This paper presents a new algorithm for identifying all supported non-dominated vectors (or outcomes) in the objective space, as well as the corresponding efficient solutions in the decision space, for multiobjective ...
详细信息
This paper presents a new algorithm for identifying all supported non-dominated vectors (or outcomes) in the objective space, as well as the corresponding efficient solutions in the decision space, for multiobjective integer network flow problems. Identifying the set of supported non-dominated vectors is of the utmost importance for obtaining a first approximation of the whole set of non-dominated vectors. This approximation is crucial, for example, in two-phase methods that first compute the supported non-dominated vectors and then the unsupported non-dominated ones. Our approach is based on a negative-cycle algorithm used in single objective minimum cost flow problems, applied to a sequence of parametric problems. The proposed approach uses the connectedness property of the set of supported non-dominated vectors/efficient solutions to find all integer solutions in maximal non-dominated/efficient facets. (C) 2008 Elsevier B.V. All rights reserved.
In this paper, we present a primal-dual interior-point algorithm to solve a class of multi-objectivenetwork flow problems. More precisely, our algorithm is an extension of the single-objective primal infeasible dual ...
详细信息
In this paper, we present a primal-dual interior-point algorithm to solve a class of multi-objectivenetwork flow problems. More precisely, our algorithm is an extension of the single-objective primal infeasible dual feasible inexact interior point method for multi-objective linear network flow problems. Our algorithm is contrasted with standard interior point methods and experimental results on bi-objective instances are reported. The multi-objective instances are converted into single objective problems with the aid of an achievement function, which is particularly adequate for interactive decision-making methods.
暂无评论