69 resultados para Engineering Asset Management, Optimisation, Preventive Maintenance, Reliability Based Preventive Maintenance, Multiple Criteria Decision Making


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Management strategies to reduce the risks to human life and property from wildfire commonly involve burning native vegetation. However, planned burning can conflict with other societal objectives such as human health and biodiversity conservation. These conflicts are likely to intensify as fire regimes change under future climates and as growing human populations encroach farther into fire-prone ecosystems. Decisions about managing fire risks are therefore complex and warrant more sophisticated approaches than are typically used. We applied a multicriteria decision making approach (MCDA) with the potential to improve fire management outcomes to the case of a highly populated, biodiverse, and flammable wildland-urban interface. We considered the effects of 22 planned burning options on 8 objectives: house protection, maximizing water quality, minimizing carbon emissions and impacts on human health, and minimizing declines of 5 distinct species types. The MCDA identified a small number of management options (burning forest adjacent to houses) that performed well for most objectives, but not for one species type (arboreal mammal) or for water quality. Although MCDA made the conflict between objectives explicit, resolution of the problem depended on the weighting assigned to each objective. Additive weighting of criteria traded off the arboreal mammal and water quality objectives for other objectives. Multiplicative weighting identified scenarios that avoided poor outcomes for any objective, which is important for avoiding potentially irreversible biodiversity losses. To distinguish reliably among management options, future work should focus on reducing uncertainty in outcomes across a range of objectives. Considering management actions that have more predictable outcomes than landscape fuel management will be important. We found that, where data were adequate, an MCDA can support decision making in the complex and often conflicted area of fire management.

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In group decision making (GDM) problems, it is natural for decision makers (DMs) to provide different preferences and evaluations owing to varying domain knowledge and cultural values. When the number of DMs is large, a higher degree of heterogeneity is expected, and it is difficult to translate heterogeneous information into one unified preference without loss of context. In this aspect, the current GDM models face two main challenges, i.e., handling the complexity pertaining to the unification of heterogeneous information from a large number of DMs, and providing optimal solutions based on unification methods. This paper presents a new consensus-based GDM model to manage heterogeneous information. In the new GDM model, an aggregation of individual priority (AIP)-based aggregation mechanism, which is able to employ flexible methods for deriving each DM's individual priority and to avoid information loss caused by unifying heterogeneous information, is utilized to aggregate the individual preferences. To reach a consensus more efficiently, different revision schemes are employed to reward/penalize the cooperative/non-cooperative DMs, respectively. The temporary collective opinion used to guide the revision process is derived by aggregating only those non-conflicting opinions at each round of revision. In order to measure the consensus in a robust manner, a position-based dissimilarity measure is developed. Compared with the existing GDM models, the proposed GDM model is more effective and flexible in processing heterogeneous information. It can be used to handle different types of information with different degrees of granularity. Six types of information are exemplified in this paper, i.e., ordinal, interval, fuzzy number, linguistic, intuitionistic fuzzy set, and real number. The results indicate that the position-based consensus measure is able to overcome possible distortions of the results in large-scale GDM problems.

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Population models for multiple species provide one of the few means of assessing the impact of alternative management options on the persistence of biodiversity, but
they are inevitably uncertain. Is it possible to use population models in multiple-speciesconservation planning given the associated uncertainties? We use information-gap decision theory to explore the impact of parameter uncertainty on the conservation decision when planning for the persistence of multiple species. An information-gap approach seeks robust outcomes that are most immune from error. We assess the impact of uncertainty in key model parameters for three species, whose extinction risks under four alternative management scenarios are estimated using a metapopulation model. Three methods are described for making conservation decisions across the species, taking into account uncertainty. We find that decisions based on single species are relatively robust to uncertainty in parameters, although the estimates of extinction risk increase rapidly with uncertainty. When identifying the best conservation decision for the persistence of all species, the methods that rely on the rankings of the management options by each species result in decisions that are similarly robust to uncertainty. Methods that depend on absolute values of extinction risk are sensitive to uncertainty, as small changes in extinction risk can alter the ranking of the alternative scenarios. We discover that it is possible to make robust conservation decisions even when the uncertainties of the multiple-species problem appear overwhelming. However, the decision most robust to uncertainty is likely to differ from the best decision when uncertainty is ignored, illustrating the importance of incorporating uncertainty into the decision-making process.

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Extracting knowledge from the transaction records and the personal data of credit card holders has great profit potential for the banking industry. The challenge is to detect/predict bankrupts and to keep and recruit the profitable customers. However, grouping and targeting credit card customers by traditional data-driven mining often does not directly meet the needs of the banking industry, because data-driven mining automatically generates classification outputs that are imprecise, meaningless, and beyond users' control. In this paper, we provide a novel domain-driven classification method that takes advantage of multiple criteria and multiple constraint-level programming for intelligent credit scoring. The method involves credit scoring to produce a set of customers' scores that allows the classification results actionable and controllable by human interaction during the scoring process. Domain knowledge and experts' experience parameters are built into the criteria and constraint functions of mathematical programming and the human and machine conversation is employed to generate an efficient and precise solution. Experiments based on various data sets validated the effectiveness and efficiency of the proposed methods. © 2006 IEEE.

