors Impacting Cost Growth on Heavy Industrial Projects in Alberta(2020-04-28) Haines, Daniel; Sadeghpour, Farnaz; Jergeas, George F.; Li, Simon; Dann, Markus R.Construction in the heavy industrial sector of Alberta makes up a large portion of the provincial economy. Unfortunately, this construction work is completed in an environment of some of the highest cost growth in the world. Projects constructed in this industry typically go over budget by 11% and on some projects, costs have even exceeded budgets by up to double. Given this situation, the industry and province are eager to reduce these cost overruns to bring greater profits and to encourage investment in the province. While research thus far has determined many potential causes and indicators of this cost growth, quantification of these factors is lacking. To truly reduce cost growth, it is necessary to determine the actual impact of many potential methods of controlling costs and also gain a better understanding of the predictive nature of various indicators. Large amounts of data was collected on 139 projects based in Alberta over the past 15 years. This data was statistically analyzed to find factors that contribute to cost growth and to develop phase-based predictive models to forecast which projects are at risk of running over budget. It was found that larger, longer and more complex projects are more likely to have cost growth. However, better engineering, increased modularization, and larger contingencies can help reduce this cost growth. Further, the amount of rework, growth in the amount of work completed in the winter and growth in the size of the peak work force are all early indicators that can help determine when a project may be facing unanticipated problems. These factors are investigated using bivariate and multiple regression procedures which identify the magnitude of the impact of each variable, allow for the prediction of cost growth and, when combined, allow the unique contribution of each variable to be determined. This detailed information allows companies to perform cost-benefit calculations to better prioritize investments into cost control measures. By controlling costs better, companies can increase their profits and the province can benefit from increased investment in an efficiently running industry.
- ItemOpen AccessA general approach for solving the Resource-Constrained Project and Multi-Project Scheduling Problem(2020-11-02) Ben Issa, Samer; Tu, Paul Yiliu L.; Li, Simon; Xue, DeyiAbstract The project scheduling problem belongs to the complex decision-making process, which has been increasingly attracting attention from both researchers and practitioners. Researchers have studied resource-constrained project scheduling problems (RCPSPs) and resource-constrained multi-project scheduling problems (RCMPSPs), focusing on limited or even inadequate resources as constraints to implementing project activities and decreasing completion times. These constraints typically make project scheduling problems highly challenging to deal with. Most of the current project scheduling software packages and scheduling techniques, which assume that activities, once started, cannot be interrupted. In other words, each project activity is pre-planned and cannot be re-scheduled into two or more sub-activities. Project activities can be interrupted due to limited resources and according to case studies in this thesis, it validates that this assumption leads to significant improvements in project schedules. Activities under traditional assumptions (i.e., under category A) can be performed using fixed resources along the Y-axis (resource axis) over a fixed duration along the X-axis (time axis) and cannot be interrupted. In this thesis, the traditional RCPSP is referred to as RCPSP(A). However, in practice, some project activities can be interrupted during the project execution. Consequently, the RCPSP should consider not only project activities under category A but also two more categories: B and D. Category B applies to activities that use fixed resources along the Y-axis but can be interrupted along the X-axis. Category D refers to activities that can be flexibly re-scheduled along both the Y-axis (changeable resources) and X-axis (interrupted or not interrupted). Under the classification of project activities into categories A, B and D individually, the project scheduling problems are correspondingly referred to as RCPSP(A), RCPSP(B) or RCPSP(D), respectively. Many of the existing papers and commercially available project scheduling software packages deal with project scheduling problems that belong to RCPSP(A); however, the RCPSP(B) and RCPSP(D) are rarely addressed. In the RCPSP(A) and RCPSP(B), project activity is pre-planned in advance by a fixed resource requirement over a total time duration, e.g. 4 men over 6 days, whereas, in the RCPSP(D), the activities are pre-planned by a fixed work content, e.g. 24 man-days. However, A, B and D categories can exist simultaneously in engineering projects, such as a one-of-a-kind production (OKP) project for making customized oil cargo, which leads the RCPSP to generally include the three categories simultaneously, simply recorded as RCPSP(ABD). The RCPSP(ABD) has not been considered in existing papers and commercially available project scheduling software packages. In fact, the RCPSP(ABD) is a more general view of RCPSP, and the RCPSP(A), RCPSP(B) or RCPSP(D) can be viewed as a special case of the RCPSP(ABD). In make-to-order and the Design-to-order manufacturing companies, to better focus on customer demands and specific requirements, each customer order is treated as a project in the company, and the company has to handle multi-projects to meet all its customers’ demands. Project activities are classified under category A, and the production schedule is subject to three constraints: 1) resources are limited, 2) customer requests are considered as projects, and 3) projects are initiated based on demand time. The production schedule in these types of production companies can be considered as the resource-constrained multi-project scheduling problem under activity categories A, B and D simultaneously (RCMPSP(ABD)) in an integrated manner. To improve production efficiency and reduce costs, the resources must be shared among the projects. Thus, an effective scheduling algorithm is needed to integrate all project schedules into one production plan and then to optimize this plan, i.e. to find the shortest makespan for the production. In this thesis, the research focus is placed on the RCPSP(ABD) /RCMPSP(ABD), and a new priority rule-based heuristic has been developed to examine the impact of the interruptible and changeable activities assumptions in terms of shortening the project or multi-project makespan and increasing the average utilization rate of resources. The thesis finds alternative schedules and helps project managers selectively choose how specific activities are to be planned and re-scheduled in order to improve project scheduling efficiency. The heuristic identifies the critical interruptible activities that have significant impacts on the project makespan and facilitates project activity planning and re-scheduling under categories A, B and D simultaneously.
