Modeling small-scale dairy farms in central Mexico using multi-criteria programming

Milk supply from Mexican dairy farms does not meet demand and small-scale farms can contribute toward closing the gap. Two multi-criteria programming techniques, goal programming and compromise programming, were used in a study of small-scale dairy farms in central Mexico. To build the goal and compromise programming models, 4 ordinary linear programming models were also developed, which had objective functions to maximize metabolizable energy for milk production, to maximize margin of income over feed costs, to maximize metabolizable protein for milk production, and to minimize purchased feedstuffs. Neither multicriteria approach was significantly better than the other; however, by applying both models it was possible to perform a more comprehensive analysis of these small-scale dairy systems. The multi-criteria programming models affirm findings from previous work and suggest that a forage strategy based on alfalfa, rye-grass, and corn silage would meet nutrient requirements of the herd. Both models suggested that there is an economic advantage in rescheduling the calving season to the second and third calendar quarters to better synchronize higher demand for nutrients with the period of high forage availability.

Deconstruction plan assessment using multi-criteria decision-making methods

The purpose of this paper is to present two multi-criteria decision-making models, including an Analytic Hierarchy Process (AHP) model and an Analytic Network Process (ANP) model for the assessment of deconstruction plans and to make a comparison between the two models with an experimental case study. Deconstruction planning is under pressure to reduce operation costs, adverse environmental impacts and duration, in the meanwhile to improve productivity and safety in accordance with structure characteristics, site conditions and past experiences. To achieve these targets in deconstruction projects, there is an impending need to develop a formal procedure for contractors to select a most appropriate deconstruction plan. Because numbers of factors influence the selection of deconstruction techniques, engineers definitely need effective tools to conduct the selection process. In this regard, multi-criteria decision-making methods such as AHP have been adopted to effectively support deconstruction technique selection in previous researches. in which it has been proved that AHP method can help decision-makers to make informed decisions on deconstruction technique selection based on a sound technical framework. In this paper, the authors present the application and comparison of two decision-making models including the AHP model and the ANP model for deconstruction plan assessment. The paper concludes that both AHP and ANP are viable and capable tools for deconstruction plan assessment under the same set of evaluation criteria. However, although the ANP can measure relationship among selection criteria and their sub-criteria, which is normally ignored in the AHP, the authors also indicate that whether the ANP model can provide a more accurate result should be examined in further research.

A fuzzy multi-criteria decision approach for enhanced auto-tracking of seismic events

The main activity carried out by the geophysicist when interpreting seismic data, in terms of both importance and time spent is tracking (or picking) seismic events. in practice, this activity turns out to be rather challenging, particularly when the targeted event is interrupted by discontinuities such as geological faults or exhibits lateral changes in seismic character. In recent years, several automated schemes, known as auto-trackers, have been developed to assist the interpreter in this tedious and time-consuming task. The automatic tracking tool available in modem interpretation software packages often employs artificial neural networks (ANN's) to identify seismic picks belonging to target events through a pattern recognition process. The ability of ANNs to track horizons across discontinuities largely depends on how reliably data patterns characterise these horizons. While seismic attributes are commonly used to characterise amplitude peaks forming a seismic horizon, some researchers in the field claim that inherent seismic information is lost in the attribute extraction process and advocate instead the use of raw data (amplitude samples). This paper investigates the performance of ANNs using either characterisation methods, and demonstrates how the complementarity of both seismic attributes and raw data can be exploited in conjunction with other geological information in a fuzzy inference system (FIS) to achieve an enhanced auto-tracking performance.

Multi-criteria analysis and the resolution of sustainable development dilemmas: a stakeholder management approach

We demonstrate that stakeholder-oriented multi-criteria analysis (MCA) can adequately address a variety of sustainable development dilemmas in decision-making, especially when applied to complex project evaluations involving multiple objectives and multiple stakeholder groups. Such evaluations are typically geared towards satisfying simultaneously private economic goals, broader social objectives and environmental targets. We show that, under specific conditions, a variety of stakeholder-oriented MCA approaches may be able to contribute substantively to the resolution or improved governance of societal conflicts and the pursuit of the public good in the form of sustainable development. We contrast the potential usefulness of these stakeholder-oriented approaches – in terms of their ability to contribute to sustainable development – with more conventional MCA approaches and social cost–benefit analysis.

