In this paper we address the biobjective problem of locating a semiobnoxious facility, that must provide service to a given set of demand points and, at the same time, has some negative effect on given regions in the ...
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In this paper we address the biobjective problem of locating a semiobnoxious facility, that must provide service to a given set of demand points and, at the same time, has some negative effect on given regions in the plane. In the model considered, the location of the new facility is selected in such a way that it gives answer to these contradicting aims: minimize the service cost (given by a quite general function of the distances to the demand points) and maximize the distance to the nearest affected region, in order to reduce the negative impact. Instead of addressing the problem following the traditional trend in the literature (i.e., by aggregation of the two objectives into a single one), we will focus our attention in the construction of a finite epsilon-dominating set, that is, a finite feasible subset that approximates the Pareto-optimal outcome for the biobjective problem. This approach involves the resolution of univariate d.c. optimization problems, for each of which we show that a d.c. decomposition of its objective can be obtained, allowing us to use standard d.c. optimization techniques.
In this paper, a biobjective mixed-integer nonlinear programming model is developed for a hierarchical three-level health service network design problem, which is then transformed to its linear counterpart. The model ...
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In this paper, a biobjective mixed-integer nonlinear programming model is developed for a hierarchical three-level health service network design problem, which is then transformed to its linear counterpart. The model aims to minimize the total establishment cost and total weighted distance between patient zones and health facilities simultaneously. In order to cope with inherent epistemic uncertainty in input parameters, four variants of a novel hybrid robust possibilistic programming (HRPP) approach are introduced. Finally, a real case study is provided to illustrate the performance and applicability of the proposed HRPP models in practice.
A model For stream water quality management with particular emphasis on applicability in Turkey is developed via mathematical programming. A new criterion of equity is introduc. ed and the model is formulated as a qua...
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A model For stream water quality management with particular emphasis on applicability in Turkey is developed via mathematical programming. A new criterion of equity is introduc. ed and the model is formulated as a quadratic programming problem with two objectives, namely equity among dischargers and minimization of total cost. The model is also applied to a hypothetical stream and compared with other more common water quality management programs. [ABSTRACT FROM AUTHOR]
The problem of optimizing some contiuous function over the efficient set of a multiple objective programming problem can be formulated as a nonconvex global optimization problem with special structure. Based on the co...
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The problem of optimizing some contiuous function over the efficient set of a multiple objective programming problem can be formulated as a nonconvex global optimization problem with special structure. Based on the conical branch and bound algorithm in global optimization, we establish an algorithm for optimizing over efficient sets and discuss about the implementation of this algorithm for some interesting special cases including the case of biobjective programming problems.
Clustering involves partitioning a set of related objects into a set of mutually exclusive and completely exhaustive clusters. The objective is to form clusters which reflect minimum difference among objects as measur...
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Clustering involves partitioning a set of related objects into a set of mutually exclusive and completely exhaustive clusters. The objective is to form clusters which reflect minimum difference among objects as measured by the relevant clustering criterion. Most statements of clustering problems assume that the number of clusters, g, in the partition is known. In reality, a value for g may not be immediately obvious. It is known that as g increases, there is an improvement in the value of the clustering criterion function. However, for some values of g, this rate of improvement may be less than expected. Because there may be a cost factor involved, there is also interest in identifying those values of g that offer attractive rates of improvement. Partitions that are optimal for a given g, and for which the given g offer an attractive rate of improvement, are referred to as being w-efficient;other partitions, even if optimal for a given g, are referred to as being w-inefficient. We present a linear programming approach for generating the w-efficient partitions of the sequential clustering problem, and demonstrate the importance of w-efficient partitions to the efficient solution of the sequential clustering problem.
This paper is concerned with a biobjective routing problem, called the shortest path with shortest detour problem, in which the length of a route is minimized as one criterion and, as second, the maximal length of a d...
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This paper is concerned with a biobjective routing problem, called the shortest path with shortest detour problem, in which the length of a route is minimized as one criterion and, as second, the maximal length of a detour route if the chosen route is blocked is minimized. Furthermore, the relation to robust optimization is pointed out, and we present a new polynomial time algorithm, which computes a minimal complete set of efficient paths for the shortest path with shortest detour problem. Moreover, we show that the number of nondominated points is bounded by the number of arcs in the graph.
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