An interactive multiobjective procedure for selecting medium-term countermeasures after nuclear accidents

This paper presents a methodology for supporting medium-term decisions in the context of nuclear accidents. An interactive procedure based on a multiobjective linear model is introduced, allowing the set of feasible strategies to be explored. This procedure enables the users to express preferences on both criterion values and the structure of the strategies. Additional flexibility is provided by the possibility of integrating evolutive constraints during the decision process. © 1998 John Wiley & Sons, Ltd.     

Using aspiration levels in an interior primal-dual multiobjective linear programming algorithm

We introduce in this paper a new multiple-objective linear programming (MOLP) algorithm. The algorithm is based on the single-objective path-following primal—dual linear programming algorithm and combines it with aspiration levels and the use of achievement scalarizing functions. The resulting algorithm falls in the class of interactive MOLP algorithms, as it requires interaction with the decision maker (DM) during the iterative process to obtain statements of aspirations for levels of objectives of the MOLP problem. The interior point algorithm is then used to trace a path of interates from a current (interior) solution and approach as closely as desired a non-dominated solution corresponding to the optimum of the achievement scalarizing function. The timing of the interaction with the DM is dependent on the progress of the interior algorithm. It can take place every few, pre-specified, iterations or after the duality gap achieved for the stated aspirations has fallen below a certain threshold. It is expected that an interior algorithm will speed up the overall process of searching and finding the most preferred MOLP solution—especially in large-scale problems—by avoiding the need for numerous pivot operations and their corresponding interactive sessions inherent in simplex-based algorithms.     

An Application of MCDM in Water Resources Problems

In this paper a practical application of MCDM in water resources problems is presented. Based on a real project for Qinhuangdao water resources management sponsored by Qinhuangdao Municipality, we construct a set of models for inflow forecast, reservoir operations, water supply and allocation, and flood routing for system optimal operation and flood management. A stochastic dynamic programming (DP) model with a fuzzy criterion is proposed for monthly reservoir operations. A series of goal programming (GP) models is built for water supply and allocation on different planning and operating levels. The DP–GP models fulfil the optimal operation tasks of a water resources management decision support system (WRMDSS) for Qinhuangdao water resources management.     

The operation of Lake Kariba: A multiobjective decision analysis

The paper describes a successful application of Bayesian decision analysis to the operation of the Lake Kariba hydropower system. This management problem is complicated by the high uncertainty of the inflow process, multiple and conflicting objectives and the influence of time on some of the parameters in the management task. Inflows to the reservoir are forecast through dynamic linear models. Managerial preferences are modelled through a multiattribute utility function. Since the solution of the exact model is computationally too demanding, a heuristic method is applied to find a feasible control strategy. A comparison with results obtained by methods used previously demonstrates the superiority of the methodology presented here.     

An encompassing framework for Paraconsistent Logic Programs

We propose a framework which extends Antitonic Logic Programs [Damásio and Pereira, in: Proc. 6th Int. Conf. on Logic Programming and Nonmonotonic Reasoning, Springer, 2001, p. 748] to an arbitrary complete bilattice of truth-values, where belief and doubt are explicitly represented. Inspired by Ginsberg and Fitting's bilattice approaches, this framework allows a precise definition of important operators found in logic programming, such as explicit and default negation. In particular, it leads to a natural semantical integration of explicit and default negation through the Coherence Principle [Pereira and Alferes, in: European Conference on Artificial Intelligence, 1992, p. 102], according to which explicit negation entails default negation. We then define Coherent Answer Sets, and the Paraconsistent Well-founded Model semantics, generalizing many paraconsistent semantics for logic programs. In particular, Paraconsistent Well-Founded Semantics with eXplicit negation (WFSX_p) [Alferes et al., J. Automated Reas. 14 (1) (1995) 93–147; Damásio, PhD thesis, 1996]. The framework is an extension of Antitonic Logic Programs for most cases, and is general enough to capture Probabilistic Deductive Databases, Possibilistic Logic Programming, Hybrid Probabilistic Logic Programs, and Fuzzy Logic Programming. Thus, we have a powerful mathematical formalism for dealing simultaneously with default, paraconsistency, and uncertainty reasoning. Results are provided about how our semantical framework deals with inconsistent information and with its propagation by the rules of the program.     

