Search‐based model transformations

Model transformations are an important cornerstone of model‐driven engineering, a discipline which facilitates the abstraction of relevant information of a system as models. The success of the final system mainly depends on the optimization of these models through model transformations. Currently, the application of transformations is realized either by following the apply‐as‐long‐as‐possible strategy or by the provision of explicit rule orchestrations. This implies two main limitations. First, the optimization objectives are implicitly hidden in the transformation rules and their orchestration. Second, manually finding the best orchestration for a particular scenario is a major challenge due to the high number of possible combinations. To overcome these limitations, we present a novel framework that builds on the non‐intrusive integration of optimization and model transformation technologies. In particular, we formulate the transformation orchestration task as an optimization problem, which allows for the efficient exploration of the transformation space and explication of the transformation objectives. Our generic framework provides several search algorithms and guides the user in providing a proper search configuration. We present different instantiations of our framework to demonstrate its feasibility, applicability, and benefits using several case studiesEuropean Commission ICT Policy Support Programme 317859Ministerio de Economia y Competitividad TIN2015-70560-RJunta de Andalucía P10-TIC-5960Junta de Andalucía P12-TIC-186

How to Evaluate Solutions in Pareto-based Search-Based Software Engineering? A Critical Review and Methodological Guidance

With modern requirements, there is an increasing tendency of considering multiple objectives/criteria simultaneously in many Software Engineering (SE) scenarios. Such a multi-objective optimization scenario comes with an important issue -- how to evaluate the outcome of optimization algorithms, which typically is a set of incomparable solutions (i.e., being Pareto non-dominated to each other). This issue can be challenging for the SE community, particularly for practitioners of Search-Based SE (SBSE). On one hand, multi-objective optimization could still be relatively new to SE/SBSE researchers, who may not be able to identify the right evaluation methods for their problems. On the other hand, simply following the evaluation methods for general multi-objective optimization problems may not be appropriate for specific SE problems, especially when the problem nature or decision maker's preferences are explicitly/implicitly available. This has been well echoed in the literature by various inappropriate/inadequate selection and inaccurate/misleading use of evaluation methods. In this paper, we first carry out a systematic and critical review of quality evaluation for multi-objective optimization in SBSE. We survey 717 papers published between 2009 and 2019 from 36 venues in seven repositories, and select 95 prominent studies, through which we identify five important but overlooked issues in the area. We then conduct an in-depth analysis of quality evaluation indicators/methods and general situations in SBSE, which, together with the identified issues, enables us to codify a methodological guidance for selecting and using evaluation methods in different SBSE scenarios.Comment: This paper has been accepted by IEEE Transactions on Software Engineering, available as full OA: https://ieeexplore.ieee.org/document/925218

SPEA2-based safety system multi-objective optimization

Safety systems are designed to prevent the occurrence of certain conditions and their future development into a hazardous situation. The consequence of the failure of a safety system of a potentially hazardous industrial system or process varies from minor inconvenience and cost to personal injury, significant economic loss and death. To minimise the likelihood of a hazardous situation, safety systems must be designed to maximise their availability. Therefore, the purpose of this thesis is to propose an effective safety system design optimization scheme. A multi-objective genetic algorithm has been adopted, where the criteria catered for includes unavailability, cost, spurious trip and maintenance down time. Analyses of individual system designs are carried out using the latest advantages of the fault tree analysis technique and the binary decision diagram approach (BDD). The improved strength Pareto evolutionary approach (SPEA2) is chosen to perform the system optimization resulting in the final design specifications. The practicality of the developed approach is demonstrated initially through application to a High Integrity Protection System (HIPS) and subsequently to test scalability using the more complex Firewater Deluge System (FDS). Computer code has been developed to carry out the analysis. The results for both systems are compared to those using a single objective optimization approach (GASSOP) and exhaustive search. The overall conclusions show a number of benefits of the SPEA2 based technique application to the safety system design optimization. It is common for safety systems to feature dependency relationships between its components. To enable the use of the fault tree analysis technique and the BDD approach for such systems, the Markov method is incorporated into the optimization process. The main types of dependency which can exist between the safety system component failures are identified. The Markov model generation algorithms are suggested for each type of dependency. The modified optimization tool is tested on the HIPS and FDS. Results comparison shows the benefit of using the modified technique for safety system optimization. Finally the effectiveness and application to general safety systems is discussed