Key performance indicators (KPIs) and priority setting in using the multi-attribute approach for assessing sustainable intelligent buildings

The main objectives of this paper are to: firstly, identify key issues related to sustainable intelligent buildings (environmental, social, economic and technological factors); develop a conceptual model for the selection of the appropriate KPIs; secondly, test critically stakeholder's perceptions and values of selected KPIs intelligent buildings; and thirdly develop a new model for measuring the level of sustainability for sustainable intelligent buildings. This paper uses a consensus-based model (Sustainable Built Environment Tool- SuBETool), which is analysed using the analytical hierarchical process (AHP) for multi-criteria decision-making. The use of the multi-attribute model for priority setting in the sustainability assessment of intelligent buildings is introduced. The paper commences by reviewing the literature on sustainable intelligent buildings research and presents a pilot-study investigating the problems of complexity and subjectivity. This study is based upon a survey perceptions held by selected stakeholders and the value they attribute to selected KPIs. It is argued that the benefit of the new proposed model (SuBETool) is a ‘tool’ for ‘comparative’ rather than an absolute measurement. It has the potential to provide useful lessons from current sustainability assessment methods for strategic future of sustainable intelligent buildings in order to improve a building's performance and to deliver objective outcomes. Findings of this survey enrich the field of intelligent buildings in two ways. Firstly, it gives a detailed insight into the selection of sustainable building indicators, as well as their degree of importance. Secondly, it tesst critically stakeholder's perceptions and values of selected KPIs intelligent buildings. It is concluded that the priority levels for selected criteria is largely dependent on the integrated design team, which includes the client, architects, engineers and facilities managers.

A knowledge-based evolution strategy for the multi-objective minimum spanning tree problem

A fast Knowledge-based Evolution Strategy, KES, for the multi-objective minimum spanning tree, is presented. The proposed algorithm is validated, for the bi-objective case, with an exhaustive search for small problems (4-10 nodes), and compared with a deterministic algorithm, EPDA and NSGA-II for larger problems (up to 100 nodes) using benchmark hard instances. Experimental results show that KES finds the true Pareto fronts for small instances of the problem and calculates good approximation Pareto sets for larger instances tested. It is shown that the fronts calculated by YES are superior to NSGA-II fronts and almost as good as those established by EPDA. KES is designed to be scalable to multi-objective problems and fast due to its small complexity.

Evaluation of agri-environmental and forestry schemes with multiple objectives

The evaluation of EU policy in the area of rural land use management often encounters problems of multiple and poorly articulated objectives. Agri-environmental policy has a range of aims, including natural resource protection, biodiversity conservation and the protection and enhancement of landscape quality. Forestry policy, in addition to production and environmental objectives, increasingly has social aims, including enhancement of human health and wellbeing, lifelong learning, and the cultural and amenity value of the landscape. Many of these aims are intangible, making them hard to define and quantify. This article describes two approaches for dealing with such situations, both of which rely on substantial participation by stakeholders. The first is the Agri-Environment Footprint Index, a form of multi-criteria participatory approach. The other, applied here to forestry, has been the development of ‘multi-purpose’ approaches to evaluation, which respond to the diverse needs of stakeholders through the use of mixed methods and a broad suite of indicators, selected through a participatory process. Each makes use of case studies and involves stakeholders in the evaluation process, thereby enhancing their commitment to the programmes and increasing their sustainability. Both also demonstrate more ‘holistic’ approaches to evaluation than the formal methods prescribed in the EU Common Monitoring and Evaluation Framework.

Building synergies in development research: The application of the mosaic model to animal health research

Development research has responded to a number of charges over the past few decades. For example, when traditional research was accused of being 'top-down', the response was participatory research, linking the 'receptors' to the generators of research. As participatory processes were recognised as producing limited outcomes, the demand-led agenda was born. In response to the alleged failure of research to deliver its products, the 'joined-up' model, which links research with the private sector, has become popular. However, using examples from animal-health research, this article demonstrates that all the aforementioned approaches are seriously limited in their attempts to generate outputs to address the multi-faceted problems facing the poor. The article outlines a new approach to research: the Mosaic Model. By combining different knowledge forms, and focusing on existing gaps, the model aims to bridge basic and applied findings to enhance the efficiency and value of research, past, present, and future.

