Defining a management strategy for the azorean natural thermal resources : valuing criteria identification

Dissertação de Mestrado, Gestão de Empresas (MBA), 23 de Maio de 2016, Universidade dos Açores.

Application of the analytic hierarchy process (AHP) in multi-criteria analysis of the selection of intelligent building systems

The availability of innumerable intelligent building (IB) products, and the current dearth of inclusive building component selection methods suggest that decision makers might be confronted with the quandary of forming a particular combination of components to suit the needs of a specific IB project. Despite this problem, few empirical studies have so far been undertaken to analyse the selection of the IB systems, and to identify key selection criteria for major IB systems. This study is designed to fill these research gaps. Two surveys: a general survey and the analytic hierarchy process (AHP) survey are proposed to achieve these objectives. The first general survey aims to collect general views from IB experts and practitioners to identify the perceived critical selection criteria, while the AHP survey was conducted to prioritize and assign the important weightings for the perceived criteria in the general survey. Results generally suggest that each IB system was determined by a disparate set of selection criteria with different weightings. ‘Work efficiency’ is perceived to be most important core selection criterion for various IB systems, while ‘user comfort’, ‘safety’ and ‘cost effectiveness’ are also considered to be significant. Two sub-criteria, ‘reliability’ and ‘operating and maintenance costs’, are regarded as prime factors to be considered in selecting IB systems. The current study contributes to the industry and IB research in at least two aspects. First, it widens the understanding of the selection criteria, as well as their degree of importance, of the IB systems. It also adopts a multi-criteria AHP approach which is a new method to analyse and select the building systems in IB. Further research would investigate the inter-relationship amongst the selection criteria.

Case studies on vibration based damage identification : multi-criteria approach

Vibration based damage identification methods examine the changes in primary modal parameters or quantities derived from modal parameters. As one method may have advantages over the other under some circumstances, a multi-criteria approach is proposed. Case studies are conducted separately on beam, plate and plate-on-beam structures. Using the numerically simulated modal data obtained through finite element analysis software, algorithms based on flexibility and strain energy changes before and after damage are obtained and used as the indices for the assessment of the state of structural health. Results show that the proposed multi-criteria method is effective in damage identification in these structures.

Re-valuing construction through project delivery

In moving from lowest cost adversarial based traditional procurement towards value driven methodologies the challenges range from re-engineering the process, to metrics and team alignment. This paper describes research into methodologies which encourage alignment of project partners towards achieving mutually beneficial goals. The research identifies nine variables which influence the achievement of successful projects delivering value. Results from case studies illustrate that not all parties can achieve value for themselves which directs attention to the balance between deliverables and the interests of team members. Re- valuing construction demands refocusing towards the delivery of operational assets and their place in the value system whilst recognising the need to manage the delivery process and the team to align the value to the parties. The objective of the project was to develop tools and recommendations for reform of project delivery in the building and construction industry to transform business-as-usual performance into exceptional performance. Benefits flow not only to the construction industry, but to the community as a whole because a more sophisticated industry can deliver more effective use of assets, financing, operating and maintenance of facilities to suit the community’s needs. This research was funded by the Australian Cooperative Research Centre for Construction Innovation.

Multi-criteria ranking and source apportionment of fine particulate matter in Brisbane, Australia

This paper reports the application of multicriteria decision making techniques, PROMETHEE and GAIA, and receptor models, PCA/APCS and PMF, to data from an air monitoring site located on the campus of Queensland University of Technology in Brisbane, Australia and operated by Queensland Environmental Protection Agency (QEPA). The data consisted of the concentrations of 21 chemical species and meteorological data collected between 1995 and 2003. PROMETHEE/GAIA separated the samples into those collected when leaded and unleaded petrol were used to power vehicles in the region. The number and source profiles of the factors obtained from PCA/APCS and PMF analyses were compared. There are noticeable differences in the outcomes possibly because of the non-negative constraints imposed on the PMF analysis. While PCA/APCS identified 6 sources, PMF reduced the data to 9 factors. Each factor had distinctive compositions that suggested that motor vehicle emissions, controlled burning of forests, secondary sulphate, sea salt and road dust/soil were the most important sources of fine particulate matter at the site. The most plausible locations of the sources were identified by combining the results obtained from the receptor models with meteorological data. The study demonstrated the potential benefits of combining results from multi-criteria decision making analysis with those from receptor models in order to gain insights into information that could enhance the development of air pollution control measures.

Using information criteria to select the correct variance–covariance structure for longitudinal data in ecology

1. Ecological data sets often use clustered measurements or use repeated sampling in a longitudinal design. Choosing the correct covariance structure is an important step in the analysis of such data, as the covariance describes the degree of similarity among the repeated observations. 2. Three methods for choosing the covariance are: the Akaike information criterion (AIC), the quasi-information criterion (QIC), and the deviance information criterion (DIC). We compared the methods using a simulation study and using a data set that explored effects of forest fragmentation on avian species richness over 15 years. 3. The overall success was 80.6% for the AIC, 29.4% for the QIC and 81.6% for the DIC. For the forest fragmentation study the AIC and DIC selected the unstructured covariance, whereas the QIC selected the simpler autoregressive covariance. Graphical diagnostics suggested that the unstructured covariance was probably correct. 4. We recommend using DIC for selecting the correct covariance structure.

Valuing brand equity of a geographic region

There is much anecdotal evidence and academic argument that the location of a business influences its value. That is, some businesses appear to be worth more than others because of their location. This is particularly so in the tourism industry. Within the domain of the destination literature, many factors can be posited on why business valuation varies, ranging from access to markets, availability of labor, climate, and surrounding services. Given that business value is such a fundamental principle that underpins the viability of the tourist industry through its relationship with pricing, business acquisition, and investment, it is surprising that scant research has sought to quantify the relative premium associated with geographic locations. This study proposes a novel way in which to estimate geographic brand premium. Specifically, the approach translates valuation techniques from financial economics to quantify the incremental value derived from businesses operating in a particular geographic region, and produces a geographic brand premium. The article applies the technique to a well-known tourist destination in Australia, and the results are consistent with a positive value of brand equity in the key industries and are of a plausible order of magnitude. The article carries strong implications for business and tourism operators in terms of valuation, pricing, and investment, but more generally, the approach is potentially useful to local authorities and business associations when deciding how much resource and effort should be devoted to brand protection.