BIM-based Generative Modular Housing Design and Implications for Post-Disaster Housing Recovery

The adverse social and financial impacts of catastrophic disasters are increasing as population centers grow. After disastrous events, the government agencies must respond to post-disaster housing issues quickly and efficiently and provide sufficient resources for the reconstruction of destroyed and damaged houses for full rehabilitation. However, post-disaster housing reconstruction is a highly complex process because of the large number of projects, shortage of resources, and heavy pressure for delivery of the projects after a disastrous event. This complexity and lack of an inconsistent, systematic approach for planning lead to an ad-hoc decision-making process and inefficient recovery. This research explored modular construction as a highly time-efficient approach to tackle the abovementioned challenges and facilitate the housing reconstruction process. Firstly, this research investigated the feasibility of using the modular construction method for rapid post-disaster housing reconstruction through a targeted literature review and survey of subject matter experts to broaden the understanding of modular construction-based post-disaster housing reconstruction, benefits, and barriers. Second, this research focused on improving the design and pre-planning phase of modular construction that can facilitate the successful implementation of modular construction in a post-disaster situation. To this end, a BIM-based generative modular housing design system was developed by using Generative Adversarial Networks (GANs) to automate the entire design process by incorporating manufacturing and construction constraints to fit the needs of the modular construction method. The framework was further extended by developing an optimization model to optimize the modularization strategy in the early design phase which was capable of reflecting the entire multi-stage process of modular construction (production, transportation, and assembly), and considering both individual project’s requirements and post-disaster housing reconstruction portfolio’s requirements. The outcomes of this study fit the MC industry that may be used by designers and modular housing companies looking to automate their design process. It is also expected to provide critical benchmarks for planners, decision-makers, and community developers to facilitate their decision-making process on considering modular construction as an efficient way for mass post-disaster housing reconstruction and addressing communities’ housing needs following a disastrous event

Intelligent Web Services Architecture Evolution Via An Automated Learning-Based Refactoring Framework

Architecture degradation can have fundamental impact on software quality and productivity, resulting in inability to support new features, increasing technical debt and leading to significant losses. While code-level refactoring is widely-studied and well supported by tools, architecture-level refactorings, such as repackaging to group related features into one component, or retrofitting files into patterns, remain to be expensive and risky. Serval domains, such as Web services, heavily depend on complex architectures to design and implement interface-level operations, provided by several companies such as FedEx, eBay, Google, Yahoo and PayPal, to the end-users. The objectives of this work are: (1) to advance our ability to support complex architecture refactoring by explicitly defining Web service anti-patterns at various levels of abstraction, (2) to enable complex refactorings by learning from user feedback and creating reusable/personalized refactoring strategies to augment intelligent designers’ interaction that will guide low-level refactoring automation with high-level abstractions, and (3) to enable intelligent architecture evolution by detecting, quantifying, prioritizing, fixing and predicting design technical debts. We proposed various approaches and tools based on intelligent computational search techniques for (a) predicting and detecting multi-level Web services antipatterns, (b) creating an interactive refactoring framework that integrates refactoring path recommendation, design-level human abstraction, and code-level refactoring automation with user feedback using interactive mutli-objective search, and (c) automatically learning reusable and personalized refactoring strategies for Web services by abstracting recurring refactoring patterns from Web service releases. Based on empirical validations performed on both large open source and industrial services from multiple providers (eBay, Amazon, FedEx and Yahoo), we found that the proposed approaches advance our understanding of the correlation and mutual impact between service antipatterns at different levels, revealing when, where and how architecture-level anti-patterns the quality of services. The interactive refactoring framework enables, based on several controlled experiments, human-based, domain-specific abstraction and high-level design to guide automated code-level atomic refactoring steps for services decompositions. The reusable refactoring strategy packages recurring refactoring activities into automatable units, improving refactoring path recommendation and further reducing time-consuming and error-prone human intervention.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/142810/1/Wang Final Dissertation.pdfDescription of Wang Final Dissertation.pdf : Dissertatio