A neural implementation of multi-adjoint logic programming

A neural net based implementation of propositional [0,1]-valued multi-adjoint logic programming is presented, which is an extension of earlier work on representing logic programs in neural networks carried out in [A.S. d'Avila Garcez et al., Neural-Symbolic Learning Systems: Foundations and Applications, Springer, 2002; S. Hölldobler et al., Appl. Intelligence 11 (1) (1999) 45–58]. Proofs of preservation of semantics are given, this makes the extension to be well-founded.The implementation needs some preprocessing of the initial program to transform it into a homogeneous program; then, transformation rules carry programs into neural networks, where truth-values of rules relate to output of neurons, truth-values of facts represent input, and network functions are determined by a set of general operators; the net outputs the values of propositional variables under its minimal model.     

Planning Beyond the Next Trial in Adaptive Experiments: A Dynamic Programming Approach

Experimentation is at the heart of scientific inquiry. In the behavioral and neural sciences, where only a limited number of observations can often be made, it is ideal to design an experiment that leads to the rapid accumulation of information about the phenomenon under study. Adaptive experimentation has the potential to accelerate scientific progress by maximizing inferential gain in such research settings. To date, most adaptive experiments have relied on myopic, one‐step‐ahead strategies in which the stimulus on each trial is selected to maximize inference on the next trial only. A lingering question in the field has been how much additional benefit would be gained by optimizing beyond the next trial. A range of technical challenges has prevented this important question from being addressed adequately. This study applies dynamic programming (DP), a technique applicable for such full‐horizon, “global” optimization, to model‐based perceptual threshold estimation, a domain that has been a major beneficiary of adaptive methods. The results provide insight into conditions that will benefit from optimizing beyond the next trial. Implications for the use of adaptive methods in cognitive science are discussed.     

Order-Constrained Solutions in <Emphasis Type="Italic">K</Emphasis>-Means Clustering: Even Better Than Being Globally Optimal

This paper proposes an order-constrained K-means cluster analysis strategy, and implements that strategy through an auxiliary quadratic assignment optimization heuristic that identifies an initial object order. A subsequent dynamic programming recursion is applied to optimally subdivide the object set subject to the order constraint. We show that although the usual K-means sum-of-squared-error criterion is not guaranteed to be minimal, a true underlying cluster structure may be more accurately recovered. Also, substantive interpretability seems generally improved when constrained solutions are considered. We illustrate the procedure with several data sets from the literature.     

Interactive Frameworks for Investigation of Goal Programming Models: Theory and Practice

This paper discusses means of introducing decision maker input into the goal programming model in order to produce more satisfactory solutions. Both formal interactive methods and informal trial-and-error approaches are discussed. The design criteria for the choice of the initial test solution and the stopping criteria for the final solution are detailed. The area of presentation of results to, and elicitation of preferences from, the decision maker is dealt with. Practical suggestions for means of parameter alteration to produce alternative solutions are given. Finally, the integration of the above issues into an integrated framework is discussed. © 1997 John Wiley & Sons, Ltd.     

Approximately Efficient Solutions in Vector Optimization

An approach to approximating solutions in vector optimization is developed for vector optimization problems with arbitrary ordering cones. This paper presents a study of approximately efficient points of a given set with respect to a convex cone in an ordered Banach space. Existence results for such approximately efficient points are obtained. A domination property related to these existence results is observed and then it is proved that each element of a given set is approximated by the sum of a point in a convex cone inducing the ordering and a point in a finite set consisting of such approximately efficient points of the set.