A soft computing framework to support consumers in obtaining sustainable appliances from the market

Currently, sustainability is considered a priority by society, with the household appliances being one of the economic sectors involved in achieving sustainability. However, the existence of several issues (e.g., energy and water consumption, reliability, initial cost, and illuminance, among others) together with the diversity of brands and models on the market, make the consumer’s decisions regarding sustainable options difficult, according to their concerns and related to each sustainability dimension (economic, environmental, and social). By combining evolutionary algorithms (EA) with multicriteria techniques, it is possible to achieve sustainable solutions for the consumer based on their requirements. In this paper, a method is presented to support the consumer by obtaining a set of sustainable household appliances on the market that suit their preferences, concerns, and needs. By using a case study to apply the approach developed here, a set of sustainable appliances from the market is obtained, where several benefits are achieved (e.g., energy and water consumption savings, avoidance of CO2 emissions) during the lifecycle of each appliance, chosen from the appliance’s industry.info:eu-repo/semantics/publishedVersio

Modelo de suporte à tomada de decisão para a escolha sustentável de equipamentos

Nos últimos anos, têm-se assistido a um aumento generalizado da procura de energia a nível mundial, devido em grande parte ao aumento do consumo de energia elétrica final, comprometendo, na sua generalidade, o desenvolvimento sustentável, através do consumo de recursos e do aumento das emissões de gases de efeito de estufa (GEE). Neste sentido, a incorporação de medidas de sustentabilidade constitui um importante contributo para a redução das emissões de GEE para a atmosfera, sendo o sector dos edifícios uma área com especial relevância em termos de atuação neste domínio. Assim, uma das formas de promover a sustentabilidade, poderá ser através da escolha racional dos equipamentos utilizados atualmente neste sector, e no âmbito dos serviços energéticos a adquirir, aliada a medidas de eficiência energética. Deste modo, neste trabalho desenvolve-se uma metodologia de suporte à decisão, que permita ao consumidor tomar decisões que promovam a sustentabilidade, com base em soluções existentes no mercado. Para tal, recorre-se a abordagens multicritério, combinadas com técnicas de otimização multiobjectivo, sendas estas, por seu turno, suportadas em algoritmos evolucionários. Assim, o objetivo é obter soluções sustentáveis, que maximizem os três vetores da sustentabilidade: benefício económico, responsabilidade ambiental e conforto social do consumidor, respeitando naturalmente um conjunto de restrições associadas, sejam as mesmas económicas (ex. valores orçamentais máximos), sociais (ex. níveis mínimos de conforto) e até mesmo ambientais (ex. níveis máximos de emissões de CO2 equivalentes). Por outro lado, serão igualmente tidos em conta fatores como o ciclo de vida do produto, investimento realizado, etiquetagem energética, preferências do consumidor, pegada de carbono, entre outros. Dada a elevada natureza combinatória do problema, e apesar de se poderem empregar métodos de otimização clássicos, os algoritmos evolucionários tem assumido, cada vez mais, um maior destaque na resolução de problemas deste tipo, demonstrando bom desempenho na sua resolução, tendo ainda a vantagem de apresentar diversas soluções ótimas para o mesmo problema e em menor período de tempo. Pretende-se deste modo contribuir para a problemática identificada, através de uma abordagem holística, que permita ao agente-decisor tomar decisões sustentáveis, dado o contexto descrito anteriormente.Lately, there has been a generalized increase in world energy demand, due in large part to the increase in final electric energy consumption, generally compromising sustainable development, through consumption of resources and emissions of greenhouse gases (GHG). In this sense, the incorporation of sustainability measures is an important contribution to the reduction of GHG emissions to the atmosphere, and the building sector is an area of relevance in terms of its performance in this area. Thus, one of the ways to promote sustainability can be through the rational choice of equipment currently used in this sector, and in the scope of energy services to be acquired, combined with energy efficiency measures. In this way, this work develops a decision support methodology that allows the consumer to make decisions that promote sustainability, based on existing solutions in the market. For this, multi-criteria approaches are used, combined with multiobjective optimization techniques, these paths, in turn, supported by evolutionary algorithms. The objective is therefore to achieve sustainable solutions that maximize the three vectors of sustainability: economic benefit, environmental responsibility and social comfort of the consumer, while respecting a set of associated constraints, be they economic (eg maximum budgetary values), social (e.g. minimum levels of comfort) and even environmental (eg maximum CO2 equivalent levels). On the other hand, factors such as the product life cycle, investment made, energy labeling, consumer preferences, carbon footprint, among others, will also be considered. Given the high combinatorial nature of the problem, and although classic optimization methods can be used, evolutionary algorithms have increasingly assumed a greater prominence in solving problems of this type, demonstrating good performance in solving, having the advantage of presenting several optimal solutions to the same problem and in a shorter period of time. In this way, it is intended to contribute to the identified problem, through a holistic approach, that allows the decision-maker to make sustainable decisions, given the context described previously.Programa Doutoral em Engenharia e Gestão Industria

A new approach to provide sustainable solutions for residential sector

An energy-efficient appliance normally presents a lower energy con-sumption compared to a less efficient one, with a higher initial investment, alt-hough this not always happens. Additionally, each appliance, presents very dif-ferent features, leading to some difficulties on its choice by the consumer (deci-sion-agent). On the other hand, each consumer, has specific and distinguished needs from other consumers, namely of social, economic or environmental nature. Even by adopting these criteria, this is not an isolated guarantee of an optimal solution for the consumer. It is then necessary to complement this approach with multicriteria, combined with optimization techniques. Evolutionary Algorithms (EA), could be used as an optimization technique, to provide sustainable solutions to the consumer, from the market. In this paper, it’s presented an approach that integrates both concepts, where at the end, it shall be presented a case study, to demonstrate the application of the proposed method.info:eu-repo/semantics/publishedVersio