Product modularity : a multi-objective configuration approach

Product modularity is often seen as a means by which a product system can be decomposed into smaller, more manageable chunks in order to better manage design, manufacturing and after-sales complexity. The most common approach is to decompose the product down to component level and then group the components to form modules. The rationale for module grouping can vary, from the more technical physical and functional component interactions, to any number of strategic objectives such as variety, maintenance and recycling. The problem lies with the complexity of product modularity under these multiple (often conflicting) objectives. The research in this thesis presents a holistic multi-objective computer aided modularity optimisation (CAMO) framework. The framework consists of four main steps: 1) product decomposition; 2) interaction analysis; 3) formation of modular architectures and; 4) scenario analysis. In summary of these steps: the product is first decomposed into a number a basic components by analysis of both the physical and functional product domains. The various dependencies and strategic similarities that occur between the product s components are then analysed and entered into a number of interaction matrixes. A specially developed multi-objective grouping genetic algorithm (MOGGA) then searches the matrices and provides a whole set of alternative (yet optimal) modular product configurations. The solution set is then evaluated and explored (scenario analysis) using the principles of Analytic Hierarchy Process. A software prototype has been created for the CAMO framework using Visual Basic to create a multi-objective genetic algorithm (GA) based optimiser within an excel environment. A case study has been followed to demonstrate the various steps of the framework and make comparisons with previous works. Unlike previous works, that have used simplistic optimisation algorithms and have in general only considered a limited number of modularisation objectives, the developed framework provides a true multi-objective approach to the product modularisation problem.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

SPEA2-based safety system multi-objective optimization

Safety systems are designed to prevent the occurrence of certain conditions and their future development into a hazardous situation. The consequence of the failure of a safety system of a potentially hazardous industrial system or process varies from minor inconvenience and cost to personal injury, significant economic loss and death. To minimise the likelihood of a hazardous situation, safety systems must be designed to maximise their availability. Therefore, the purpose of this thesis is to propose an effective safety system design optimization scheme. A multi-objective genetic algorithm has been adopted, where the criteria catered for includes unavailability, cost, spurious trip and maintenance down time. Analyses of individual system designs are carried out using the latest advantages of the fault tree analysis technique and the binary decision diagram approach (BDD). The improved strength Pareto evolutionary approach (SPEA2) is chosen to perform the system optimization resulting in the final design specifications. The practicality of the developed approach is demonstrated initially through application to a High Integrity Protection System (HIPS) and subsequently to test scalability using the more complex Firewater Deluge System (FDS). Computer code has been developed to carry out the analysis. The results for both systems are compared to those using a single objective optimization approach (GASSOP) and exhaustive search. The overall conclusions show a number of benefits of the SPEA2 based technique application to the safety system design optimization. It is common for safety systems to feature dependency relationships between its components. To enable the use of the fault tree analysis technique and the BDD approach for such systems, the Markov method is incorporated into the optimization process. The main types of dependency which can exist between the safety system component failures are identified. The Markov model generation algorithms are suggested for each type of dependency. The modified optimization tool is tested on the HIPS and FDS. Results comparison shows the benefit of using the modified technique for safety system optimization. Finally the effectiveness and application to general safety systems is discussed.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