Assessing the environmental performance of English arable and livestock holdings using data from the Farm Accountancy Data Network (FADN)

Agri-environment schemes (AESs) have been implemented across EU member states in an attempt to reconcile agricultural production methods with protection of the environment and maintenance of the countryside. To determine the extent to which such policy objectives are being fulfilled, participating countries are obliged to monitor and evaluate the environmental, agricultural and socio-economic impacts of their AESs. However, few evaluations measure precise environmental outcomes and critically, there are no agreed methodologies to evaluate the benefits of particular agri-environmental measures, or to track the environmental consequences of changing agricultural practices. In response to these issues, the Agri-Environmental Footprint project developed a common methodology for assessing the environmental impact of European AES. The Agri-Environmental Footprint Index (AFI) is a farm-level, adaptable methodology that aggregates measurements of agri-environmental indicators based on Multi-Criteria Analysis (MCA) techniques. The method was developed specifically to allow assessment of differences in the environmental performance of farms according to participation in agri-environment schemes. The AFI methodology is constructed so that high values represent good environmental performance. This paper explores the use of the AFI methodology in combination with Farm Business Survey data collected in England for the Farm Accountancy Data Network (FADN), to test whether its use could be extended for the routine surveillance of environmental performance of farming systems using established data sources. Overall, the aim was to measure the environmental impact of three different types of agriculture (arable, lowland livestock and upland livestock) in England and to identify differences in AFI due to participation in agri-environment schemes. However, because farm size, farmer age, level of education and region are also likely to influence the environmental performance of a holding, these factors were also considered. Application of the methodology revealed that only arable holdings participating in agri-environment schemes had a greater environmental performance, although responses differed between regions. Of the other explanatory variables explored, the key factors determining the environmental performance for lowland livestock holdings were farm size, farmer age and level of education. In contrast, the AFI value of upland livestock holdings differed only between regions. The paper demonstrates that the AFI methodology can be used readily with English FADN data and therefore has the potential to be applied more widely to similar data sources routinely collected across the EU-27 in a standardised manner.

The impact of Group Intelligence software on enquiry-based learning

Despite the increasing use of groupware technologies in education, there is little evidence of their impact, especially within an enquiry-based learning (EBL) context. In this paper, we examine the use of a commercial standard Group Intelligence software called GroupSystems®ThinkTank. To date, ThinkTank has been adopted mainly in the USA and supports teams in generating ideas, categorising, prioritising, voting and multi-criteria decision-making and automatically generates a report at the end of each session. The software was used by students carrying out an EBL project, set by employers, for a full academic year. The criteria for assessing the impact of ThinkTank on student learning were those of creativity, participation, productivity, engagement and understanding. Data was collected throughout the year using a combination of interviews and questionnaires, and written feedback from employers. The overall findings show an increase in levels of productivity and creativity, evidence of a deeper understanding of their work but some variation in attitudes towards participation in the early stages of the project.

Sustainable urban metabolism as a link between bio-physical sciences and urban planning: the BRIDGE project

Urban metabolism considers a city as a system with flows of energy and material between it and the environment. Recent advances in bio-physical sciences provide methods and models to estimate local scale energy, water, carbon and pollutant fluxes. However, good communication is required to provide this new knowledge and its implications to endusers (such as urban planners, architects and engineers). The FP7 project BRIDGE (sustainaBle uRban plannIng Decision support accountinG for urban mEtabolism) aimed to address this gap by illustrating the advantages of considering these issues in urban planning. The BRIDGE Decision Support System (DSS) aids the evaluation of the sustainability of urban planning interventions. The Multi Criteria Analysis approach adopted provides a method to cope with the complexity of urban metabolism. In consultation with targeted end-users, objectives were defined in relation to the interactions between the environmental elements (fluxes of energy, water, carbon and pollutants) and socioeconomic components (investment costs, housing, employment, etc.) of urban sustainability. The tool was tested in five case study cities: Helsinki, Athens, London, Florence and Gliwice; and sub-models were evaluated using flux data selected. This overview of the BRIDGE project covers the methods and tools used to measure and model the physical flows, the selected set of sustainability indicators, the methodological framework for evaluating urban planning alternatives and the resulting DSS prototype.

