The objective of this thesis research is to model the multiple program objectives used by Defence Research and Development Canada (DRDC) for the annual management and allocation of their broad range of Science and Technology (S&T) projects in order to best achieve the strategic goals of the agency and the government. This M.Sc. thesis presents methodologies, techniques and applications in Linear Programming (LP) and Multi-Criteria Decision Making (MCDM) for decision support in program prioritization and project selection of the DRDC S&T projects. The results of this research produce a model that supports decision makers effectively in the assignment of limited human and financial resources to competing S&T projects based on the evaluation of projects that merit funding and the multiple criteria established by the organization. While there is a well-defined set of criteria for the annual program formulation process, the selection procedure is currently based on simple scoring processes and expert judgement; it lacks a well-defined and structured analysis. The application of an MCDM framework is proposed to take advantage of the well-structured problem and improve annual renewal and ongoing monitoring or project performance measures. The results of the analysis provide a traceable and rigorous MCDM framework to evaluate the performance of DRDC S&T projects for enhanced resource allocation.

The objective of this thesis research is to model the multiple program objectives used by Defence Research and Development Canada (DRDC) for the annual management and allocation of their broad range of Science and Technology (S&T) projects in order to best achieve the strategic goals of the agency and the government. This M.Sc. thesis presents methodologies, techniques and applications in Linear Programming (LP) and Multi-Criteria Decision Making (MCDM) for decision support in program prioritization and project selection of the DRDC S&T projects. The results of this research produce a model that supports decision makers effectively in the assignment of limited human and financial resources to competing S&T projects based on the evaluation of projects that merit funding and the multiple criteria established by the organization. While there is a well-defined set of criteria for the annual program formulation process, the selection procedure is currently based on simple scoring processes and expert judgement; it lacks a well-defined and structured analysis. The application of an MCDM framework is proposed to take advantage of the well-structured problem and improve annual renewal and ongoing monitoring or project performance measures. The results of the analysis provide a traceable and rigorous MCDM framework to evaluate the performance of DRDC S&T projects for enhanced resource allocation.

The objective of this thesis research is to model the multiple program objectives used by Defence Research and Development Canada (DRDC) for the annual management and allocation of their broad range of Science and Technology (S&T) projects in order to best achieve the strategic goals of the agency and the government. This M.Sc. thesis presents methodologies, techniques and applications in Linear Programming (LP) and Multi-Criteria Decision Making (MCDM) for decision support in program prioritization and project selection of the DRDC S&T projects. The results of this research produce a model that supports decision makers effectively in the assignment of limited human and financial resources to competing S&T projects based on the evaluation of projects that merit funding and the multiple criteria established by the organization. While there is a well-defined set of criteria for the annual program formulation process, the selection procedure is currently based on simple scoring processes and expert judgement; it lacks a well-defined and structured analysis. The application of an MCDM framework is proposed to take advantage of the well-structured problem and improve annual renewal and ongoing monitoring or project performance measures. The results of the analysis provide a traceable and rigorous MCDM framework to evaluate the performance of DRDC S&T projects for enhanced resource allocation.

Significant investments are being made in the construction sector in order to raise the quality of the buildings and make them more sustainable and energy-efficient. The key aspect of these investments should be the purposeful optimization of the possible renovation and construction measures. However, this important matter usually is being pushed aside in favor of construction price and/or quality. Nevertheless, there are plenty of criteria that play a major role in building sustainable development. The main purpose of this study is to present a tool that combines multi-criteria decision making (MCDM) methods and building certification systems in order to make weighted decisions in complicated construction tasks. For this, a decision making model was developed with a focus on sustainability, buildings’ life cycle, MCDM methods, and building certification. The first section of this thesis, the introduction, discusses the importance of the investigated area, and the main objectives, tasks, and structure of the thesis. A literature review is presented in Section 2 – Theory. The main works in the area of sustainability, LCA, building certification, and MCDM are collected to show their role and importance and how they interact in the construction industry. Section 3 presents and discusses the main ideas and instructions of the proposed decision making model. Section 4 (Methodology) introduces the main existing and proposed techniques that I have used to implement the study. Sections 5 and 6 are the case studies, which demonstrate how the proposed methods can be used in practice. Final conclusions and recommendations are presented in Section 7. / <p>QC 20170209</p> / Funded by L.E. Lundberg foundation