SPEA2-based safety system multi-objective optimization

Safety systems are designed to prevent the occurrence of certain conditions and their future development into a hazardous situation. The consequence of the failure of a safety system of a potentially hazardous industrial system or process varies from minor inconvenience and cost to personal injury, significant economic loss and death. To minimise the likelihood of a hazardous situation, safety systems must be designed to maximise their availability. Therefore, the purpose of this thesis is to propose an effective safety system design optimization scheme. A multi-objective genetic algorithm has been adopted, where the criteria catered for includes unavailability, cost, spurious trip and maintenance down time. Analyses of individual system designs are carried out using the latest advantages of the fault tree analysis technique and the binary decision diagram approach (BDD). The improved strength Pareto evolutionary approach (SPEA2) is chosen to perform the system optimization resulting in the final design specifications. The practicality of the developed approach is demonstrated initially through application to a High Integrity Protection System (HIPS) and subsequently to test scalability using the more complex Firewater Deluge System (FDS). Computer code has been developed to carry out the analysis. The results for both systems are compared to those using a single objective optimization approach (GASSOP) and exhaustive search. The overall conclusions show a number of benefits of the SPEA2 based technique application to the safety system design optimization. It is common for safety systems to feature dependency relationships between its components. To enable the use of the fault tree analysis technique and the BDD approach for such systems, the Markov method is incorporated into the optimization process. The main types of dependency which can exist between the safety system component failures are identified. The Markov model generation algorithms are suggested for each type of dependency. The modified optimization tool is tested on the HIPS and FDS. Results comparison shows the benefit of using the modified technique for safety system optimization. Finally the effectiveness and application to general safety systems is discussed.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Optimized Planning and Scheduling for Modular and Offsite Construction

Offsite construction has gained momentum in recent years due to its improved performance in projects‘ schedule, quality, safety, and environmental impact without increasing cost. Several research studies have introduced planning and scheduling techniques for modular and offsite construction using Building Information Modeling (BIM) and simulation tools. In this research, a questionnaire survey was carried out in collaboration with the modular building institute (MBI), Niagara Relocatable Buildings Inc. (NRB Inc.), and the School of Building Science and Engineering at the University of Alberta. The questionnaire focused on two issues: (1) modular and offsite construction industry characteristics, and (2) barriers to increased market share in this industry. For the latter issue, effort was made to address five factors that emanated from the workshop on ―challenges and opportunities for modular construction in Canada,‖ held in October 2015, Montreal to analyze barriers to modular construction growth in Canada. Key findings of this questionnaire include requests for the use of a separate modular construction design code, innovative financing and insurance solutions, standards that consider procurement regulations, and lending institutions that partner with financial houses to create special lending programs for modular construction. Findings of this questionnaire were published on the official MBI website. This research presents an alternative BIM-based integrated framework for modeling, planning, and scheduling of modular and offsite construction projects. BIM Vertex BD software was used in the proposed framework for automating data exchange between projects‘ BIM model and the proposed scheduling method. The proposed method integrated linear scheduling method (LSM), critical chain project management (CCPM), and the last planner system (LPS) into a comprehensive BIM-based framework for scheduling, monitoring, tracking, and controlling of projects while considering uncertainty associated with activity durations. A procedure for integrating offsite and onsite construction was introduced based on the proposed scheduling methodology. Then, a new multi-objective optimization model was developed using genetic algorithm (GA) to optimize the integration between the LSM and CCPM. This optimization model minimizes time, cost, and work interruptions simultaneously while considering uncertainty in productivity rates, quantities, and availability of resources. The developed model was based on the integration of six modules: 1) uncertainty and defuzzification module, 2) schedule calculations module, 3) cost calculations module, 4) optimization module, 5) module for identifying multiple critical sequences and schedule buffers, and 6) reporting module. Schedule buffers were assigned whether or not the optimized schedule allows for interruptions. This method considers delay and work interruption penalties and bonus payments. The developed integrated scheduling model for offsite and onsite construction was automated in newly developed software named ―Mod-Scheduler‖ using the ASP.NET system coded in C# programming language. A number of case studies were presented and analyzed to demonstrate the developed methodologies‘ features and capabilities. This research also introduces a novel modular suitability index (MSI), which utilizes five indices; 1) connections index (CI), 2) transportation dimensions index (TDI), 3) transportation shipping distance index (TSDI), 4) crane cost penalty index (CCPI), and 5) concrete volume index (CVI). Calculating the MSI provided a unified indicator to assist in selecting near optimum module configurations for efficient planning of modular residential construction. This research identifies the main factors affecting the configuration of modules in hybrid construction projects to introduce a new configuration model that is expected to assist hybrid construction stakeholders in identifying the most suitable configuration for each type of modules (i.e. panels) in their projects. A hybrid construction case study was selected to demonstrate the applicability of proposed model and to highlight its capabilities in selecting the most suitable configuration of panelized projects