A Decision Support System for Prioritizing Investments in an Energy Efficiency Program in Favelas in the City of Rio de Janeiro

This study proposes an integrated qualitative and quantitative assessment of expert opinions aiming at ranking a set of five shantytowns (favelas) located in the city of Rio de Janeiro. These communities are candidates for investments in an energy efficiency program implemented by the local electric utility company. The city, state, and federal governments want to eliminate domination of these areas by organized criminal gangs and present the city as a peaceful metropolis while hosting two big sports events: the soccer World Cup in 2014 and the Olympic Games in 2016. In recent years, some favelas were chosen to be prototypes for an ambitious project to reshape Rio de Janeiro. This involves first sending in special tactics police to drive drug gangs out, then installing the Pacifying Police Unit in the favela. Once security has been established, it is possible to improve general living conditions in these areas, including by providing public services such as health clinics, formal electricity connections and cable television. The core of the energy efficiency program was to convert informal customers to formal ones, because such communities were responsible for approximately 40% of the commercial losses (stolen energy) in the city. The model specification presented in this paper was set up with ten relevant criteria for decision making, identified through an in‐depth interview with the decision maker. The relative importance of the criteria and the performance of each favela regarding each criterion were measured by the Simos method. The preferences resulting from this method were translated into a nine‐point scale. The imprecision of subjective judgment was partially compensated by using a fuzzy analytical hierarchy process. Some criteria were ordinal, such as ‘Fair Relationship with the Community’ and ‘Complexity to Rebuild the Distribution Lines’, whereas other were cardinal, like ‘Percentage of Clients in Default’ and ‘Commercial Loss Due to Energy Theft’. At the end, the model was efficient in ranking the five favelas, therefore contributing to a rational and transparent approach for capital investment in social projects. Copyright © 2014 John Wiley & Sons, Ltd.     

Simplified approaches for multicriteria decision making under uncertainty

The problem of uncertainty in the outcome of decisions has tended to be glossed over by many MCDM methods, with the exception of multiattribute utility theory (MAUT). MAUT does, however, require quite complicated preference elicitations and knowledge of the full multivariate distribution of outcomes. Results from a series of simulation studies indicate that the preference orderings of MAUT are only minimally changed when using a simple additive aggregation of marginal utilities, especially in relation to the natural imprecisions inherent in preference elicitation. It is shown in the simulations that by far the most critical aspect of multicriteria decision analysis under uncertainty is not the form of aggregation, but the correct elicitation of marginal utilities which properly represent decision maker preferences over gambles. We relate the results obtained here to other results on the approximation of distributions by three- or five-point discrete distributions, and suggest that the use of deterministic MCDM methods of any form (not necessarily value function techniques), applied to an extended formulation in which each criterion measure is repeated for three or five ‘scenarios’, can be justified.     

R&D Project Selection: A Decision Process Approach

The fundamental decision problem of an R&D firm is to select projects in which to invest. Most reported models dealing with this subject use sophisticated mathematical models maximizing specific attributes of the projects without dealing with the underlying motivation of the decision maker. This paper summarizes a successful application of a decision-making process involving multicriteria in the selection of R&D projects in the Ecogen Israel Partnership. We emphasize the structure of the decision problem, with the main goal of helping the decision maker to better understand the nature of his problem. We systematically generate the objective hierarchy of the decision maker, including his main criteria, subcriteria attributes and alternatives. The ‘best’ alternative can then be found using most interactive procedures found in the literature. As the decision maker is totally involved in the entire decision-making process he is better able to understand his problem and preferences.     

Note on lexicographic‐order preservation and stochastic dominance

This note concerns two issues left unresolved in our study of lexicographic‐order preservation and stochastic dominance in settings where preferences are represented by utility vectors, ordered lexicographically, and judgements emerge as matrices that premultiply utility vectors in expected utility sums. First, a generalization of the ‘Conjecture Σ’, which implied transitivity of a stochastic dominance relation under non‐vacuous resolution‐level information, is proved. Second, this paper comments on using resolution‐level information in higher as well as in first degree stochastic dominance analysis. Copyright © 1999 John Wiley & Sons, Ltd.     