The availability of water can be considered as one of the key ingredients to the human life, yet this resource remains scarcely available to those living in the rural parts of Africa. When water does present itself, it is often impure and requires extensive treatment. Water treatment systems, particularly those capable of treating water in rural areas, are currently areas of research and entrepreneurship focus, making a number of potential solutions available, and other still coming in. Unfortunately, these systems are not always capable of performing in particular socio-cultural and economic contexts, or are often deployed in the wrong rural areas. Therefore these systems do not perform at their optimal level of design. Rural areas in Africa have different socio-cultural and economic context from each other, and this needs to be taken into account if one is going to select the right water treatment system for a particular area. Using industrial engineering tools, two water treatment system selection models; an Additive Analytic Hierarchy Process model and a Fuzzy Logic based model, are presented and then integrated. These models take into account the context of selected rural area by pitting available water purification systems against selected criteria to determine if it is the right fit for the rural area considered. Both models are then pitted against each other to determine which is more adept at selecting the appropriate water purification system. Three water treatment alternatives were considered after an analysis was conducted on the available solutions on the market. The water treatment systems under consideration were the Biosand Filter with Zeolites (BSFZ), the Silver Impregnated Porous Pot, and A Borehole system. Makwane, a rural village in Limpopo, South Africa was used as a case study to demonstrate the application of the selection models. The BSFZ was selected as the ideal water treatment system to be implemented in Makwane / Dissertation (MEng)--University of Pretoria, 2016. / Industrial and Systems Engineering / MEng / Unrestricted

[pt] A Indústria 4.0 (I4.0) e a transformação digital estão revolucionando a manutenção nas indústrias, impulsionando-a rumo a uma abordagem mais inteligente e proativa, conhecida como manutenção inteligente (smart maintenance – SM). Recentemente vive-se a transição para a Manutenção 4.0, em que decisões baseada em dados e análises avançadas trazidas com a SM permitem aumentar a eficiência, reduzir os custos operacionais e têm um grande impacto no desempenho operacional. Com a crescente digitalização dos processos e a disponibilidade de novas tecnologias, as decisões estão se tornando mais inteligentes, o que requer ter um processo de tomada de decisão estruturado. No entanto, tomar decisões gerenciais pode ser complexo devido a múltiplos critérios e pontos de vista envolvidos. Por exemplo, podem existir trade-offs e prioridades competitivas diferentes entre equipes funcionais como de manutenção, de produção e financeira. Nessa perspectiva, é crucial ter uma metodologia que combine esses aspectos conflitantes e, na era da Manutenção 4.0, a consideração de múltiplos critérios e pontos de vista, justifica a necessidade de um framework de apoio a decisão que combine técnicas de apoio multicritério a decisão (multi-criteria decision making - MCDM) e Machine Learning (ML). A partir da revisão de escopo observou-se a ausência de metodologias (e frameworks) de apoio a decisão combinando essas abordagens em estudos empíricos e em países emergentes. Diante disso, a presente pesquisa propoe aplicar um framework de apoio à decisão para SM em empresa de manufatura brasileira. Como método empírico foi realizado um estudo de caso, utilizando dados reais de manutenção, observação participante e entrevistas, além de análise documental. Uma abordagem multicritério híbrida é proposta por meio dos métodos AHP, MOORA, MULTIMORA e de Borda com dados qualitativos e quantitativos, para resolver um problema de ranking de impressoras para fazer parte do início das manutenções preditivas. A implementação computacional compõem a metodologia ocorreu em Python. Ao final foi possível observar que a combinação de MCDM e ML pode ser uma abordagem eficaz para aprimorar a tomada de decisão na manutenção, considerando a complexidade dos dados envolvidos. / [en] Industry 4.0 (I4.0) and digital transformation are revolutionizing maintenance in industries, pushing it towards a more intelligent and proactive approach, known as smart maintenance (SM). Recently, the transition to Maintenance 4.0 has been experienced, where maintenance decisions based on data and advanced analytics brought about by SM make it possible to increase efficiency, reduce operating costs and have a major impact on operational performance. With the increasing digitalization of processes and the availability of new technologies, decisions are becoming smarter, which requires having a structured, data-driven decision-making process for efficient decisions. However, making management decisions can be complex due to the multiple criteria and points of view involved. For example, there can be trade-offs and different competing priorities between functional areas such as maintenance, production and finance. From this perspective, it is crucial to have a methodology that combines these conflicting aspects, and in the Maintenance 4.0 era, the consideration of multiple criteria and points of view justifies the need for a decision support framework that combines multi-criteria decision making (MCDM) and Machine Learning (ML) techniques. A scoping review showed that there is a lack of decision support methodologies (and frameworks) combining these approaches in empirical studies and in emerging countries. In view of this, this research aims to propose and apply a decision support framework for MS in a Brazilian manufacturing company. A case study is used as the empirical method, using real maintenance data, participant observation and interviews, as well as document analysis. A hybrid multi-criteria approach is proposed using AHP, MOORA, MULTIMORA and Borda methods with qualitative and quantitative data, to solve a problem of ranking printers to be part of the start of predictive maintenance. The computational implementation of the approaches that make up the methodology took place in Python. At the end of the research, it was possible to observe that the combination of MCDM and ML can be an effective approach to improve decision-making in asset maintenance, considering multiple criteria and the complexity of the data involved.