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There is an apparent gap between the LCA or other assessment's outcomes and its effective application in the decision making process. It is needed to provide to the decision makers a simple, less human interfered mechanism that integrates all the key criteria (environmental, economic, technical and safety etc.). The proposed index: Interlink Decision Making Index (IDMI) has all these features: simple, interlink (all criteria) and automatically and quantified influence of critical criteria (ie. no human weighting needed) and is able to assist the multi-criteria decision making for sustainability based on the outcomes of specific assessments (eg. LCA, ElA etc.). The index represents a pure numerical value and does not necessarily have any physical meanings, but it reflects the total merits of a particular option once the normal decision making criteria and (up to two) critical criteria (CC) have been chosen. Then, without arbitrarily weighting process, the comparison and selection of the best possible option, ie. decision can be made based on the derived IDMI results. Two hypothetical examples are presented in part 2 of the paper to demonstrate the application of the IDMI concept and it's differences with the traditional "tabular method" in the decision making process.

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There is an apparent gap between the LCA or other assessment's outcomes and its effective application in the decision making process. It is needed to provide to the decision makers a simple, less human interfered mechanism that integrates all the key criteria (environmental, economic, technical and safety etc.). The proposed index: Interlink Decision Making Index (IDMI) has all these features: simple, interlink (all criteria) and automatically and quantified influence of critical criteria (ie. no human weighting needed) and is able to assist the multi-criteria decision making for sustainability based on the outcomes of specific assessments (eg. LCA, BIA etc.). The index represents a pure numerical value and does not necessarily have any physical meanings, but it reflects the total merits of a particular option once the normal decision making criteria and (up to two) critical criteria (Ce) have been chosen. Then, without arbitrarily weighting process, the comparison and selection of the best possible option, ie. decision can be made based on the derived IDMI results. Two hypothetical examples are presented in the part 2 of the paper to demonstrate the application of the IDMI concept and it's differences with the traditional "tabular method" in the decision making process.

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Provides a synthesis of human rights theory and human services practice and offers a rights based model to aid professional decision making and practice.

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In group decision making (GDM) problems, ordinal data provide a convenient way of articulating preferences from decision makers (DMs). A number of GDM models have been proposed to aggregate such kind of preferences in the literature. However, most of the GDM models that handle ordinal preferences suffer from two drawbacks: (1) it is difficult for the GDM models to manage conflicting opinions, especially with a large number of DMs; and (2) the relationships between the preferences provided by the DMs are neglected, and all DMs are assumed to be of equal importance, therefore causing the aggregated collective preference not an ideal representative of the group's decision. In order to overcome these problems, a two-stage dynamic group decision making method for aggregating ordinal preferences is proposed in this paper. The method consists of two main processes: (i) a data cleansing process, which aims to reduce the influence of conflicting opinions pertaining to the collective decision prior to the aggregation process; as such an effective solution for undertaking large-scale GDM problems is formulated; and (ii) a support degree oriented consensus-reaching process, where the collective preference is aggregated by using the Power Average (PA) operator; as such, the relationships of the arguments being aggregated are taken into consideration (i.e., allowing the values being aggregated to support each other). A new support function for the PA operator to deal with ordinal information is defined based on the dominance-based rough set approach. The proposed GDM model is compared with the models presented by Herrera-Viedma et al. An application related to controlling the degradation of the hydrographic basin of a river in Brazil is evaluated. The results demonstrate the usefulness of the proposed method in handling GDM problems with ordinal information.

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Coordinated systems are required to ensure evidence-informed practice and evaluation of community-based interventions (CBIs). Knowledge translation and exchange (KTE) strategies show promise, but these require evaluation. This paper describes implementation and evaluation of COOPS, a national KTE platform to support best practice in obesity prevention CBIs. A logic model guides KTE activities including knowledge brokering, networking, tailored communications, training, and needs assessments. A mixed-methods evaluation includes communications data, knowledge brokering database, annual survey of CBIs, pre- and post-event questionnaires, interviews, social network analysis, and case studies. This evaluation will contribute to understanding the process of implementing a KTE platform with CBIs and its reach, quality and effectiveness.