A Note on the Power of Multiattribute One‐switch Utility Functions

One‐switch utility functions model situations in which the preference between two alternatives switches only once as the outcome of one attribute of both alternatives changes from low to high. Recent research cites evidence that the sum of exponential functions (sumex) is the most convincing type for modelling one‐switch utility functions. Sumex functions allow to model exactly one preferential switch and they are convenient for estimating one‐switch utility functions. However, it is unclear so far if sumex functions are suitable to model preferential switches that are perceivable by a decision maker. This paper first analyses how different the utility of two alternatives before and after a preferential can be modelled with sumex functions given that the preferential switch is caused by a particular attribute outcome improvement. It thereafter investigates how accurately decision makers perceive such utility differences. Copyright © 2015 John Wiley & Sons, Ltd.     

An interactive fuzzy satisficing method for multiobjective stochastic linear programming problems using chance constrained conditions

Two major approaches to deal with randomness or ambiguity involved in mathematical programming problems have been developed. They are stochastic programming approaches and fuzzy programming approaches. In this paper, we focus on multiobjective linear programming problems with random variable coefficients in objective functions and/or constraints. Using chance constrained programming techniques, the stochastic programming problems are transformed into deterministic ones. As a fusion of stochastic approaches and fuzzy ones, after determining the fuzzy goals of the decision maker, interactive fuzzy satisficing methods to derive a satisficing solution for the decision maker by updating the reference membership levels is presented. Copyright © 2003 John Wiley & Sons, Ltd.     

Coherent psychometric modelling with Bayesian nonparametrics

In this paper we argue that model selection, as commonly practised in psychometrics, violates certain principles of coherence. On the other hand, we show that Bayesian nonparametrics provides a coherent basis for model selection, through the use of a ‘nonparametric’ prior distribution that has a large support on the space of sampling distributions. We illustrate model selection under the Bayesian nonparametric approach, through the analysis of real questionnaire data. Also, we present ways to use the Bayesian nonparametric framework to define very flexible psychometric models, through the specification of a nonparametric prior distribution that supports all distribution functions for the inverse link, including the standard logistic distribution functions. The Bayesian nonparametric approach provides a coherent method for model selection that can be applied to any statistical model, including psychometric models. Moreover, under a ‘non‐informative’ choice of nonparametric prior, the Bayesian nonparametric approach is easy to apply, and selects the model that maximizes the log likelihood. Thus, under this choice of prior, the approach can be extended to non‐Bayesian settings where the parameters of the competing models are estimated by likelihood maximization, and it can be used with any psychometric software package that routinely reports the model log likelihood.     

Utility Monsters for the Fission Age

One of the standard approaches to the metaphysics of personal identity has some counter‐intuitive ethical consequences when combined with maximising consequentialism and a plausible (though not uncontroversial) doctrine about aggregation of consequences. This metaphysical doctrine is the so‐called ‘multiple occupancy’ approach to puzzles about fission and fusion. It gives rise to a new version of the ‘utility monster’ problem, particularly difficult problems about infinite utility, and a new version of a Parfit‐style ‘repugnant conclusion’. While the article focuses on maximising consequentialism for simplicity, the problems demonstrated apply more widely to a range of ethical views, especially flavours of consequentialism. This article demonstrates how these problems arise, and discusses a number of options available in the light of these problems for a consequentialist tempted by a multiple occupancy metaphysics.     

Cleaning versus replacement in power plant air preheaters: a comparison between deterministic and stochastic goal programming models

Power plants lose efficiency when the air preheater elements are not replaced or cleaned as they deteriorate. Replacing is more expensive than cleaning but more secure to preserve power and lifetime. How many baskets should be then replaced and how many cleaned to achieve ‘satisficing’ goals of cost, power and lifetime? We design deterministic/stochastic goal programming approaches to this problem and develop a decision case for usual preheaters. Some discrepancies in results between both approaches appear, the deterministic model advising to replace less baskets than the stochastic, which is plausible due to the risk variable. Copyright © 2005 John Wiley & Sons, Ltd.     