Sustainability assessment indicators and methodology for intelligent buildings

The sustainable intelligent building is a building that has the best combination of environmental, social, economic and technical values. And its sustainability assessment is related with system engineering methods and multi-criteria decision-making. Therefore firstly, the wireless monitoring system of sustainable parameters for intelligent buildings is achieved; secondly, the indicators and key issues based on the “whole life circle” for sustainability of intelligent buildings are researched; thirdly, the sustainable assessment model identified on the structure entropy and fuzzy analytic hierarchy process is proposed.

A hybridised evolutionary algorithm for multi-criterion minimum spanning tree problems

A hybridised and Knowledge-based Evolutionary Algorithm (KEA) is applied to the multi-criterion minimum spanning tree problems. Hybridisation is used across its three phases. In the first phase a deterministic single objective optimization algorithm finds the extreme points of the Pareto front. In the second phase a K-best approach finds the first neighbours of the extreme points, which serve as an elitist parent population to an evolutionary algorithm in the third phase. A knowledge-based mutation operator is applied in each generation to reproduce individuals that are at least as good as the unique parent. The advantages of KEA over previous algorithms include its speed (making it applicable to large real-world problems), its scalability to more than two criteria, and its ability to find both the supported and unsupported optimal solutions.

A multi-model evaluation of aerosols over South Asia: common problems and possible causes

Atmospheric pollution over South Asia attracts special attention due to its effects on regional climate, water cycle and human health. These effects are potentially growing owing to rising trends of anthropogenic aerosol emissions. In this study, the spatio-temporal aerosol distributions over South Asia from seven global aerosol models are evaluated against aerosol retrievals from NASA satellite sensors and ground-based measurements for the period of 2000–2007. Overall, substantial underestimations of aerosol loading over South Asia are found systematically in most model simulations. Averaged over the entire South Asia, the annual mean aerosol optical depth (AOD) is underestimated by a range 15 to 44% across models compared to MISR (Multi-angle Imaging SpectroRadiometer), which is the lowest bound among various satellite AOD retrievals (from MISR, SeaWiFS (Sea-Viewing Wide Field-of-View Sensor), MODIS (Moderate Resolution Imaging Spectroradiometer) Aqua and Terra). In particular during the post-monsoon and wintertime periods (i.e., October–January), when agricultural waste burning and anthropogenic emissions dominate, models fail to capture AOD and aerosol absorption optical depth (AAOD) over the Indo–Gangetic Plain (IGP) compared to ground-based Aerosol Robotic Network (AERONET) sunphotometer measurements. The underestimations of aerosol loading in models generally occur in the lower troposphere (below 2 km) based on the comparisons of aerosol extinction profiles calculated by the models with those from Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) data. Furthermore, surface concentrations of all aerosol components (sulfate, nitrate, organic aerosol (OA) and black carbon (BC)) from the models are found much lower than in situ measurements in winter. Several possible causes for these common problems of underestimating aerosols in models during the post-monsoon and wintertime periods are identified: the aerosol hygroscopic growth and formation of secondary inorganic aerosol are suppressed in the models because relative humidity (RH) is biased far too low in the boundary layer and thus foggy conditions are poorly represented in current models, the nitrate aerosol is either missing or inadequately accounted for, and emissions from agricultural waste burning and biofuel usage are too low in the emission inventories. These common problems and possible causes found in multiple models point out directions for future model improvements in this important region.

Supporting multi-organisational collaborative groups in the public sector

In this paper, the issues that arise in multi-organisational collaborative groups (MOCGs) in the public sector are discussed and how a technology-based group support system (GSS) could assist individuals within these groups. MOCGs are commonly used in the public sector to find solutions to multifaceted social problems. Finding solutions for such problems is difficult because their scope is outside the boundary of a single government agency. The standard approach to solving such problems is collaborative involving a diverse range of stakeholders. Collaborative working can be advantageous but it also introduces its own pressures. Conflicts can arise due to the multiple contexts and goals of group members and the organisations that they represent. Trust, communication and a shared interface are crucial to making any significant progress. A GSS could support these elements.