Multi-Criteria versus Data Envelopment Analysis for Assessing the Performance of Biogas Plants

This paper compares multi-criteria decision aiding (MCDA) and data envelopment analysis (DEA) approaches for assessing renewable energy plants, in order to determine their performance in terms of economic, environmental, and social criteria and indicators. The case is for a dataset of 41 agricultural biogas plants in Austria using anaerobic digestion. The results indicate that MCDA constitutes an insightful approach, to be used alternatively or in a complementary way to DEA, namely in situations requiring a meaningful expression of managerial preferences regarding the relative importance of evaluation aspects to be considered in performance assessment.Multi-criteria decision analysis; DEA; Renewable energy; Biogas

Managing uncertainty in decision support models foreword to the special issue

http://www.sciencedirect.com/science/article/B6V8S-4KGPP34-2/1/b8b0563410520cbad9402d23e6ee42e

A weight space-based approach to fuzzy multiple-objective linear programming

In this paper, the effects of uncertainty on multiple-objective linear programming models are studied using the concepts of fuzzy set theory. The proposed interactive decision support system is based on the interactive exploration of the weight space. The comparative analysis of indifference regions on the various weight spaces (which vary according to intervals of values of the satisfaction degree of objective functions and constraints) enables to study the stability and evolution of the basis that correspond to the calculated efficient solutions with changes of some model parameters.http://www.sciencedirect.com/science/article/B6V8S-45S9DHF-2/1/f597062363c29e9bb464a6ba6f21f0d

Managing uncertainty in decision support models foreword to the special issue

http://www.sciencedirect.com/science/article/B6V8S-4KGPP34-2/1/b8b0563410520cbad9402d23e6ee42e

A web spatial decision support system for vehicle routing using Google Maps

This article presents a user-friendly web-based Spatial Decision Support System (wSDSS) aimed at generating optimized vehicle routes for multiple vehicle routing problems that involve serving the demand located along arcs of a transportation network. The wSDSS incorporates Google Maps™ (cartography and network data), a database, a heuristic and an ant-colony meta-heuristic developed by the authors to generate routes and detailed individual vehicle route maps. It accommodates realistic system specifics, such as vehicle capacity and shift time constraints, as well as network constraints such as one-way streets and prohibited turns. The wSDSS can be used for “what-if” analysis related to possible changes to input parameters such as vehicle capacity, maximum driving shift time, seasonal variations of demand, network modifications, imposed arc orientations, etc. Since just a web browser is needed, it can be easily adapted to be widely used in many real-world situations. The system was tested for urban trash collection in Coimbra, Portugal

Using SSM for structuring decision support in urban energy planning

This paper describes the use of Soft Systems Methodology (SSM) as a tool for problem structuring, which is the first phase encompassed in a methodological approach currently under development to provide decision support based on Multi‐Criteria Decision Analysis (MCDA) in energy planning problems in an urban context. In order to apply the methodology to a real‐world problem, a medium sized Portuguese city has been chosen as the decision setting. SSM is used for characterizing as precisely as possible the decision problem context, identifying the main stakeholders and their relations, and discerning the relevant criteria at stake for each one. Future work directions based on this phase are also envisaged. Santrauka Straipsnyje aprašoma operacinės sistemos metodologija (OSM), kuri bus taikoma kaip daugiakriterinės analizės metodais pagrįsta sprendimų paramos sistema miesto energetikos planavimo problemoms spręsti. Siekiant metodologiją pritaikyti realiame gyvenime, eksperimentui buvo parinktas vidutinio dydžio Portugalijos miestas. Operacines sistemos metodologija taikyta kuo tiksliau nustatant pagrindines problemas, identifikuojant pagrindines suinteresuotas šalis ir jų santykius, nustatant vienas kitam įtaka darančius rodiklius. Numatytos būsimos darbo kryptys. First published online: 10 Feb 2011 Reikšminiai žodžiai: operacinė sistemos metodologija, daugiakriterinė sprendimų analizė, miesto energetikos planavima

Multi-objective optimization for building retrofit: a model using genetic algorithm and artificial neural network and an application

Retrofitting of existing buildings offers significant opportunities for improving occupants’ comfort and well-being, reducing global energy consumption and greenhouse gas emissions. This is being considered as one of the main approaches to achieve sustainability in the built environment at relatively low cost and high uptake rates. Although a wide range of retrofit technologies is readily available, methods to identify the most suitable set of retrofit actions for particular projects are still a major technical and methodological challenge. This paper presents a multi-objective optimization model using genetic algorithm (GA) and artificial neural network (ANN) to quantitatively assess technology choices in a building retrofit project. This model combines the rapidity of evaluation of ANNs with the optimization power of GAs. A school building is used as a case study to demonstrate the practicability of the proposed approach and highlight potential problems that may arise. The study starts with the individual optimization of objective functions focusing on building's characteristics and performance: energy consumption, retrofit cost, and thermal discomfort hours. Then a multi-objective optimization model is developed to study the interaction between these conflicting objectives and assess their trade-offs

A multi-objective input-output model to assess E4 impacts of building retrofitting measures to improve energy efficiency

This paper develops a bottom-up approach in the scope of a multi-objective linear programming model (MOLP) based on Input-Output (I-O) analysis to account for investment options aimed at improving the thermal properties of building envelope (e.g., the insulation of external walls and roof, and the replacement of window frames and window glazing). This methodological framework aims at assessing the trade-offs between the overall employment, GDP and energy savings associated with the building sector (residential, private services and public services). Distinct impacts, namely on direct and indirect employment generation, environment (CO2 emissions), energy security supply (energy imports and renewable energy production) and other relevant economic indicators are also analysed. Different sets of input parameters for the economic context and the environmental impacts have been defined as interval coefficients to account for uncertainty. Robust solutions are then obtained by considering the minimisation of the worst possible deviation of the interval objective functions to the corresponding interval ideal solutions

Using SSM to rethink the analysis of energy efficiency initiatives

This paper reflects an attempt to rethink the process of analysis of energy efficiency initiatives using soft systems methodology (SSM) as a problem structuring tool. The aim of the work is to provide public and private initiative promoters or evaluators with a structured support for a more informed decision regarding the implementation of energy efficiency measures. The SSM approach contributed with the identification of all market players and their relations, as well as the insight into the deficiencies of current methodologies. Some future work directions are also proposed.info:eu-repo/semantics/publishedVersio