Developing an Output‐Oriented Super Slacks‐Based Measure Model with an Application to Third‐Party Reverse Logistics Providers

Outsourcing is an increasingly significant topic pursued via corporations seeking enhanced efficiency. Third‐party reverse logistics involves the employ of external firms to carry out some or all of the firm's logistics activities. Output‐oriented super slacks‐based measure (SBM) model is one of the models in data envelopment analysis (DEA). In many real‐world applications, data are often stochastic. A successful approach to address uncertainty in data is to replace deterministic data via random variables, leading to chance‐constrained DEA. In this paper, a chance‐constrained output‐oriented super SBM model is developed and also its deterministic equivalent, which is a nonlinear program, is derived. Furthermore, it is shown that the deterministic equivalent of the stochastic output‐oriented super SBM model can be converted into a quadratic program. In addition, sensitivity analysis of the stochastic output‐oriented super SBM model is discussed with respect to changes on parameters. Finally, a numerical example demonstrates the application of the proposed model. Copyright © 2011 John Wiley & Sons, Ltd.     

An evolutionary approach to support decision making with linear decision models

In this paper we consider decision problems that can be described as linear decision models. These models have been traditionally solved using linear programming, fuzzy linear programming, multiple-objective linear programming or ‘what-if’ analysis. Using these approaches, one encounters a number of difficulties. We propose an ‘evolutionary approach’ to overcome these difficulties. In the proposed approach the decision maker does not have to precisely specify the model (i.e. the objective functions, the RHS values, etc.) at the beginning of the solution procedure. In fact, the model evolves as the solution procedure proceeds.     

Preference‐based Learning of Ideal Solutions in TOPSIS‐like Decision Models

Combining established modelling techniques from multiple‐criteria decision aiding with recent algorithmic advances in the emerging field of preference learning, we propose a new method that can be seen as an adaptive version of TOPSIS, the technique for order preference by similarity to ideal solution decision model (or at least a simplified variant of this model). On the basis of exemplary preference information in the form of pairwise comparisons between alternatives, our method seeks to induce an ‘ideal solution’ that, in conjunction with a weight factor for each criterion, represents the preferences of the decision maker. To this end, we resort to probabilistic models of discrete choice and make use of maximum likelihood inference. First experimental results on suitable preference data suggest that our approach is not only intuitively appealing and interesting from an interpretation point of view but also competitive to state‐of‐the‐art preference learning methods in terms of prediction accuracy. Copyright © 2014 John Wiley & Sons, Ltd.     

Portfolio Selection from Multiple Benchmarks: A Goal Programming Approach to an Actual Case

This paper deals with benchmark‐based portfolio choice for buy‐and‐hold strategies of investing. Multiple benchmarks for returns are considered, which is more realistic than taking a unique benchmark – a unique aspiration difficult to select in practice among the various aspirations for returns that the investor has in mind. Portfolio selection with multiple benchmarks leads to a multi‐objective problem, which is addressed by mean value – stochastic goal programming. In particular, two benchmarks are considered, which involves two goals. Weights for goals depend on investor's preferences and Arrow's absolute risk aversion coefficients. An efficient frontier of portfolios is obtained. Advantages of this stochastic method in our context are as follows: (i) Mean value‐stochastic goal programming relies on classical utility theory under uncertainty and Arrow's absolute risk aversion, which ensures soundness and strictness, and (ii) the numerical model is easily solved by using available software such as mean–variance software. Numerical results are tabulated and discussed. Copyright © 2011 John Wiley & Sons, Ltd.     

A methodology to assess the utility of future space systems

The Air Force SPACECAST 2020 study identified and prioritized some future space systems and technologies required in the next century. This paper presents the results of research on the SPACECAST 2020 value model. The model determines and prioritizes future space systems’ utility towards controlling and exploiting space. This research identifies the assumptions and simplifications in the additive utility function and assesses modifications. The research shows that mutual utility independence of the mission areas is a reasonable assumption. Mutual utility independence allows the use of the multiplicative and multilinear utility functions. Also, the SPACECAST 2020 scoring functions used the same scoring scale and only measured the utility of future capabilities. This study makes modifications to the SPACECAST 2020 measure-of-merit scoring functions. It replaces most of these functions with either a concave, convex, linear or ‘S’ scoring function, which have expanded capability ranges to include both current and future capabilities. The modified scoring functions and alternative utility functions do not alter the SPACECAST 2020 results but do improve the credibility and future usefulness of the model. This study shows that the initial assumption of using an additive utility function is also valid. © 1997 John Wiley & Sons, Ltd.     