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Compression ignition (CI) engine design is subject to many constraints which presents a multi-criteria optimisation problem that the engine researcher must solve. In particular, the modern CI engine must not only be efficient, but must also deliver low gaseous, particulate and life cycle greenhouse gas emissions so that its impact on urban air quality, human health, and global warming are minimised. Consequently, this study undertakes a multi-criteria analysis which seeks to identify alternative fuels, injection technologies and combustion strategies that could potentially satisfy these CI engine design constraints. Three datasets are analysed with the Preference Ranking Organization Method for Enrichment Evaluations and Geometrical Analysis for Interactive Aid (PROMETHEE-GAIA) algorithm to explore the impact of 1): an ethanol fumigation system, 2): alternative fuels (20 % biodiesel and synthetic diesel) and alternative injection technologies (mechanical direct injection and common rail injection), and 3): various biodiesel fuels made from 3 feedstocks (i.e. soy, tallow, and canola) tested at several blend percentages (20-100 %) on the resulting emissions and efficiency profile of the various test engines. The results show that moderate ethanol substitutions (~20 % by energy) at moderate load, high percentage soy blends (60-100 %), and alternative fuels (biodiesel and synthetic diesel) provide an efficiency and emissions profile that yields the most “preferred” solutions to this multi-criteria engine design problem. Further research is, however, required to reduce Reactive Oxygen Species (ROS) emissions with alternative fuels, and to deliver technologies that do not significantly reduce the median diameter of particle emissions.

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The importance of management to the long-term careers of practicing professional engineers has long been recognized. Undergraduate engineering education should therefore provide an exposure to the management skills required by engineers in professional practice. For the rational and effective design of undergraduate engineering management studies, it is essential to understand the nature of engineering management and to identify those management skills identified as important by practicing professional engineers. Through an investigation of the recent literature, the management skills considered important by engineering stakeholder groups are identified and ranked. This information is supplemented by recent surveys conducted by the author of stakeholders in Australia, including academic staff, mature age undergraduate students, and recent graduates of the engineering programs at Deakin University in Australia. Based on an examination of the literature and original research, a framework of ranked classified management skills is proposed. Broadly, the ranking framework is generic professional skills first, followed by general management skills and technical discipline specific management skills, followed by other professional discipline skills and theoretical skills.

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Challenges the existing normal science in project management with its limitations of high certainty in scope definition and develops a contingency based model for project / program management (PROJAM). The PROJAM concept is theoretically supported through the identification of eight inter related aspects - uncertainty, maturity, stakeholders, pareto, reward, transparency, partnering, and program management. The ideas presented represent a radical change to the eisting project management body of knowledge and expand the scope of project management to deal with complex projects / programs including organisational change, core function outsourcing (asset management) and IT hardware and software outsourcing.

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Although information communication technology (ICT) is long regard as very useful tool in today’s construction engineering and project management environment, organizations must not only operate based upon its original setting, but also requires on-going observation, additional features and fine is a very common phenomenon that organizations purchase the licensed “off their own business need. Due to the incapability of such software and inefficient customization, the possible result is making that ICT tool not user-friendly and sometimes the whole system becomes obsolete.

The purpose of this paper is to review and report those action organization to enhance the performance of its Enterprise Resource Planning (ERP) system launched in December 2002. Such actions include: improving data inputting method; removing the tran the organization’s “Delegation and Limits of Authority”; publishing the “League Table” amongst users; integrating the 3D Mode into the system and upgrading hardware.

Whilst the ultimate goals of such system are well beyond the time limit of this research study, an obvious interim result, achieved by this case studied organization, was winning a landmark project worth US$500 million after the ERP system was functioned prope and effectively. Their experience and success becomes an exemplar which can be borrowed by those companies, from managerial perspectives and as a roadmap, planning to adopt information technology (IT) strategy and use ICT tool in the construction engineering and project management framework. Singapore, where public housing provisions have been a major concern of their citizens as the building stock gets older.

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The modern disciplines of engineering and management are inextricably linked. Frederick Taylor, Henry Gantt and Henri Fayol are engineers whose names are also part of the history of the theory and practice of management. As far back as 1968 it was identified that, “In all phases of practice in the profession the technical work is coupled, to a greater or lesser extent, with engineering management.” For more than 20 years the call had been increasing for an improvement in the preparation of engineering graduates in the area of management skills. In 1989 the IEAust created the task force on management engineering with the goal of formulating a policy for management education in engineering undergraduate courses. In 1990, the Council of the IEAust approved the Policy on Management Studies in Engineering Undergraduate Courses that said, “From January 1991 the Institution will require at least 5% management content in all professional engineering undergraduate courses and that the total of all management and management related components rises to the vicinity of 10% by 1995.” A 1999 analysis of engineering programs showed that the Policy had been applied with enthusiasm by about one-third of the engineering schools, fairly well in another third, remaining responses were ineffectual. Around the same time, revisions to the IEAust accreditation requirements de-emphasised the importance of management studies, mentioning it only as a subset of ‘professional practice’. By 2004 the IEAust stage 1 competency standards for professional engineers mentioned ‘management’ in only three of 79 indicators of competency. In 2002, the IEAust established the Centre for Engineering Leadership and Management. In December 2005 CELM established a working group, “…for improving the business and management content of undergraduate courses. It appears that it’s back (about 20 years) to the future for Australian undergraduate engineering management education.