Carlos Henggeler Antunes

Multiple objective linear programming models with interval coefficients – an illustrated overview

In most real-world situations, the coefficients of decision support models are not exactly known. In this context, it is convenient to consider an extension of traditional mathematical programming models incorporating their intrinsic uncertainty, without assuming the exactness of the model coefficients. Interval programming is one of the tools to tackle uncertainty in mathematical programming models. Moreover, most real-world problems inherently impose the need to consider multiple, conflicting and incommensurate objective functions. This paper provides an illustrated overview of the state of the art of Interval Programming in the context of multiple objective linear programming models.

A fuzzy multiple objective decision support model for energy-economy planning

Abstract In this paper an interactive approach to deal with fuzzy multiple objective linear programming problems is presented, which is based on the analysis of the decomposition of the parametric (weight) diagram into indifference regions corresponding to basic efficient solutions. This approach is illustrated to tackle uncertainty and imprecision associated with the coefficients of an input–output energy-economy planning model, aimed at providing decision support to decision makers in the study of the interactions between the energy system and the economy on a national level.

Assessing the performance of biogas plants with multi-criteria and data envelopment analysis

This paper performs an assessment of 41 agricultural biogas plants located in Austria to determine their relative performance in terms of economic, environmental, and social criteria and corresponding indicators. The comparison of these renewable energy conversion plants is based on two complementary analyses. Data envelopment analysis (DEA) was conducted to provide measures of radial efficiency relative to the observed frontier of production possibilities. Multi-criteria decision analysis (MCDA), using the IRIS/ELECTRE TRI methodology, was conducted to obtain a different perspective on the results, and as a tool that would enable to incorporate managerial preferences easily. To be able to use IRIS while keeping the spirit behind DEA, the evaluation criteria were defined as different output/input efficiency ratios, and no information about criteria weights was introduced at the outset. The results suggest that MCDA, and the use of IRIS in particular, constitutes a useful approach that can be applied in a complementary way to DEA.

A GIS-based multicriteria spatial decision support system for planning urban infrastructures

The planning of urban infrastructures has important spatial implications. The evaluation of alternative courses of action in this setting requires the explicit consideration of multiple criteria as they have important social, economic, and environmental effects. This paper presents a decision support system aimed at offering the users (e.g., government or municipal agencies) a flexible and user-friendly environment to provide decision aid in urban infrastructure planning. The visualization of available alternatives on maps provides a value-added for decision support processes in urban infrastructure evaluation problems. The development of this system has been motivated by a real world urban case study.

A multiple objective model to deal with economy-energy-environment interactions

Abstract An economy–energy–environment multiple objective model based on the linear structure of inter-industry production linkages is presented. Axes of evaluation consistent with sustainable energy strategies, economic growth, social welfare and environmental friendliness are explicitly considered. The aim of this study is to provide decision-makers with a comprehensive model which allows to assess environmental burdens (global warming potential and acidification potential) with respect to changes in economic activities consistent with distinct policy measures.

A Multiple Objective Approach to Direct Load Control Using an Interactive Evolutionary Algorithm

This paper describes the use of an interactive evolutionary algorithm for the identification and selection of direct load control actions in electrical distribution networks. The evolutionary algorithm accommodates a progressive articulation of the decision makers preferences by changing aspiration or reservation levels used in the fitness assessment of the individuals in the population (load control strategies). Genetic operators have revealed as an adequate way to supply the evolutionary process with relevant information about the search results. Besides contributing to reduce the scope of the search, and thus the computer effort, this also enables the identification of solutions more in accordance with the decision makers evolving preferences.

A multicriteria decision support system for housing evaluation

Economic and population growth, higher life quality standards, and lower interest rates have led to an increase in the demand for housing. However, in many urban areas, land for housing is becoming scarce, and environmental and construction requirements more stringent. Therefore, the need arises for an adequate methodology to evaluate the urban built space under different perspectives (consumers, promoters, municipal authorities, etc.) and multiple evaluation criteria. A decision support system for housing evaluation is presented in this paper. It integrates a problem editor, a data base management module, a set of multiple criteria decision aid methods and an adequate Human-computer interface, which can be integrated with GIS tools.

MCDA and Energy Planning

The growing environmental awareness and the apparent conflict between economic and environmental objectives was the main impetus that pushed energy planners during the early eighties towards the use of MCDA methods. Thereafter, the rapid changes and the increasing complexity of the energy market gave rise to further methodological developments. Although the energy market restructuring and ongoing liberalization seemed to restrict the purpose for centralized energy decisions, they added new dimensions in energy planning. Increasing competition along with the prerequisite for sustainability have broadened the energy application field by bringing out new challenges for the development of integrated multicriteria and multi-stakeholders approaches also taking uncertainty into consideration. This paper aims at illustrating the evolution of MCDA approaches, in the context of the emerging problems faced by energy planners and other stakeholders involved in energy-related decision situations, one of the most active and exciting areas of application of MCDA models and methods.

A multiple objective evolutionary approach for the design and selection of load control strategies

Load management activities, even in scenarios characterized by an unbundled electricity market, maintain their potential attractiveness, not just due to operational issues but also because of potential economic benefits. However, multiple incommensurate and conflicting objectives are at stake in the design and selection of load management actions. Evolutionary algorithms, working with a population of potential solutions, are well suited for such multiobjective optimization problems of combinatorial nature. Moreover, when applied to load management programs, they allow both for the design and the selection of control strategies. The combined use of this type of algorithms and adequate load models allows some of the concerns these actions may arise, such as the payback phenomenon, to be taken into account. In the proposed approach, the effects of load control strategies are computed at different demand aggregation levels. This capability and the explicit consideration of multiple objective functions in the mathematical model enable the proposed approach to be used in different possible scenarios related with power systems structure and by different entities.

A multiple objective decision support model for the selection of remote load control strategies

Utilities frequently use remote load control as an effective means to achieve suitable network operational conditions. This procedure, usually designated load management (LM), is a part of the resources considered under the general designation of demand-side management (DSM). However, the use of LM in electric distribution network management is a problem that involves different conflicting aspects such as reducing peak demand, maximizing utility profits and minimizing discomfort caused to consumers. Hence, the problem is multiobjective in nature: economical, technical and quality of service aspects must all be explicitly accounted for in mathematical models. This paper presents a multiobjective decision support model which allows the consideration of the main concerns that have an important role in LM: minimize peak demand as perceived by the distribution network dispatch center, maximize utility profit corresponding to the energy services delivered by the controlled loads, maximize quality of service in the context of LM.