On choosing between deterministic and indeterministic models: underdetermination and indirect evidence

There are results which show that measure-theoretic deterministic models and stochastic models are observationally equivalent. Thus there is a choice between a deterministic and an indeterministic model and the question arises: Which model is preferable relative to evidence? If the evidence equally supports both models, there is underdetermination. This paper first distinguishes between different kinds of choice and clarifies the possible resulting types of underdetermination. Then a new answer is presented: the focus is on the choice between a Newtonian deterministic model supported by indirect evidence from other Newtonian models which invoke similar additional assumptions about the physical systems and a stochastic model that is not supported by indirect evidence. It is argued that the deterministic model is preferable. The argument against underdetermination is then generalised to a broader class of cases. Finally, the paper criticises the extant philosophical answers in relation to the preferable model. Winnie’s (1998) argument for the deterministic model is shown to deliver the correct conclusion relative to observations which are possible in principle and where there are no limits, in principle, on observational accuracy (the type of choice Winnie was concerned with). However, in practice the argument fails. A further point made is that Hoefer’s (2008) argument for the deterministic model is untenable.     

A new fuzzy multicriteria evaluation method for group site selection in GIS

Multiple criteria group site selection problems involve a group of individuals evaluating a set of alternative sites on the basis of multiple criteria. This paper presents an application of a new fuzzy algorithm for finding and exploring potential solutions to these problems in a raster Geographical Information System (GIS) environment. Linguistic assessments from decision‐makers are represented as triangular fuzzy numbers (TFN's), which are adjusted for uncertainty in the source data and their relationship to suitability by using an approach based on type‐2 fuzzy sets. The first aggregation of inputs is a compensatory one based on fuzzy multiattribute decision‐making (MADM) theory. An adjusted aggregation then factors in conflicts, risks and uncertainties to enable a variety of compensatory and non‐compensatory outcomes to be generated based on decision‐maker preferences. The algorithm was implemented in ArcView GIS as part of an ongoing collaborative project with Brisbane Airport. This paper outlines the fuzzy algorithm and its use in site selection for a recycling facility on the Brisbane Airport site. Copyright © 2005 John Wiley & Sons, Ltd.     

The marketing of industrial real estate: application of Taguchi loss functions

The marketing of industrial real estate is a resource‐consuming endeavour for all parties involved consisting of many objectives that, in many cases, may be in conflict with one another. One method of minimizing resource requirements, especially time, while increasing the probability of a successful match is to select properties for presentation that maximizes buyer utility. Zionts (1992) indicated one area for future research in multiple criteria decision‐making (MCDM) is in the development of ‘Eclectic Approaches’ using old ideas in a new way to help develop MCDM approaches. In this paper Taguchi loss functions, a procedure commonly used in quality control, is proposed as a tool that can be used by industrial real estate professionals to more efficiently determine the property that most closely matches the buyer's needs. Copyright © 2001 John Wiley & Sons, Ltd.     

Beyond the qualitative paradigm: A framework for introducing diversity within qualitative psychology

The case for qualitative research in psychology is considered. We argue against the idea that qualitative research is merely a matter of technique or method, and question the utility of viewing it as a unitary paradigm. Rather, the links between epistemology, methodology, and method are explored within three theorized strands of qualitative inquiry, making reference to illustrative projects. Each strand is organized around a different approach to the issues of justifying and warranting psychological knowledge: (1) reliability and validity; (2) generativity and grounding; and (3) discourse and reflexivity. These are exemplified in Miles and Huberman's ‘data display’ model, Glaser and Strauss' method of ‘grounded theory’, and in various forms of ‘discourse’ analysis. Reflections upon points of contact between the three strands address two main issues: (1) rendering research publicly accountable; and (2) challenging relativism.