According to the IPCC (2007) fourth assessment report, small islands and coastal communities have a set of characteristics that makes them very vulnerable to climate change impacts, mainly sea-level rise and storm surges. Coastal hazards including inundation, salinisation of the water supply, and erosion threaten vital infrastructure that support coastal communities. Although Canada has the longest coastline in the world, little work has been done on impacts of climate change and adaptation to these impacts in the Canadian coastal zones. This research is part of an International Community-University Research Alliance (ICURA) C-Change, project to develop a multicriteria decision evaluation and support for the systems analysis of adaptation options for coastal communities toward adapting to environmental changes. This study estimates the vulnerability of coastal communities with respect to their environmental, economic, social, and cultural dimensions. It also applies a group version of the Analytical Hierarchy Process for identifying decisions that various stakeholders make on suggested adaptation strategies. This study develops a methodological framework that is applicable to various coastal and small island contexts. The application of the proposed framework is further discussed in a case study conducted on the communities of Charlottetown, Prince Edward Island (PEI), and Little Anse on Isle Madame, Nova Scotia. Specifically, the state of the Little Anse breakwater is analyzed and new adaptation options are presented and evaluated. This research has illustrated and applied a process of decision evaluation and support that explicitly engages multiple participants and critieria in complex problems situations involving environmental change in coastal communities.

According to the IPCC (2007) fourth assessment report, small islands and coastal communities have a set of characteristics that makes them very vulnerable to climate change impacts, mainly sea-level rise and storm surges. Coastal hazards including inundation, salinisation of the water supply, and erosion threaten vital infrastructure that support coastal communities. Although Canada has the longest coastline in the world, little work has been done on impacts of climate change and adaptation to these impacts in the Canadian coastal zones. This research is part of an International Community-University Research Alliance (ICURA) C-Change, project to develop a multicriteria decision evaluation and support for the systems analysis of adaptation options for coastal communities toward adapting to environmental changes. This study estimates the vulnerability of coastal communities with respect to their environmental, economic, social, and cultural dimensions. It also applies a group version of the Analytical Hierarchy Process for identifying decisions that various stakeholders make on suggested adaptation strategies. This study develops a methodological framework that is applicable to various coastal and small island contexts. The application of the proposed framework is further discussed in a case study conducted on the communities of Charlottetown, Prince Edward Island (PEI), and Little Anse on Isle Madame, Nova Scotia. Specifically, the state of the Little Anse breakwater is analyzed and new adaptation options are presented and evaluated. This research has illustrated and applied a process of decision evaluation and support that explicitly engages multiple participants and critieria in complex problems situations involving environmental change in coastal communities.

Streptomyces clavuligerus is well-known for synthesizing several &beta / -lactam antibiotics like cephamycin C which is produced through aspartic acid pathway initiated by aspartokinase (Ask) enzyme encoded by ask. Four different strains were constructed in our laboratory to increase cephamycin C production by S. clavuligerus. TB3585 and BA39 contained extra copies of ask gene on a multicopy plasmid, control strains TBV and BAV contained vector only in wild type strain NRRL3585 and hom-minus background, AK39, respectively. In this study, the effects of carbon and nitrogen sources incorporated into chemically defined medium were investigated for optimum growth and cephamycin C production by AK39. A modified-chemically defined medium (mCDM) was obtained by increasing the asparagine concentration two-fold and replacing glycerol with sucrose. Subsequently, growth and cephamycin C production by recombinant S. clavuligerus strains (TB3585, AK39, BA39, BAV, TBV) in Tryptic Soy Broth (TSB) and mCDM were compared. The specific antibiotic production in mCDM by TB3585 was 3.3- and 3.2-fold higher than TBV at 72h and 96h, respectively. Aspartokinase activity of S. clavuligerus recombinants was measured to verify the ask overexpression. TB3585 showed the highest activity at 48h. Finally, intracellular amino acid pools of the strains were measured to relate the Ask activity and antibiotic production to the amino acid content within the cells. AK39 was shown to have the highest intracellular levels of lysine, leading to cephamycin C precursor synthesis / lysine plus threonine, exerting concerted feedback inhibition on Ask enzyme / methionine, which cannot be produced by AK39 like threonine due to hom disruption.

According to the IPCC (2007) fourth assessment report, small islands and coastal communities have a set of characteristics that makes them very vulnerable to climate change impacts, mainly sea-level rise and storm surges. Coastal hazards including inundation, salinisation of the water supply, and erosion threaten vital infrastructure that support coastal communities. Although Canada has the longest coastline in the world, little work has been done on impacts of climate change and adaptation to these impacts in the Canadian coastal zones. This research is part of an International Community-University Research Alliance (ICURA) C-Change, project to develop a multicriteria decision evaluation and support for the systems analysis of adaptation options for coastal communities toward adapting to environmental changes. This study estimates the vulnerability of coastal communities with respect to their environmental, economic, social, and cultural dimensions. It also applies a group version of the Analytical Hierarchy Process for identifying decisions that various stakeholders make on suggested adaptation strategies. This study develops a methodological framework that is applicable to various coastal and small island contexts. The application of the proposed framework is further discussed in a case study conducted on the communities of Charlottetown, Prince Edward Island (PEI), and Little Anse on Isle Madame, Nova Scotia. Specifically, the state of the Little Anse breakwater is analyzed and new adaptation options are presented and evaluated. This research has illustrated and applied a process of decision evaluation and support that explicitly engages multiple participants and critieria in complex problems situations involving environmental change in coastal communities.