- ItemOpen AccessHigher-Order (Temporal) Relationship-Based Access Control(2022-02) Arora, Chahal; Fong, Philip; Reardon, Joel; Li, SimonWith the advent of technologies such as the Internet of Things, new type of relationships have emerged between users and devices. These relationships are transient, which means they can be activated and terminated over time. Existing Relationship-Based Access Control (ReBAC) models are not designed for handling such relationships efficiently. In this work, we present a ReBAC model that can incorporate such transient relationships, thus allowing the creation of access control policies that can use the transient nature of relationships to grant authorization. We call this model Higher-Order (Temporal) Relationship-Based Access Control (HO(T)-ReBAC) model. This thesis formalized the HO(T)-ReBAC model and defined a formal policy language for access control policies in HO(T)-ReBAC. We then discussed case studies based on real-world scenarios where HO(T)-ReBAC can be deployed for authorization decisions. After that, we designed and presented an efficient model implementation that can be used for large-scale projects in the real world. We empirically evaluated our implementation of HO(T)-ReBAC using a real-world social graph and the use case we discussed. Our evaluation found our implementation to be efficient for real-world large-scale projects.
- ItemOpen AccessImpact of Best Practices on the Cost and Schedule Performance of Heavy Industrial Construction Projects(2019-03-25) Robu, Mihai; Sadeghpour, Farnaz; Jergeas, George Farage; Dann, Markus R.; Hunt, John D.; Li, Simon; El-Badry, Mamdouh; Jergeas, George Farage; Sadeghpour, FarnazThe heavy industrial sector is important to the North American economy, however, projects in this sector are often overbudget and behind-schedule. An improvement in project delivery can positively impact the organizations involved in the heavy industrial sector and subsequently on the economy. While there are many factors impacting project performance, the implementation of best practices is within the control of practitioners. However, as implementing best practices can be costly and/or time-consuming, detailed knowledge of the impact of best practices is valuable to practitioners seeking to improve project performance. To date, the impact of best practices has only been studied at the project-level but not at the more granular phase-level. Statistical analysis is conducted on 1,015 heavy industrial projects to determine if the impact of best practices is different across phases and to determine the magnitude of the impact. Pairwise inferential tests are used to determine if the relationship between best practices and phase cost and schedule performance are statistically significant. It was found that best practices impacted different phases and was speculated that the impact is related to the nature of activities being conducted in each phase. Cost performance benefited most from a high degree of scope definition, partnering agreements, and the implementation of risk mitigation. Schedule performance improved most from increased scope definition, risk assessments, and a documented and well-understood change management plan. The cost and schedule characteristics of projects are also explored through descriptive statistics for each phase to better understand the uniqueness of each phase. It was found that each project phase exhibited different cost and schedule characteristics, with the Commissioning phase having the worst mean, and most variability in performance. The cost and schedule benchmarks can be used by industry practitioners to compare their project performance to an average specific to heavy industrial projects, which allows for identification of areas of strengths and weaknesses. The impact of best practices at the more granular phase-level allows for more judicious implementation of best practices based on the improvement goals identified by practitioners.
- ItemOpen AccessIntegrated Design of Complex Mechanical Products Considering Modeling, Simulation and Optimization Aspects(2020-04-21) Imaniyan, Davood; Xue, Deyi; Li, Simon; Yanushkevich, Svetlana N.The presently developed computer-based design systems are not effective for design of complex mechanical products when multiple tools and methods in different schemes have to be employed at different design stages. In this research, a new integrated framework has been introduced for the design of complex mechanical products considering modeling, simulation/evaluation, and optimization aspects. An integrated system for design of complex mechanical products has also been developed. In this system, first a hybrid scheme is introduced for integrated modeling of complex mechanical products considering conceptual design and detailed design stages. In conceptual design, the generic product considering different design solution candidates is modeled in an AND-OR tree. Specific design candidates modeled by AND trees are created from the generic AND-OR tree through tree-based search. The geometric descriptions in a design candidate are then converted into and associated with the geometric model in a CAD system for detailed design. Second, a hybrid simulation method is developed for evaluating different product aspects with different simulation tools that are integrated through the hybrid modeling scheme. Simulations with geometric descriptions are conducted by analysis functions of the CAD system for detailed design. Simulations with non-geometric descriptions are conducted by the knowledge-based systems for conceptual design. Third, a hybrid optimization method is developed to identify the optimal design of the complex mechanical product. For each design candidate, parameter optimization is conducted to obtain the optimal parameter values. The optimal design solution is identified from all design candidates through configuration optimization. The integrated complex mechanical product design system has been implemented using C# and SOLIDWORKS. Various user interfaces were developed for conducting design activities in modeling, simulation/evaluation and optimization aspects. Communication between the symbolic model in conceptual design and the CAD model in detailed design was achieved through TCP/IP client-server structure and SOLIDWORKS API. A case study has been developed to demonstrate the effectiveness of the newly-introduced design approach.